Characterization of low molecular weight carbohydrates in ...

Characterization of low molecular weight

carbohydrates in dietary foods by chromatographic

techniques coupled to mass spectrometry

by

Roberto Megías Pérez

a Thesis submitted in partial fulfillment

of the requirements for the degree of

Doctor of Philosophy

in Chemistry

Approved Dissertation Committee

Prof. Dr. Nikolai Kuhnert

Professor of Chemistry, Jacobs University Bremen

Prof. Dr. Matthias Ullrich

Professor of Microbiology, Jacobs University Bremen

Prof. Dr. Dirk Carl Albach

Professor of Biodiversity and Evaluation of Plant, Carl-

von Ossietzky Universität Oldenburg

Date of Defense: 20th December 2018

Department of Life Sciences and Chemistry

Statutory Declaration

Family Name, Given/First Name Megias Perez, Roberto

Matriculation number 20331333

What kind of tesis are you submitting: Bachelor-

, Master- or PhD-Thesis

PhD-thesis

English: Declaraction of Authorship

I hereby declare that the thesis submitted was created and written solely by myself without any

external support. Any sources, direct or indirect, are marked as such. I am aware of the fact that

the contents of the thesis in digital form may be revised with regard to usage of unauthorized

aid as well as whether the whole or parts of it may be identified as plagiarism. I do agree my

work to be entered into a database for it to be compared with existing sources, where it will

reimain in order to enable further comparisons with future theses. This does not gran any rights

of reproduction and usage, however.

The thesis has been written independently and has not been submitted at any other university

for the conferral of a PhD degree; neither has the thesis been previously published in full.

German: Erklärung der Autorenschaft (Urheberschaft)

Ich erkläare hiermit, dass die vorliegende Arbeit ohne fremde Hilfe ausschließlich von mir

erstellt und geschrieben worden ist. Jedwede verwendeten Quellen, direkter oder indirekter Art,

sin als solche kenntlich gemacht worden. Mir ist die Tatsache bewusst, das der Inhalt der Thesis

in digitaler Form geprüft werden kann im Hinblick darauf, ob es sich ganz oder in Teilen un

ein Plagiat handelt. Ich bin damit einverstanden, dass meine Arbeit in einer Datenbank

eingegeben werden kann, um mit bereits bestehenden. Quellen verglichen zu werden und dort

auch verbleiblt, um mit zukünftigen Arbeiten verglichen werden zu können. Dies berechtigt

jedoch nich zur Verwendung oder Vervielfältigung.

Diese Arbeit wurde in der vorliegenden Form weder einer anderen Prüfungsbehörde vorgelegt

noch wurde das Gesamtdokument bisher veröffentlicht.

…………………………………………………………………………………………………...

Date, signature

Diligence is the mother of good fortune, and idleness, its opposite, never

brought a man to the goal of any of his best wishes.

Miguel de Cervantes

To my mother for her encouragement, lifelong support and unconditional love.

To the person that recommend me not to make a PhD thesis. Despite

everything and not having considered your opinion, you have always been there

in these years.

ACKNOWLEDGEMENTS

I hereby would like to acknowledge Prof. Dr Nikolai Kuhnert for giving me the

opportunity of this PhD position in a moment of my life that I had given up with the idea of

making a PhD thesis, for guiding this “different” thesis in comparison to his previous

experience and for allowing me to perform my research in my way. Appreciations also go to

Prof. Dr Matthias Ullrich for accepting to be part of this committee and his excellent

management of COMETA project and to Prof. Dr Dirk Carl Albach for joining my dissertation

committee and for reviewing this thesis. The partial financial support from Barry Callebaut in

the earliest stages of my PhD work is also acknowledged.

In this preface of my thesis, I could not forget to mention my gratitude to Dr Javier

Gonzalez and his suggestion to apply for a PhD position in Kuhnert group.

I do not have words to describe the eternal gratitude towards Dr Ana Ruiz, her patient,

help and collaboration in the different parts of this PhD thesis. Also, the same is applied to Dr

Gorka Ruiz de Garibay for his uncountable help.

I should not forget to acknowledge the different members of Cometa project that have

spent their scarcely free time in collaborating in the different chapters of this thesis or discussing

cocoa science. A particular remark is towards Britta Behrends, Mauricio Moreno and Dr Roy

N. D’Souza.

Besides, I will remark my gratitude towards the current Kuhnert´s lab members

(remarkably my gratitude to Sabur Badmos and Fariba Sabzi) and the former members Dr

Abhinandan Sherestha, Dr Maria Patras, Dr Rohan Shah, Dr Inamullah Shah and Dr Seung-

Hun Lee. I should not forget my acknowledgement to Yeweynwuha Gellaw Zemedie and her

help with the green tea study. I should express my gratitude towards Anja Müller and her

uncountable patience and capacity to teach how to solve problems with the different mass

spectrometers. Moreover, last but not least, for the second time, thanks to Britta Behrends. I do

not hesitate to affirm that the short coffee breaks in Friseur and our lunch conversations are and

will be linked to the best moment of this complex PhD.

Outside the science in Jacobs, I have to recognize Thilo Ziegenhagen, Dr Marvin

Madrigal, Ana Gaby Victorino and the rest of the friends I met in Block A of Nordmetall

Collegue for the unforgettable moments lived in Bremen.

Besides, from a personal point of view, I must remark in this thesis a special

acknowledgement to the members of the “Group of chemistry and functionality of

carbohydrates and derivatives” from CIAL in the period 2010-2012 (Dr Ana Ruiz, Ana Belen

Garcia-Bermejo, Paula Copovi, Dr Marta Corzo, Dr Juliana Gamboa, and my bosses Prof. Dr.

Mar Villamiel and Dr Antonia Montilla). Thanks to that job, I was introduced in the exciting

scientific field of carbohydrates analysis applied to food science.

From my personal point of view, I would like to thank my friends Rosa Muñoz and

Enrique F. Patiño (comrades in chemistry studies with whom the exile in Ciudad Real was much

more colorful), Almudena Zamorano, Diana Velázquez and Dr. Margot Roig (comrades in

biochemistry studies from whom I learned everything), Luis Quo (unforgettable moments with

your motorbike) and Dr. Gorka Ruiz de Garibay (until now, the experience in Madrid with you

could be considered once-in-a-lifetime), as well as the rest of my friends not expressly

mentioned.

This preface ends with a special acknowledgement to my brother Enrique Megias and

my mother Maria Jesus Perez.

TABLE OF CONTENTS

1

Table of Contents

LIST OF ABBREVIATIONS .......................................................................................................................... 3

ABSTRACT ................................................................................................................................................ 5

INTRODUCTION ....................................................................................................................................... 7

Chapter 1. Overview of carbohydrates ................................................................................................ 9

1. 1. Definition and classification of carbohydrates ........................................................................ 9

1.2. Physicochemical properties of carbohydrates ........................................................................ 13

1.3. LMWC: from plant biology to bioactive properties, and their applications ........................... 18

Chapter 2. Analytical techniques employed in the analysis of carbohydrates. ................................. 31

2.1. Determination of total carbohydrates by colorimetric analysis-sum parameters ................... 31

2.2. Gas chromatography applied to the analysis of carbohydrates. ............................................. 31

2.3. LC operation modes for the analysis of carbohydrates .......................................................... 33

Chapter 3. Dietary food ..................................................................................................................... 41

3.1. Cocoa ...................................................................................................................................... 41

3.2. Green tea. ................................................................................................................................ 45

3.3. Kale ........................................................................................................................................ 48

AIM OF THE STUDY ................................................................................................................................ 63

RESULTS ................................................................................................................................................. 67

Part-1 LMWC in cocoa beans (chapter 4-6) ...................................................................................... 69

Chapter 4. Profiling, quantification and classification of cocoa beans based on chemometric analysis

of carbohydrates using hydrophilic interaction liquid chromatography coupled to mass

spectrometry. ..................................................................................................................................... 71

TABLE OF CONTENTS

2

Chapter 5. Analysis of minor low molecular weight carbohydrates in cocoa beans by

chromatographic techniques coupled to mass spectrometry ........................................................... 103

Chapter 6. Monitoring the changes of low molecular weight carbohydrates in cocoa beans during

spontaneous fermentation: a chemometric and kinetic approach .................................................... 131

Part 2 – LMWC in commercial green tea and kale (chapter 7 and 8) ............................................. 153

Chapter 7. Characterization of commercial green tea leaves by the analysis of low molecular weight

carbohydrates and other quality indicators. ..................................................................................... 155

Chapter 8. Changes in low molecular weight carbohydrates in kale during development and

acclimation to cold temperatures determined by chromatographic techniques coupled to mass

spectrometry .................................................................................................................................... 185

GENERAL CONCLUSIONS ..................................................................................................................... 211

SUPPLEMENTARY INFORMATION ....................................................................................................... 217

Supplementary information of Chapter 4 ........................................................................................ 219




3

LIST OF ABBREVIATIONS

GC Gas chromatography

MS Mass spectrometry

NMR Nuclear Magnetic Resonance

LMWC Low molecular weight carbohydrates

PCA Principal component analysis

ANOVA One-way analysis of variance

LOQ Limit of quantification

LOD Limit of detection

ESI Electrospray source ionization

TOF Time of flight

CGT Commercial green tea

SPE Solid Phase Extraction

LC Liquid chromatography

TLC Thin-Layer Chromatography

NPLC Normal Phase Liquid Chromatography

HILIC Hydrophylic interaction Liquid Chromatography

RPLC Reverse Phase Liquid Chromatography

ESI Electrospray source ionization

HPAEC High-performance anion-exchange chromatography

RDI Recommended daily uptake

RFO Raffinose familiy oligosaccharides

OF Other Fermentation

5

ABSTRACT

The present thesis reports the development and use of chromatographic techniques coupled to

mass spectrometry for the characterization of low molecular weight carbohydrates (LMWC) in

dietary foods of economic relevance or identified as “functional food”.

The interest for the analysis of LMWC in different dietary foods is based on the involvement

of these compounds in biological reactions in plants and the bioactive properties reported for

some LMWC. A general overview of the carbohydrates, the analytical methodologies to

perform their analysis and a brief description of the different dietary foods selected for this

study (cocoa beans, commercial green tea and kale) are reported in the different chapters

(chapter 1-3) of the introduction.

The study of LMWC in cocoa beans includes three chapters (chapter 4-6). The content of

chapters 4 and 5 is a comprehensive characterization of the LMWC profile in cocoa beans using

HILIC-ESI-TOF MS, HILIC-ESI-MSn and GC-MS, the quantification of the main and minor

LMWC from different origins and a proposal of different indicators of fermentation. Chapter 6

covers a detailed chemometric and kinetic approach to monitor the LMWC changes during the

spontaneous fermentation of cocoa beans. Different reaction mechanisms of the degradation

and formation of LMWC during cocoa fermentation have been discussed.

The study of commercial green tea (chapter 7) evaluates the LMWC together with other

physical and chemical established quality indicators (soluble solids, color and antioxidant

capacity) to characterize CGT. This approach has resulted to be useful for the characterization

of the samples according to the type of processing employed during the manufacturing.

Chapter 8 reports the identification for the first time of different LMWC in kale. This chapter

also includes the analysis of the LMWC content in three types of kale during the development

of the plant, as well as the monitoring of the changes produced as a consequence of cold

temperatures during farming.

INTRODUCTION

INTRODUCTION

9

Chapter 1. Overview of carbohydrates

1. 1. Definition and classification of carbohydrates

Carbohydrates are one of the most abundant constituents present in all living organisms. This

group of compounds, described first by Emil Fisher, has different essential roles as energy

source, structural function and forms the basis of cell-cell recognition. This group of

metabolites, produced by plants via photosynthesis, are considered as primary nutrients

involved in plant nutrition and metabolic processes. Carbohydrates are naturally present in food,

but they may also be added for the improvement of sensorial, functional and technological

properties.

Carbohydrates are a group of biomolecules consisting of carbon (C), hydrogen (H) and oxygen

(O) atoms, usually with a ratio hydrogen-oxygen of 2:1 (as H2O). Based on the empirical

formula Cn(H2O)n, these compounds were denominated carbohydrates because, in composition,

they are apparently hydrates of carbon. Carbohydrates are classified as monosaccharides,

oligosaccharides and polysaccharides according to their degree of polymerization. Recently,

the use of the acronym low molecular weight carbohydrates (LMWC) is increasing to include

monosaccharides, disaccharides and oligosaccharides and other class of compounds such as

inositols or alditols.

Monosaccharides are the simplest form of carbohydrates. Structurally, monosaccharides are

formed by a linear carbon skeleton with a length between three and eight carbon atoms. The

structure of a monosaccharide contains a carbonyl functional group, while each of the remaining

carbon atoms are bonded to one hydroxyl group. Monosaccharides are classified as aldoses or

ketoses in function of the position of the carbonyl group. In aldoses, the carbonyl group is

located in terminal positions, while in ketoses the carbonyl group is commonly located in the

position 2 of the carbon chain. The disposition of atoms in the structure of carbohydrates results

CHAPTER 1

10

in multiple stereogenic centres. A stereogenic centre is defined as one carbon atom carrying

four different substituents, without any element of symmetry present.

The presence of a stereogenic centre in a molecule results in the existence of several versions

of this molecule, called stereoisomers. Stereoisomers are isomeric molecules with the same

molecular formula and similar sequence of bonded atoms (constitution), differing in the

orientations of the atoms in space. The number of stereoisomers is calculated as 2n, with n

representing the number of stereogenic centres. Two stereoisomers are enantiomers when they

are mirror images of each other. Stereoisomers that are not mirror images are called

diastereoisomers.

In solution, carbohydrates form several chemical species in equilibrium, being the most

common species the cyclic conformations. The cyclization reaction can take places between

any hydroxyl group and the carbonyl group from the aldehyde or ketone group. The more stable

cyclic conformations in carbohydrates are furanoses, characterized by a ring structure of five

carbon atoms, and pyranoses, characterized by a ring structure of six carbon atoms. After the

cyclization, the carbonyl group (with the anomeric carbon) allows two spatial orientations of

the hydroxyl group bound to the anomeric carbon. These two orientations allow the

classification of the carbohydrates into two anomers: alpha and beta. When the anomeric carbon

is not taking part in any further chemical bond, it provides to the carbohydrates reducing

properties.

Oligosaccharides are defined by the number of their constituent monosaccharides (between 2

and 10), type of O-glycosidic linkage (example 14, 16), the composition of the monomeric

units and the stereochemistry of the anomeric centre. The linkage between monosaccharides is

established between the anomeric carbon of the first monosaccharide and the hydroxyl group

bound to a carbon (anomeric or not) of the second monosaccharide, with the subsequent loss of

INTRODUCTION

11

a water molecule. In a few cases, a linkage can be established between two anomeric carbons,

for example, the case of sucrose and trehalose.

Polysaccharides are defined as polymers constituted by linear and ramified chains of

monosaccharides. Inulin, cellulose, starch, chitin and glycogen are the more common

polysaccharides.

The generic denomination of “carbohydrate”, apart from the compounds above mentioned, also

includes their derivatives such as alditols, cyclitols, uronic acids and iminosugars. Alditols are

derived from the reduction of the carbonyl group of a monosaccharide. Alditols are named using

the prefix from the carbohydrate from which they are derived and the suffix –itol.

Cyclitols are cyclic polyalcohols in which at least three of the carbon atoms have a hydroxyl

group. This category includes the inositols, whose structure is a ring of six carbon atoms with

a hydroxyl group on each of them. There are nine isomers, which differ from each other by the

axial or equatorial disposition of their hydroxyl groups. The nomenclature for these compounds

employs the prefixes: cis-, epi-, allo-, neo-, myo-, muco-, D-chiro-, L-chiro- and scyllo-inositol.

Uronic acids are formed through the oxidation of a primary alcohol from a carbohydrate to a

carboxylic acid. Uronic acids are named using the prefix from the carbohydrate from which

they are derived and the suffix –uronic.

Iminosugars, also called azasugars or polyhydroxy alkaloids, derive from the substitution of the

endocyclic oxygen atom by a nitrogen atom. The structure is based on rings of five and six

carbon atoms, can be mono- or multicyclic and must include a minimum of two hydroxyl

groups. According to their chemical structure, iminosugars are classified as piperidines,

pyrrolidines, indolizidines, pyrrolidines and nortropanes. Selected structures of the different

LMWC that are the focus of this PhD thesis are shown in Figure 1.

CHAPTER 1

12

Figure 1 The structures of the different LMWC that are the focus of this PhD thesis. 1) Fructose, 2) Glucose, 3)

Galactose, 4) Mannitol, 5) Sorbitol, 6) myo-inositol, 7) scyllo-inositol, 8) Galactinol, 9) Sucrose, 10) Maltose, 11)

Melibiose, 12) Raffinose, 13) 1-kestose, 14) Stachyose.

INTRODUCTION

13

1.2. Physicochemical properties of carbohydrates

1.2.1. Energy content

One of the most important properties of the carbohydrates is their role as a source of energy.

The energy provided by the different type of carbohydrates is estimated as 4 kcal/g for

monosaccharides and disaccharides, as 1.6 kcal/g for alditols and as 2.4 kcal/g for cyclitols [1].

1.2.2. Solubility

The monosaccharides and oligosaccharides are generally soluble in aqueous solvents. Solvents

that allow hydrogen bonding interactions, such as formamide, dimethylformamide, dimethyl

sulfoxide and pyridine are most commonly used to solubilize unmodified carbohydrates.

Some polysaccharides such as pectins, are soluble in aqueous solvents while others like

cellulose and starch are often insoluble in aqueous solvents. The difference in solubility is

attributed to the three-dimensional structure of the polysaccharide [2]. Lineal polysaccharides

with a regular conformation can form cristaline structures and therefore tend to be insoluble in

aqueous solvents. Solubility increases in polysaccharides with a higher degree of branching,

since cristalization is prevented due to steric effects.

1.2.3. Hygroscopicity

The hydroxyl groups present in the carbohydrate structure can establish hydrogen bonds with

water, providing hygroscopic properties.

Carbohydrates often contain substantial amounts of water even after drying processes. For

example, alginic acid, a polysaccharide found in seaweed, acts as an anti-desiccant maintaining

the viability of seaweed washed ashore on hot beaches [3].

1.2.4. Crystallinity

Carbohydrates show a wide range of crystallinity. Sucrose and cellulose are the most

representive crystalline carbohydrates. Cellulose is probably the most widely distributed

organic molecule found in nature and is a mixture of crystalline an amorphous regions.

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14

Common sugar is produced in significant quantities as pure sucrose crystals. Apart from these

examples, most carbohydrates are not easily crystallized and are often isolated or synthesized

as amorphous solids or syrups [2].

1.2.5. Viscosity and Surface Activity

Polysaccharides are among the most viscous natural products, many of them are used in the

food industry as gelling agents, thickeners and high viscosity agents.

The length or molecular weight of a polysaccharide has a positive correlation with its viscosity.

The measurement of the molecular weight based on viscosity (MV) serves as a useful means

for polysaccharide characterization.

Polysaccharides lower the surface tension of aqueous solutions and their affinity for the oil-

water interface gives them great significance as emulsifiers [2].

1.2.6. Sweetness

The main characteristic of carbohydrates is the sweet taste. The sensation of sweet taste is a

consequence of the binding capacity of many carbohydrates, with different affinity, to the

human receptors TAS1R1, TAS1R2 and TAS1R3 [4]. These receptors are expressed in the

tongue and the soft palate [5]. Different studies in cell-based assays and knockout mice

demonstrated that the subunits T1R2 and T1R3 heteromerise to constitute the sweet taste

receptors, whereas the T1R1 and T1R3 subunits form a heterodimeric receptor for umami taste

[4]. The sweet taste receptors belong to the family of G protein-coupled receptors (GPCRs).

The binding of mono-, di-, oligosaccharides and their corresponding alditols produces the

activation of this receptor. The strength of the activation of the sweet taste receptor by a

sweetener correlates with their relative sweetness intensity [4], which is determined by dilution

assays using human sensory panels. A scheme of the human receptor is shown in Figure 2.

The intensity, quality and temporal profile (defined as the changes in intensity over time) of the

sweetness vary with each specific carbohydrate [6]. The intensity of the sweet flavour decreases

INTRODUCTION

15

as the length of the chain increases. The influence of the stereochemistry (α- or β-) on the

sweetness of carbohydrates has previously been reported. For instance, isomaltose (6-α-D-

glucopyranosyl-D-glucose) is sweet, but its anomer, gentiobiose (6-β-D-glucopyranosyl- D-

glucose) is bitter [7].

Sucrose is commonly used as a sweetener, noteworthy for its pleasant flavour, even at high

concentrations. Even, some authors have used this carbohydrate as a reference in a relative scale

of sweetness [8].

Figure 2 Structure of the human sweetness receptor

1.2.7. Color and flavour precursors

Carbohydrates are precursors of color and flavor compounds via caramelization and Maillard

reactions. Both reactions take place simultaneously at an elevated temperature.

1.2.7.1. Caramelization

The caramelization reaction occurs when temperatures above 110°C are applied to

carbohydrates. In this reaction, any carbohydrate can be a substrate, without the need of other

reactants.

During the reaction, different fragmentation reactions occur, producing volatile compounds

responsible for flavor. Also, different reactions of condensation or polymerization occur

CHAPTER 1

16

yielding large molecules responsible for color and texture [9, 10]. The products show dark

brown color, characteristic aromas and flavors. However, if the reaction is extended in time,

this can result in undesirable sensorial attributes due to the production of soluble and very dark

compounds [6, 11].

1.2.7.2. Maillard reaction

Louis Camille Maillard in 1912 described the formation of pigments as a result of the reaction

between glucose and glycine. This author named the pigments formed in this reaction as

melanoidins.

Maillard reaction start with the condensation of amino groups from protein, peptides and amino

acids with carbonyl groups on reducing carbohydrates. The first product is a Schiff base, which

results in an Amadori or Heyns rearrangement yielding Amadori or Heyns products. The

reactions will continue with the fragmentation of Amadori and Heyns compounds to reactive

α-dicarbonyl species. These compounds react with nucleophiles such as other amines,

guanidines, and thiols. The products react through Strecker degradation by condensation with

free amino acids, forming imines. Imines will fragment to form Strecker aldehydes, compounds

related to the organoleptic properties [12-14]. In food science, these reactions are known to

describe the non-enzymatic browning reactions of food as a consequence of the application of

a heat source. A simplified scheme of the different reactions involved in Maillard reaction

adapted from the review published by Lund et al. [15] is shown in Figure 3.

INTRODUCTION

17

Figure 3 Simplified scheme of the different reactions involved in Maillard reaction

CHAPTER 1

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1.3. LMWC: from plant biology to bioactive properties, and their applications

1.3.1. LMWC in plant biology

1.3.1.1. Monosaccharides and disaccharides

Photosynthesis is the process by which green plants use the electromagnetic energy from

sunlight to catalyze the “de novo” biosynthesis of carbohydrates. Briefly, photosynthesis occurs

in an organelle called chloroplast that contains all the enzymes and structures needed to perform

the biosynthetic process. Photosynthesis is divided into two phases, the light dependent and the

light independent, also called the Calvin cycle.

In the light-dependent phase, the energy from sunlight is absorbed by chlorophylls and

converted into electron carrier molecules, nicotinamide adenine dinucleotide phosphate in

reduced form (NADPH) and energy carrier molecules, adenosine triphosphate (ATP). A

specialized membrane within the chloroplast, called thylakoid, contains multi-protein

complexes, called photosystems, which are responsible for this process.

The Calvin cycle starts with the fixation of CO2 into 3-phosphoglycerate, reaction catalyzed by

ribulose-1,5-bisphosphate carboxylase/oxidase (RuBisCo). The next step involves the

reduction of 3-phosphoglycerate into glyceraldehyde-3-phosphate. The net result of the Calvin

cycle is the conversion of three molecules of CO2 and a molecule of phosphate into a molecule

of glyceraldehyde-3-phosphate, using six NADPH and nine ATP molecules, which had been

obtained at the light-dependent phase. Glyceraldehyde-3-phosphate is a metabolite involved in

the biosynthesis of glucose, among other carbohydrates.

A recent review from Sami et al. [16] describes the role of carbohydrates in plant metabolism.

According to that review, the accumulation of elevated concentration of carbohydrates in plant

tissues is correlated with the inhibition of photosynthesis, inducing senescence, leading to

stunted growth and necrotic leaves. On the other hand, low carbohydrate accumulation

enhances photosynthesis and reserves mobilization. Also, carbohydrates are implicated in

INTRODUCTION

19

processes as seed germination (glucose alone or in combination with other carbohydrates delays

the germination process) or flowering (sucrose concentration plays an important role in

flowering induction).

1.3.1.2. Mannitol

Mannitol is the most abundant alditol in plants. The synthetic pathway for this molecule in

plants involves three enzyme-catalyzed reactions. The reactions consist of isomerization of D-

fructose-6-phosphate to O-mannose-6-phosphate, followed by the reduction to D-mannitol-1-

phosphate and, finally a dephosphorylation process to yield D-mannitol.

The metabolism of this carbohydrate may play key roles in biotic and abiotic plant responses.

Mannitol is proposed to have a function in protecting cells and cellular structures against cell

damage induced by reactive oxygen species. Plant species with mannitol metabolism are best

adapted by greater tolerance to saline and osmotic stress as a result of mannitol's function as

"compatible solute" [17].

1.3.1.3. myo-Inositol

The biosynthesis of this cyclitol consists of a two-step biochemical pathway highly conserved

in all living organisms. D-myo-inositol-3-phosphate synthase converts D-glucose-6-phosphate

into myo-inositol-3-phosphate. Then, inositol monophosphate phosphatase enzyme acts by

dephosphorylating myo-inositol-3-phosphate to form free myo-inositol.

myo-Inositol is a source of many important molecules, including phosphatidylinositol and its

derivatives, inositol polyphosphates, galactinol (a precursor of raffinose-family

oligosaccharides (RFOs)), pinitol and several cell wall polysaccharides. Molecules derived

from myo-inositol are involved in many cellular functions such as signal transduction,

membrane trafficking, mRNA export, stress tolerance, phosphorus storage and synthesis of

various cell wall components. Thus, myo-inositol could be considered as a critical regulator of

cell metabolism in plant [18].

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1.3.1.4. Raffinose family of oligosaccharides

The reaction between myo-inositol and UDP-galactose, catalysed by the enzyme galactinol

synthase, produces galactinol. This carbohydrate acts as the donor of galactosyl groups in the

biosynthesis of the different carbohydrates from the raffinose family of oligosaccharides (RFO)

[19].

Different galactosyltransferases are involved in the formation of the α-(1→6) glycosidic linkage

between galactose and sucrose to yield raffinose and consecutively to produce stachyose and

verbascose [19].

RFOs have multiple functions in plant metabolism, such as protection against the desiccation

in seeds [20], transport of carbohydrates in phloem sap and energy storage [19]. They also act

as signalling molecules following pathogen attack and are accumulated in vegetative tissues

under abiotic stresses, including freezing. Different plants accumulate different types of RFOs

and in different parts of the plant [21].

The physiological role of RFOs in response to stress is not clear but in vitro studies suggest a

mechanism where the insertion of the RFO among the bilipid layer stabilises the cellular

membranes [22].

1.3.2. Bioactivity of LMWC

1.3.2.1. Prebiotic effect of LMWC

The vast number of microbes present in the human body, referred to as human microbiota, play

an essential role in human health and disease. The human microbiota has even been considered

to be an “essential organ” [23]. The number of microbial cells exceeds at least by two orders of

magnitude the total number of human body cells [24].

In humans, the gut microbiota has the most significant number of bacteria and the highest

number of species compared to other areas of the body [25]. The gut microbiota is involved in

fundamental human biological processes, including regulation of metabolism, regulation of

INTRODUCTION

21

epithelial development and regulation of immunity. Different chronic diseases such as obesity,

inflammatory bowel disease, diabetes mellitus, metabolic syndrome, atherosclerosis, alcoholic

liver disease, non-alcoholic fatty liver disease, cirrhosis, neurodegenerative diseases and

hepatocellular carcinoma have been associated with changes in the human microbiota [26-31].

Different strategies aiming to restore the normal gut microbiota have been extensively studied

in human and animal models, as these methods represent a valuable tool to treat the associated

diseases. Some interventions have been proposed and applied to treat and prevent diseases

including administration of probiotics, prebiotics, synbiotics and faecal microbiota

transplantation [32, 33].

The probiotic concept is defined as “a viable mono- or mixed culture of microorganisms that

applied to animals or human, beneficially affects the host by improving the properties of the

indigenous microflora” [34].

The prebiotic concept is characterized as a “non-digestible food ingredient that beneficially

affects the host by selectively stimulating the growth and activity of one or a limited number of

bacteria already resident in the colon” [35]. Among them, the most extensively documented to

have health benefits in humans are the fructooligosaccharides (FOS), inulin and

galactooligosaccharides (GOS) [36].

The synbiotics concept is described by the appropriate combination of both components

(probiotic and prebiotic) in a single product to ensure a superior effect, compared to the activity

of the probiotic or prebiotic alone. This type of product was created in order to overcome some

possible difficulties in the survival of probiotics in the gastrointestinal tract [37]. In summary,

while probiotics are living microorganisms, prebiotics are non-viable substrates that serve as

nutrients for beneficial microorganisms already harboured by the host.

The prebiotic effect of these compounds is explained by their structure. The glycosidic bonds

in FOS and GOS are degraded by β-fructanosidase and β-galactosidase enzymes preferentially.

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This enzymes are absent in the upper digestive tract and are not expressed in human tissues.

Thus, the food oligosaccharides are not degraded in the digestive tract, reaching the colon with

an intact conformation. In the colon, FOS and GOS act as a substrate for the selective growth

of beneficial bacteria from the genus Bifidobacterium [38]. The specificity for bifidobacteria is

explained by the simultaneous expression of the necessary catabolic enzymes and appropriate

transport machinery by the bacteria of this genus. The principal metabolites from FOS and GOS

fermentation in the colon by bacteria are short-chain fatty acids, acetate (two carbon, C2),

propionate (C3)) and n‑butyrate (C4). These metabolites have a relevant role in the regulation

of intestinal health [39]. It is also known that the health-promoting effects can also occur in

sites distant to the intestinal tract. The effects associated with these short-chain fatty acids

include regulation of colonocyte function, gut homeostasis, energy gain, improvement of the

immune system, decrease of lipids in blood, promotion of appetite and regulation of renal

physiology [40].

One of the first oligosaccharides to be described as prebiotics are human milk oligosaccharides

(HMOs). HMOs are relevant for the development of the intestinal microbiota and

immunological system in infants [41]. Over 200 structurally different oligosaccharides have

been identified in human milk [42].

Other compounds considered prebiotics are the fibers [43]. The physicochemical properties of

the fibers affect the therapeutic effect after consumption. Soluble fibers, such as pectins are

selectively utilized by the host microbiota and promote health. Conversely, other insoluble

fibers such as cellulose, are generally poorly fermented, but the intake in the diet has a beneficial

effect, different from prebiotic, by promoting the gut transit [44].

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23

1.3.2.2. Overview of bioactive effects of individual LMWC

Raffinose

The prebiotic effect of raffinose in humans has been evaluated previously. In humans, four-

week-long diet supplementation with raffinose (15 g/day) produced a significant increase in

intestinal bifidobacteria [45].

Apart from a prebiotic effect, another effect proposed for raffinose is an inhibitory effect on

bacterial adhesion, detected using in vitro models of colonic epithelial cells (both for normal

microbiota [46] and for pathogenic enterotoxic E.coli [47]).

Another effect reported for this compound is the modulation of epidermal differentiation

through activation of liver X receptor (LXR) by the induction of gene expression of involucrin,

filaggrin, and AQP3 [48]. Therefore, raffinose may provide a new class of therapeutic agent for

the treatment of cutaneous disorders associated with abnormal epidermal barrier function. Thus,

raffinose could be used as an ingredient in functional cosmetics.

Stachyose

The prebiotic effect of stachyose has been reported through the evaluation of Deshipu stachyose

granules (DSG) in mice. This dietary supplement, approved by the China Food and Drug

Administration, is derived from the dietary roots of Lycopus lucidus. DSG consists mainly of

stachyose (55.3%), raffinose (25.8%), verbascose (9.7%) and sucrose (6.9%). The evaluation

consisted of a regular consumption of DSG by mice. As a result, the intestinal microbiota

composition varied, with an increase of bifidobacteria and lactobacilli and a decrease in enteric

bacilli. The regulation observed was associated with substantial effects on intestinal peristalsis

promotion and bowel function improvement [49].

A follow-up study in humans showed similar results. DSG at a dosage of 5 g/ day is associated

with a significant increase in bacteria type Bifidobacterium and Lactobacillus and a remarkable

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24

decrease in Clostridium perfringens, with an improvement of the bowel function of patients

suffering from constipation [50].

However, stachyose bioactivity may not only be related to its prebiotic effect. Other positive

effects such as an inhibitory effect over the growing of a colorectal cancer cell line (Caco-2)

has been determined in vitro. The mechanism elucidated seems to involve Caspase-associated

apoptosis initiated by the mitochondria-induced pathway [51].

Consistently, using virtual screening and 3D-Quantitative Structure-Activity Relationship

(QSAR) methods for compounds isolated from traditional Chinese medicines, Hsiao et al. [52],

identified stachyose (together with mannotriose and raffinose) as a potential compound with

high potential to inhibit enzymes involved in the “de novo” synthesis of nucleotides. The target

enzymes of this study were dihydrofolate reductase (DHFR), enyme involved in the synthesis

of tetrahydrofolate, and thymidylate synthase (TS), which is involved in purine synthesis. These

enzymes are targets in the treatment of cancer with chemotherapies. The inhibition of these

enzymes produces numerous side effects as a consequence of the narrow therapeutic range of

the chemotherapies. The authors suggested that a combination of stachyose with chemotherapy

may have synergistic effects, achieving similar effects with lower doses of chemotherapy and

,therefore, reducing side effects [52].

Melibiose

Melibiose promotes different bioactive properties such as calcium absorption in the intestines

[53] and the improvement of the symptoms of allergic disease. In the case of allergic disease,

one study performed in mice suggested the possibility that melibiose would be useful for

preventing or improving the allergic symptoms by suppression of the Th2 immune response

[54].

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25

1-Kestose

The prebiotic effect of this trisaccharide has been evaluated in mice. A diet supplemented with

0.5 to 5% of 1-kestose demonstrated strong induction of bacteria growth from the genus

Bifidobacterium. Consequently, different metabolites as butyrate were increased, producing a

decrease in insuline in serum [55].

Additional application have been evaluated, for example as treatment for skin diseases. The

daily administration in infants with atopic dermatitis of this carbohydrate during 12 weeks

produced a significant improvement of the atopic dermatitis symptoms [56].

myo-Inositol

This compound is a precursor of the biogenesis of phosphatidylinositol. This phospholipid is a

component of cellular membranes and forms several molecules that act as second messengers,

such as inositol 1,4,5-triphosphate (a regulator of intracellular calcium levels) and

phosphatidylinositol-3,4,5-triphosphate (activator of downstream signalling components).

Another function of phosphatidylinositol is the activation of intracelular pathways that lead to

the activation of serotonin receptors. The activation of serotonin receptors could explain the

therapeutic effects of myo-inositol in the treatment of depressions and obsessive-compulsive

disorder [57]. myo-Inositol has also proved to be useful in the treatment of Bulimia nervosa

[58].

On the other hand, alterations in myo-inositol metabolism have been associated with the

pathogenesis associated with Diabetes mellitus and chronic renal failure [59].

Other beneficial functions of myo-inositol related to human health have been reported. This

carbohydrate has shown its influence in different pathways at ovarian tissues. myo-Inositol is

an essential constituent of the follicular microenvironment, where it plays a crucial role in

oocyte maturation [60]. A capability of myo-inositol in restoring ovarian activity in most

patients with Polycystic ovary syndrome (PCOS) [61] has been reported. PCOS pathology is

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26

one of the most common causes of infertility, affecting 5 – 10% of females in reproductive age

[62].

This carbohydrate has been tested as a supplement in the elaboration of infant food formula and

clinical products for feeding premature infants, resulting in beneficial effects such as reducing

intraventricular haemorrhage, protection from retinopathy of prematurity and reducing neonatal

and infant deaths [63].

scyllo-Inositol

The effectiveness of this carbohydrate together with other inositols (chiro-, allo-, cis-, epi-,

muco-, neo-inositol) to treat dyslipidemia, hypercholesterolemia and cardiovascular diseases

has been evaluated and currently is under patent [64].

In vitro studies have revealed the capability of scyllo-inositol in reducing the aberrant

accumulation of amyloid-β protein (Aβ) in Alzheimer’s disease [65]. Initial clinical trials

evaluating scyllo-inositol as a therapeutical agent in Alzheimer disease (phase II clinical trial)

did not show any significant effect. However, the evaluation of this carbohydrate as a

therapeutical agent is still under study [66].

scyllo-Inositol has been found to have similar effects in reducing aberrant accumulation of other

proteins such as α–synuclein in Parkinson’s disease [67] and huntingtin in Huntington disease

[68].

Mannitol

The most significant benefit of this compound is its low glycaemic index and safety for healthy

teeth [69]. This carbohydrate is a low digestible compound. As a consequence, it reaches the

lower intestinal tract causing undesired effects as a consequence of an osmotic imbalance,

leading to osmotic diarrhoea and also fermentation by bacteria. Mannitol is one of the least

well-tolerated polyols, with a laxation threshold of only 20 g /day [70].

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The diuretic effect is another interesting property of this carbohydrate. Mannitol is filtered at

the glomerulus but not reabsorbed by the renal tubule. It exerts osmotic activity within the

proximal convoluted tubule and the descending limb of the loop of Henle, which limits passive

tubular reabsorption of water. Water loss produced by mannitol is accompanied by a variable

natriuresis [71].

1.3.3. Presence of LMWC in dietary food

As previously mentioned, carbohydrates are major components of dietary foods and have a

decisive importance in the diet because they belong to the group of essential nutrients involved

in nutrition and metabolism. The following Table 1 describes the content of the different

LMWC in other dietary food. The units are given in mg/100 g representing a typical serving.

Table 1. Content of different LMWC in dietary food.

Carbohydrate Food Content (mg/100 g) Reference

Fructose

Endive 687.1 (3.9) [15]

Iceberg lettuce 485.6 (7.6) [15]

Spinach 47.5 (6.4) [15]

Onion 1760.1 (434.2) [15]

Eggplant 827.0 (96.8) [15]

Glucose

Escarole 239.3 (4.8) [15]

Chicory leaves 229.5 (25.8) [15]

Radish 799.9 (108.3) [15]

Cabbage 693.7 (19.9) [15]

Beet root 220.7 (75.5) [15]

Galactose

Cresta lettuce 2.0 (0.5) [15]

Purple yam 181.7 (3.5) [15]

Onion 12.7 (0.9) [15]

Spinach 2.2 (0.1) [15]

Cabbage 61.1 (0.5) [15]

Mannito

l

Pumpkin 400 [72]

Celery 100 [72]

Seaweed Trace [72]

Spinach 0.4 (0.5) [15]

Purple yam 141.1 (7.2) [15]

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Sorbitol

Rucola 280 (26) [73]

Yellow beans 150 (13) [73]

Cabbage 180 (16) [73]

Fenel (bulb) 100 (9) [73]

myo-Inositol

Escarole 4.1 (0.2) [15]

Eggplant 21.5 (0.2) [15]

Egg 5-34 [74]

Coffee Trace-2200 [75]

Honey 10-220 [76]

scyllo-Inositol

Grape 80 [77]

Carrot 150-580 [78]

Coriander 160-240 [78]

Wine 10-70 [79]

Eggplant 1.6 (0.0) [15]

Sucrose

Spinach 54.4 (14.7) [15]

Beet root 10697.5 (1010.9) [15]

Eggplant 147 (11.93) [15]

Artichoke 3150 (184) [73]

Garlic 2050 (126) [73]

Maltose

Grape 60 [80]

Nectarine 90 [80]

Broccoli 420 [80]

Iceberg lettuce 20 [80]

Raisins 180 [80]

Melibiose Honey 2443 (74.5) [81]

Radix rehmanniae 272 (274) [82]

Galactinol

Lentils 50 [83]

Beans 60 [83]

Soya 10-40 [84]

Alfalfa 127-169 [85]

Chickpea 80 [83]

Raffinose

Onion 230 (11) [73]

Parsnip 240 (15) [73]

Scallion 780 (39) [73]

Beet root 37.7 (8.6) [15]

Cabbage 1.3 (0.1) [15]

1-kestose

Beet root 16.9 (2.6) [15]

Purple yam 19.0 (1) [15]

Raspberry 320 (0.9) [73]

Cherry 220 (11) [73]

Apricot 80 (0.4) [73]

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Stachyose

Apple traces [73]

Apricot traces [73]

Chickpeas 2700 [86]

Green peas 3540 [86]

Soya 3500 [86]

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Chapter 2. Analytical techniques employed in the analysis of carbohydrates.

2.1. Determination of total carbohydrates by colorimetric analysis-sum parameters

The phenol-sulfuric acid method is a simple and rapid colorimetric method to determine total

carbohydrates. Percentage of carbohydrates on typical food labels are determined using this

sum parameter method.

The method employs concentrated sulfuric acid that breaks down the glycosidic bond of

polysaccharides, oligosaccharides, and disaccharides to produce monosaccharides. In this

conditions, pentoses and hexoses are dehydrated to furfural and 5-hydroxymethyl furfural

respectively [87]. Both compounds react with phenol to produce a yellow-gold color. It is

recommended to measure the absorption at 480 nm if the sample is considered to be high in

pentoses (such as wheat bran or corn bran). In the case of a product under analysis with high

hexose content, the absorption is measured at 490 nm. The color formed in the reaction is stable

for several hours [87]. Other phenolics compounds such as naphtol could be employed as well.

The use of sulfuric acid in this method makes this technique remarkably tedious. However, the

accuracy of the method is within ± 2% under proper conditions [87].

2.2. Gas chromatography applied to the analysis of carbohydrates.

2.2.1. Derivatization procedure.

Carbohydrates, due to their high polarity, hydrophilicity and low volatility, have to be converted

into volatile derivatives before analysis by gas chromatography (GC). This process confers

volatility and stability through different reactions based on the substitution of all active

hydrogen atoms by non-polar groups. Usually, the hydroxyl groups are silylated, acetylated or

trifluoroacetylated, although they can also be methylated or ethylated [88].

Ruiz-Matute et al. [88] reviewed the advantages and drawbacks of the main derivatization

methods for carbohydrates analysis. The methods evaluated were derivatization to methyl

CHAPTER 2

32

ethers, acetates, trifluoroacetates, alditol acetates, aldonitrile acetates, silylation and dialkyl

dithioacetals.

One of the most sought-after derivatization methods is silylation. This method, developed by

Sweeley et al. [89], consists in the introduction of dimethylsilyl, trimethylsilyl or tert-

butyldimethylsilyl groups in the molecule to form the corresponding silyl ethers. As a first step

of the derivatization process, it is necessary to dissolve the sample in non-accusative solvents

such as pyridine or dimethylsulfoxide. Silymethyldisilazane (HMDS), trimethylchlorosilane

(TMCS), trimethylsilylimidazole (TMSI) or bis (trimethylsilyl)-trifluoroacetamide (BSTFA) or

combinations of them are used as silylating reagents [90]. The silylation reaction occurs with a

temperature ranging from room temperature to 45°C. Some authors have proposed a reaction

time ranging between 5 to 30 minutes. However, the disadvantage of this type of derivatization

is the formation of up to 5 silylated forms for the reducing carbohydrates, which can hinder the

analysis of complex mixtures due to the complexity of the chromatogram [91].

The problem of the formation of 5 silylated forms is solved in the derivatization method to

trimethylsilyl oximes (TMSO). The derivatization consists in the use of NH2OH to decrease the

number of chromatographic peaks of reducing carbohydrates to anti (E) and syn (Z) isomers

and posterior conversion of the carbonyl group to an oxime before silylation. As a consequence,

the number of chromatographic peaks is reduced to 2 for reducing carbohydrates and 1 for non-

reducing carbohydrates. TMSO derivatives have high volatility and have been widely employed

for the determination of the carbohydrate composition in food [92, 93].

2.2.3. Analysis of carbohydrates by gas chromatography.

Carbohydrate analysis by gas chromatography can be carried out employing different detectors

such as flame ionization detector (FID), electron capture detector (GC-ECD) or mass

spectrometry (GC-MS).

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Morgan et al. [94] reported the construction of a gas chromatographer incorporating an FID

detector. In this gas chromatographer, the detector collects the ions formed during the

combustion of organic compounds in a hydrogen flame. The number of ions generated is

proportional to the concentration of organic species in the sample gas stream [94].

Lovelock et al. [95] reported the first gas chromatographer incorporating an ECD detector. ECD

detectors are recommended for the analysis of halogens, organometallic compounds, nitriles,

and nitro compounds. The ECD detector (electron capture detector) employs beta particles to

ionize the carrier gas and produce electrons consequently. In conditions of a constant electric

field applied between two electrodes, there will be a constant electron current. The different

compounds from the injected samples will capture part of the electrons, which will be detected

as a decrease in the current intensity.

GC coupled to mass spectrometry (GC-MS) applied to the carbohydrates analysis is quite

challenging due to the similarities of the fragmentation patterns. However, the use of silyl

derivatives has the advantage of different diastereoisomers showing changes in their mass

spectrum. These differences allow the assignment of different ions to different carbohydrates

in function of their structure [96].

The identification of the derivatives in GC-FID, GC-ECD and GC-MS is based on the use of

commercial standards. However, for GC-MS analysis, the identification of LMWC is typically

performed using linear retention indices (IT), relative intensities of characteristic m/z fragment

ions and bibliographic data.

2.3. LC operation modes for the analysis of carbohydrates

LC is a technique widely used for the analysis, separation and isolation of carbohydrates. The

chromatographic operation modes historically used for the analysis of carbohydrates include

normal phase liquid chromatography (NPLC), reverse phase liquid chromatography (RPLC)

and high-performance anion-exchange chromatography (HPAEC). However, in recent years,

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34

the most commonly used mode of operation for carbohydrate analysis using LC is hydrophilic

interaction chromatography (HILIC).

2.3.1. Normal phase liquid chromatography

Normal phase liquid chromatography (NPLC) employs a polar stationary phase and a non-

polar, non-aqueous mobile phase. This operation mode offers considerable variability in

selectivity with an appropriate selection of the mobile phase. Thus, this operation mode has the

advantages of separation of low-molecular moderately polar samples based on the differences

in the number and position of functional groups of the analyte.

Among the stationary phases used in NPLC, silica gel ([SiO2]x [H2O]y) has been extensively

employed. After column preparation, the surface of the silica gel consists mainly of hydroxyl

groups bound to silica atoms (silanol groups). These silanol groups predominantly bind analytes

by polar interactions (hydrogen bonding, π–π and dipole-dipole interactions). Stationary phases

such as aluminium oxide or chemically modified silica gel are also used (amino, diol, nitro or

cyano group-containing chemicals are used to modify the silanol groups) [97].

2.3.2. High-performance anion-exchange chromatography (HPAEC)

The HPAEC is used for LMWC analysis due to its high sensitivity and selectivity [98]. This

type of chromatography is based on the ionization of carbohydrates in alkaline conditions (pH

9-13). The chromatographic separation takes place in columns with ion exchange resins. As

eluent, sodium hydroxide with sodium acetate is commonly used. Under basic conditions, the

open chain form of LMWC dominates, reducing the complexity of the chromatograms.

Different applications of this chromatography in the LMWC in food or plants have been

proposed [99, 100].

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2.3.3. Reverse phase liquid chromatography (RPLC)

RPLC is one of the operation modes most commonly employed in analitical chemistry. This

operation mode employs a non-polar stationary phase consisting of alkyl or aryl groups bound

to a micro-silica particle surface (C8, C18). The polar mobile phase consists of water and an

organic solvent (methanol, and acetonitrile mainly). The retention mechanism is based on

hydrophobic interactions of the analytes with the stationary phase. Therefore, the retention

mechanism depends on the characteristics of the analyte and mobile phase.

Carbohydrates are poorly retained in RPLC as a result of their high polarity. The affinity of

carbohydrates to the hydrophobic stationary phase could be increased through derivatization

[101, 102]. Different methods of derivation have been discussed previously by Lamarini et al.

[101].

2.3.4. Hydrophilic interaction liquid chromatography (HILIC)

Historically, HILIC has been considered as a variant of normal phase liquid chromatography.

However, the separation mechanism of this operation mode is quite more complex than the

mechanism established in NPLC [103]. Alpert [104] was the first author, in 1990, to propose

the acronym HILIC for this type of chromatography.

HILIC is an LC operation mode suitable for the analysis of polar and hydrophilic compounds,

poorly separated in RPLC. This operation mode uses highly polar and hydrophilic stationary

phases such as silica or silica functionalized with amine, amide or zwitterionic groups, among

others. The most common mobile phase is composed of acetonitrile in a high proportion (50-

95%) and water (5-45%). Both solvents could contain different types of additives such as acids,

bases and salts.

The retention mechanism of HILIC is still unclear, although the most accepted theory explains

the retention as the distribution of the analyte between the organic phase and an aqueous film

partially immobilized in the stationary phase. Depending on the type of stationary phase (amide,

CHAPTER 2

36

silica, zwitterionic), other types of additional mechanisms may exist. As an example, in the

zwitterionic phases, some electrostatic interactions between the analytes and the stationary

phase itself may take place depending on the pH of the mobile phase [103, 105, 106].

This chromatographic operation mode has been extensively used for the analysis of

carbohydrates from different food matrices. A summary of the main applications of this

chromatography applied to the analysis of carbohydrates in different food matrices has recently

been published [107].

2.3.5. Detectors employed for the analysis of carbohydrates.

The lack of chromophore groups in the structure of carbohydrates complicates their detection

using LC. Different detectors ( electrochemical, ultraviolet, fluorescence, refraction index, mass

spectrometers ) widely used for the analysis of carbohydrates are discussed in this section.

The electrochemical detectors usually have limited stability. The detection by HPAEC is carried

out using pulse amperometry. In this case, the detection consists in the measurement of the

electric current generated by the oxidation of the carbohydrates on the surface of a platinum or

gold electrode and its subsequent reduction for cleaning the electrode. Also, this type of

chromatography requires specific pumps adapted to work at highly basic pH.

The refraction index (RI) is one of the most commonly used detectors for carbohydrate

analyisis. This detector measures the refractive index of carbohydrates relative to the solvent.

The sample preparation does not require a previous step of derivatization. However, it can only

be used when the elution is in isocratic mode, so its application for the analysis of complex

mixtures is limited [108].

The light scattering detector (ELSD) measures the amount of light scattered by analyte particles

created by evaporation of a solvent as it passes through a light beam. However, the main

INTRODUCTION

37

disadvantage of this detector is the problem of limited reproducibility of the response factor in

quantification [109, 110].

Regarding UV and fluorescence detectors, their employment for carbohydrate analysis requires

a previous step of derivatization. The different methods of derivatization introduce

chromophore groups before, during or after the chromatographic separation. The chromophore

groups allow the detection of carbohydrates using ultraviolet (UV) or fluorophores, for

fluorescent detection [101]. The reagents most commonly used for this purpose are 2-

aminopidine, 2-aminoacridone, p-aminobenzene, 2-aminonaphthalene trisulfone and 1-phenyl-

1-3-methyl-5-pyrazolone [102]. The derivatization process produces changes in carbohydrate

properties such as hydrophobicity, improving its resolution in reverse phase systems.

Finally, the MS detectors allow the analysis of carbohydrates without prior derivatization. The

absence of derivatization is an advantage in terms of sample processing time compared to other

detection methods.

2.3.6. Analysis of LMWC using liquid chromatography coupled to mass spectrometry.

The characterization of LMWC using mass spectrometry is considered quite challenging due to

the absence of specific fragmentation patterns.

The use of mass spectrometry for carbohydrate analysis has allowed the determination of their

molecular weight and also provided structural information of LMWC. Among all the possible

ionization sources employed for LMWC analysis, electrospray ionization (ESI) is widely used,

although the employment of atmospheric-pressure chemical ionization (APCI) is also possible.

ESI can be easily coupled to quadrupole (Q), ionic tramp (IT), time of flight (TOF) and

quadrupole coupled to time of flight (Q-TOF) mass spectrometers.

TOF mass spectrometers are used for the determination of high molecular weight and molecular

formulae. The ionization of carbohydrates is difficult due to the lack of acidic or basic

functional groups. Carbohydrates have a stronger affinity for alkali metal or alkali earth metal

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38

ions than to protons. In positive ion mode, the identification of carbohydrates is based on the

determination of the molecular weight of the sodium molecular ion, although the presence of

molecular ammonium ion can be detected. In negative ion mode, the determination of the

carbohydrates is based on the determination of the molecular weight of the deprotonated

molecular ion of the carbohydrate.

MS/MS or tandem MS can be used to identify the presence of a carbohydrate. Neutral loss

characteristics of carbohydrates are 18 Da (H2O), 30 Da (CH2O), 60 Da (C2H4O2), 90 Da

(C3H6O3), 120 Da (C4H8O4), 132 Da (C5H8O4), 162 Da (C6H10O5).

As mentioned previously, the application of mass spectrometry to the characterization of

disaccharides and oligosaccharides is quite challenging due to the absence of a clear

fragmentation pattern. Tedious approaches, such as permethylation of the hydroxyl groups, can

be used to elucidate the carbohydrate sequence through the identification of branching sites and

the linkage between monomers [111].

Zhang et al. [112] and Hermandez-Hernandez et al. [113] have proposed the characterization

of oligosaccharides based on the MS2 fragmentation behaviour of several standard

disaccharides containing galactose, glucose and fructose units. This characterization was based

on the observation that similar fragmentation profiles, but different relative ratios of the

fragment ions had been previously observed in disaccharides with the same linkage but different

monosaccharide units. Relative ratios of the fragment ions from disaccharides with different

linkage between the monosaccharides units were reported by Hernandez-Hernandez et al [113].

In line with this observation, Simoes et al.[114] identified the anomeric configuration (α or β)

of glucopyranosyl-glucose disaccharides by tandem mass spectrometry. These authors

observed differences in the relative abundances of specific product ions obtained from

collisionally induced dissociation (CID) of the lithium adduct. Overall, this approach is less

INTRODUCTION

39

tedious and time-consuming than the combination with complex isolation techniques by

fractionation methods and the subsequent analysis by NMR and methylation procedures.

INTRODUCTION

41

Chapter 3. Dietary food

3.1. Cocoa

3.1.1. Economical impact.

Cocoa powder is the crucial ingredient in the production of chocolates and related products.

The global market for chocolate, with more than 4.5 million tons of cocoa beans consumed

annually, reached in 2016 an economic value of USD 98.3 billion [115].

Cocoa prices have increased by approximately 50% since the first data available in

MarketsandMarkets database in 2005, reaching the price of € 2,211.55 ton on October 2018

[116].

In the present, more than half of the top 20 chocolate consuming countries are European. World

Cocoa Foundation estimates that 50 million people around the world have their income based

on cocoa farming or industry.

3.1.2. Farming

The tree Theobroma cacao L. grows within 20° latitude of the equator. It requires a warm and

humid climate for growth Therefore, major producer countries are Ivory Coast, Ghana,

Indonesia, Brazil, Nigeria, Cameroon, Malaysia and Ecuador.

The tree has an average productive life of 25 years, with two flowering cycles a year. The fruit

of the tree is called cocoa pod. Cocoa pods have an ovoid shape, average size of 15-30 cm long

and 8-10 cm wide, different shades of color (from yellow to red-orange) and an average ripe

weight of 500 g.

On average, each cocoa pod contains between 20 to 60 cocoa beans (seed). Morphologically,

each seed consists of two cotyledons (nibs) and an embryo (radicle) surrounded by a seed coat

(testa). The beans are enveloped in a white and viscous pulp rich in carbohydrates.

The four predominant varieties of cocoa tree are Criollo, Forastero, Trinitario and Nacional.

Criollo, characterized for producing a unique aroma, is the most commonly farmed variety in

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42

south-central America. Forastero, characteristic for its strong aroma, is the variety typical from

the Amazon region. Trinitario is a hybrid cultivar between Criollo and Forastero characteristic

for its high productivity and resistance to diseases. Nacional, mainly farmed in Ecuador, is

characteristic for its full cocoa flavor with additional floral and spicy notes [117].

3.1.3. Processing of cocoa beans to chocolate bar.

The processing of cocoa beans into chocolate involves a microbiological fermentation step

followed by a drying step, both performed at the location of the farms. The microbiological

fermentation is the essential step for the production of precursors of the flavour and aroma of

chocolate. The fermentation usually lasts 5 to 6 days. Beans fermented shorter than 5 days can

be considered under-fermented. Conversely, beans fermented longer than 6 days can be

considered over-fermented, which are characterized by “hammy” off-flavours [118].

The spontaneous fermentation process starts with piling up the beans together with the pulp.

The fermentation of the beans takes place in two phases: anaerobic and aerobic. Therefore, the

microorganism composition changes during the fermentation. The different microorganisms

involved use the carbohydrates from the pulp as the main source of nutrients. During the first

two days, several species of yeast proliferate, leading to peak production of ethanol. In the

following phase lactic acid bacteria (day 1 to 3) and acetic acid bacteria (day 1 to 4), proliferate,

with the following peak production of lactic and acetic acid respectively. Finally, some

filamentous fungi may appear on the surface. Diversity in the microorganisms according to the

country of origin has been reported. This diversity has been mainly attributed as the cause of

the diversity in cocoa flavour and taste from different origins [119-121].

The fermentation index (FI) is a measurement of the extension of the fermentation. Different

methods to assess this parameter have been reported. Among the different methods reported,

cut-test and UV-visible are the most common. Cut-test method consists in the evaluation of the

internal color of cocoa beans during the fermentation, using a score based on purple and brown

INTRODUCTION

43

beans. With respect to UV-visible methods, the ratio of total free amino acids between

fermented and unfermented cocoa beans has been proposed to assess FI. Furthermore, the ratio

A460 nm/A530 nm of methanolic extracts is another parameter employed to assess the

fermentation index [122].

After the fermentation, the process continues in the factory. Inside the factory, the beans are

sorted according to their size. Then, the beans are cleaned to remove the remaining pulp. During

this process, the cracked husks are air-separated (winnowing) from the nibs, which are

processed into chocolate.

For this purpose, beans are ground several times and roasted under alkali conditions to make a

fluid paste called cocoa liquor. The process of roasting varies depending on the bean variety

and the properties of the product desired. The main variables are duration (from 15 min to 90

min) and temperature applied (110° C to 130°C) During roasting, the cocoa beans lose their

moisture, taking place the characteristic formation of color pigments, flavours and chocolate

aromas [123].

The cocoa powder is obtained by pressing the cocoa liquor to separate cocoa butter from the

cocoa cake. The cocoa cake is grounded to cocoa powder, with several applications in cooking

and baking [124].

The production of chocolates consists in the mixture of cocoa butter with cocoa liquor, sugars,

sweeteners, milk powder (for milk chocolate) and emulsifiers according to the requirements of

the final product. Once the chocolate mixture is prepared, “conching” is performed to increase

the homogeneity of the samples followed by tempering to get fine crystallization [124].

3.1.4. Health benefits of cocoa and chemical composition

Cocoa and chocolate consumption for medicinal purposes has been reported since the aztec

period [125]. The evidence of this health-promoting benefits has raised scientific interest in the

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44

chemical composition of cocoa, with the corresponding identification of flavonoids as the

compounds responsible for its health benefits [126-131].

The complexity of cocoa beans has been previously addressed by the use of Fourier transform

ion cyclotron resonance mass spectrometer (FTICR-MS) [132]. The estimated average

composition of unfermented cocoa beans is 32–39% water, 30–32% lipids, 10–15% proteins, 5

– 6% polyphenols, 12-15% polysaccharides, 3 - 5% of low molecular weight carbohydrates, 1–

2% theobromine, 1% organic acids and 1% caffeine [133]. During fermentation and subsequent

drying of the cocoa beans, several reactions take place, resulting in a reduction of water content,

a decrease in pH level and significant changes in lipid, carbohydrate, polyphenol and protein

composition [117].

3.1.5. LMWC in cocoa beans.

The analysis of LMWC in cocoa beans has been neglected in comparison to other metabolites.

This tendency might have been attributed to the low abundance of LMWC in unfermented beans

(5% DM) and fermented beans (between 0-1%) and the absence of methodologies for their

analysis. In fact, in many cases, the carbohydrate composition was assesed as the difference

with other major components in the cocoa bean.

In 1954, Cerbulis [134] studied for the first time LMWC in cocoa beans. This author employed

thin-layer chromatography (TLC) as the analytical method. The study performed reported the

presence of monosaccharides (fructose, glucose, galactose), disaccharides (sucrose and

melibiose), trisaccharides (raffinose and mannotriose) and tetrasaccharides (stachyose).

Moreover, the presence of other unknown carbohydrates was also reported. One year later, in

1955, Cerbulis reported the presence of myo-inositol, planteose, verbascose, and the presence

of unknown oligosaccharides [135].

In 1972, Reineccius et al. [136] described the presence of pentitol, fructose, sorbose, glucose,

mannitol, inositol and sucrose in unroasted cocoa beans using gas-chromatography-mass

INTRODUCTION

45

spectrometry. However, this author did not describe the presence of trisaccharides and

oligosaccharides, probably due to the limitations of the methodology used.

In 2003, Redgwell et al. [137] reported the presence of glucose, fructose, sucrose, raffinose,

stachyose and verbascose in cocoa bean samples from Ghana, Ivory Coast and Ecuador using

HPLC-Dionex. The authors studied the effect of roasting on the LMWC content. The study

detected a loss of up to 80% in the quantities of monosaccharides during roasting.

Apart from the studies mentioned above, the monosaccharide and sucrose composition has been

reported in studies focused on fermentation. The observed trend has been a sequential

degradation for sucrose across the different fermentation days and a slight increase of the

monosaccharide and mannitol content [138, 139]. No data on the oligosaccharide content during

the spontaneous fermentation is so far available.

3.2. Green tea.

3.2.1. Processing of green tea.

There are six main types of teas on the market: green, yellow, white, black, oolong and Pu-erh.

The differences between them are the consequence of differences in the processing steps,

especially in the steps involving oxidation and fermentation.

Tea has been traditionally grown in southwestern Asia and northeastern India Nowadays, the

cultivation has been spread to Japan, Korea, Thailand, Taiwan, Sri Lanka, Indonesia, central

Africa, Turkey, Argentina and Russia [140].

Tea cultivation requires 120-150 cm3 of rain, temperatures from 12 to 30 ºC and altitude ranging

from 0 to 3000 m from sea level. Two main plant varieties are used for the commercial

production of tea, Camellia sinensis var. assamica and Camellia sinensis var. sinensis. These

varieties are found in geographically distinct regions. Camellia sinensis leaves collected at high

altitude have been associated with higher tea quality. Camellia sinensis var. assamica grows as

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a tree making it more suitable for the tropical and subtropical climates. Camellia sinensis var.

sinensis grows as a bush, better adapted to colder climates [141].

Leaves can be harvested manually or mechanically. High-quality green tea has been associated

with manually harvested tea. After harvesting, the processing of green tea involves spreading,

fixing, rolling, shaping and drying. The spreading reduces tea leaves moisture by approximately

30%. Spreading produces the hydrolysis of non-water soluble carbohydrates and the formation

of catechins. It also promotes the release of the grass-like flavour [140].

The fixing step is performed to inhibit the polyphenol oxidase (PPO) and peroxidase (POD)

enzymatic activities to prevent the oxidation and fermentation of the leaf. This process consists

in the exposition of the leaves to an elevated temperature for 10-15 minutes. The process of

pan-firing and steaming are the most common process employed in the industry. The pan-firing

process consists in the introduction of the leaves in a pan previously heated at high temperature

(around 180°C). Steaming process consists in the exposition of the leaves to water steam [140].

The processing of green tea continues with the rolling step. In this step, the cell walls are broken

and this helps to release further leaf moisture. Finally, the leaves are shaped into various forms

and dried. The drying process can be performed under the sunlight or by baking, employing a

pan or basket [142].

3.2.2. The economic relevance of green tea

In the past decades, the global tea industry has experienced fast growth in response to an

increase in the number of consumers, driven by population growth, urbanization and rising

incomes.

According to data from FAO, in 2016, the production reached a level of 5.73 million ton and

the consumption a level of 5.53 million ton. It has been estimated that 80% of the production

corresponds to black tea and 20% corresponds to green tea. Two countries were the principal

producers, China (43%) and India (22 %). The European Union was the largest importer in

INTRODUCTION

47

2016, responsible for 18 % of the tea imports. The organism “Persistence Market Research”

projects an expansion of 5% of the tea market between 2016 and 2024, reaching a total value

of USD 21.33 billion.

World green tea production is expected to grow at 8.2% until 2024. The production increase is

predicted as a consequence of the expansion in green tea consumption outside Asia, partially

attributed to the perception that this beverage has health-promoting effects.

3.2.3. Health benefits and chemical composition.

Green tea is the first herbal plant infusion consumed by humans in history. According to legend,

the first brew of this infusion was performed in 2737 BC by Chinese emperor Shennong [143].

Its beneficial health effects were first suggested by Chinese culture during the Tang Dynasty

around 600 AD. More details about these beneficial health effects were described in 1191 in

the “Kissa Yoki”, suggesting the effect of this beverage in five vital organs [143]. Since then,

a vast number of epidemiological studies and clinical intervention studies have demonstrated

and verified these beneficial effects. Green tea improves cardiovascular health, reduces the

incidence of cancer, increases mental performance and improves osteoporosis, among other

functions [144].

These findings have stimulated intense research about the chemical composition of green tea,

focusing on the identification of the key tea secondary metabolites mediating these beneficial

health effects. The beneficial health effects have been mainly attributed to the composition of

catechins and caffeine. On average, the composition of green tea in terms of dry weight (DW)

is 30 % phenolic compounds, 26 % fibre, 15 % proteins, 7% carbohydrates, 7% lipids, 5%

minerals, 4% amino acids, 2% pigments and 4% other compounds such as organic acids or

caffeine [144].

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3.2.4. LMWC of green tea

The LMWC in green tea have been scarcely studied in comparison to the extensive information

provided in the literature on polyphenols and volatile compounds. Two studies have reported

the monosaccharide (fructose and glucose), disaccharide (sucrose and maltose) and inositol

(myo-inositol) composition of commercial green tea, employing high-performance liquid

chromatography (HPLC) in a limited number of samples [145, 146].

Zhou et al. (GC) [147], with the use of gas chromatography (GC), evaluated the enzymatic

activity of galactinol synthase. The study evaluated the activity of this enzyme under biotic and

abiotic conditions in clone cuttings of Camellia sinensis L grown under controlled conditions.

One of the parameters employed for this evaluation was the monitoring of raffinose and

verbascose quantities. This manuscript showed the presence of stachyose and galactinol.

However, the authors did not inform about quantities of these compounds.

Le Gall et al.[148], with the employment of Nuclear Magnetic Resonance (NMR), in a study

analyzing 191 tea samples reported the presence of 2-O-(β-L-arabinopyranosyl)-myo-inositol

as a quality indicator of green tea. The author found an elevated content of this carbohydrate in

high-quality teas produced with young shoots. Also, this author proposed the involvement of

this carbohydrate in earlier stages of tea leaf development.

3.3. Kale

3.3.1. Introduction to Cruciferous vegetables

Kale, Brassica oleracea var. Sabellica, is a plant from the Brassicaceae family. The

Brassicaceae family contains 341 genera and more than 3,700 species that are commonly

known as cruciferous vegetables [149]. Plants from the Brassicaceae family have been

described to have a protective effect on chronic diseases, both in vitro and in vivo [150-152].

Several bioactive compounds present in the edible parts of the cruciferous vegetables, such as

INTRODUCTION

49

phenolic compounds, ascorbic acid, carotenoids, glucosinolates and tocopherols have been

linked to health-promoting effects. Most of these compounds are mainly characterised by their

antioxidant capacities [153, 154], useful to neutralize the oxidative stress caused by the

accumulation of reactive oxygen species (ROS), a by-product of oxidative respiration. High

levels of ROS are associated with various pathologies, including carcinogenesis,

neurodegeneration, atherosclerosis, diabetes, and ageing [155].

3.3.2. The economic relevance of kale

Many of the cruciferous vegetables have high economic importance, such as mustard, cabbage,

rutabaga, turnip, brussel sprouts, broccoli, cauliflower, radish and kale. Among the different

cruciferous vegetable, broccoli was considered as “superfood” due to the extensive studies on

its chemical composition and biological properties [156].

In recent years, kale has experienced an increase in popularity. Consumers perceive kale to be

highly nutritious and it is claimed to have a beneficial effect on human health, being named as

a “superfood” [157]. The concept of “functional food”, popularly mentioned also as

“superfood”, refers to those foods with scientific evidence that support effects of their

components in improving the general conditions of the body, protecting against diseases or/and

curing diseases.

3.3.3. Farming

Kale is grown for its edible shoots and young leaves. It is well adapted for growing in winter,

resisting temperatures even as low as −15 °C, as well as in the summer, resisting high

temperatures. In North Germany, kale is harvested typically after the first frost in November

arguing that low temperatures induce better palatability and increase sweetness.

3.3.4. The chemical composition of kale and health effects of the main components.

According to the USDA Food Composition Databases, kale contains an average of 89% of

moisture, followed by fiber (4 %), proteins (3 %), lipids (1.5 %) and LMWC (1%),

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Kale is an excellent source of mineral nutrients, especially calcium, that is also of higher

bioavailability than in other plants [158]. A recommended serving of 100 g of kale provides 25

% and 10% of the recommended daily uptake (RDI) for calcium and other minerals respectively

[159].

Kale contains a high concentration of vitamin A and C, providing every serving 100% and 40%

of the RDI respectively [78]. Apart from these vitamins, other antioxidant compounds are

abundant in kale, such as glucosinolates, sulforaphanes and polyphenols [160].

Vitamin C

Different beneficial effects for vitamin C have been reported. First, vitamin C prevents the

inflammatory responses that lead to atherosclerotic plaque formation after oxidation of LDL

particles by ROS [161]. Second, vitamin C combined with ferulic acid, prevents the

abnormalities in lipid metabolism after myocardial infarction [162].

Vitamin A

The precursors of vitamin A in kale are lutein, β-carotene, violaxanthin and neoxanthin [163].

Vitamin A plays a role in the normal functioning of ocular, skin, bone, gastrointestinal, and

respiratory systems. Higher β-carotene serum levels have been linked to lower rates of cancer

[164], cardiovascular diseases [165] and myocardial infarction [166].

Polyphenols

Kale contains a selection of glycosylated flavonoids, mainly quercetin, kaempferol and luteolin

[84]. Apart from their antioxidant properties [167], phenolic compounds have been described

to regulate metabolism, inhibiting the growth of adipose tissue [168] and to normalize blood

glucose levels in diabetic rats [169].

Glucosinolates

Glucosinolates are a type of secondary metabolites found mainly in cruciferous vegetables.

Kale contains a concentration of glucosinolates of 2.25-93.90 μmol/g DW [170, 171] These

INTRODUCTION

51

compounds are activated after cell damage by the action of the enzyme myrosinase. The

activation consists in the production of isothiocyanates, nitriles, thiocyanates, epithionitriles,

and oxazolidinethiones, compounds biologically active with direct health-promoting effects

[172, 173].

The most abundant glucosinolate present in kale is sinigrin [174]. For allyl isothiocyanate, a

product of the sinigrin hydrolysis, anti-cancer, antibacterial, antifungal, antioxidant, anti-

inflammatory and wound healing properties have been reported [175].

3.3.5. LMWC in kale

The study of the LMWC in kale has been overlooked in comparison to other metabolites. To

the best of the author’s knowledge, there are few studies focused on the LMWC profile in kale.

In 2016, Tharavajan et al. [159] reported the LMWC composition in 25 kale genotypes

commonly farmed in USA. The analytical methodology employed was HPLC with pulsed

amperometric detection. The study reported the presence of fructose and glucose as main

LMWC followed by sucrose and lower contents of sorbitol, mannitol, arabinose, mannose and

xylose.

In 2017 Pathirana et al. [176] reported the variation of the LMWC content in kale and the

changes related to growth in a greenhouse under normal growing conditions for 4 weeks,

followed by 2 weeks of moisture stress (drought: 40% moisture; control: 80% moisture) and 2

weeks of recovery. The study reports for the first time the presence of oligosaccharides as

raffinose, 1-kestose, stachyose or verbascose.

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AIM OF THE STUDY

AIM OF STUDY

65

This thesis has as the starting point the increasing interest from consumers and food companies

on the chemical composition of healthy foods. Considering that LMWC, apart from their

essential role as an energetic source, has a proven beneficial effect on human health, this thesis

has aimed to generate an in depth-knowledge of the LMWC composition of different dietary

foods and beverages.

The starting point of this thesis is the framework of COMETA project. This project has aimed

to characterize the metabolome of cocoa beans and the changes related to the processing. Within

the general objectives of this project, the first objective of this thesis was to characterize the

LMWC profile in cocoa beans, as well as the monitorization of the main changes during the

spontaneous fermentation. In the direction of achieving this objective, the following partial

objectives have been proposed:

1. Development and validation of chromatographic methods suitable for the qualitative

and quantitative analysis of the LMWC in cocoa beans.

2. Comprehensive characterization of the LMWC in cocoa beans.

3. Quantification of the main LMWC identified in cocoa beans.

4. Evaluation of the LMWC as suitable indicators of origin and fermentation status.

5. Monitoring of the changes associated with the LMWC during the spontaneous

fermentation.

6. Establishment of the kinetic parameters of the changes related to the LMWC content

during spontaneous fermentation.

The second part of this PhD has been devoted to achieving a better understanding of the LMWC

in one of the most consumed beverages (commercial green tea) and in functional foods as as

kale (Brassica oleracea).. In order to achieve this objective, the following partial objectives

have been proposed:

AIM OF STUDY

66

1. Establishment and validation of chromatographic methods suitable for the quantitative

analysis of the LMWC content in green tea or kale.

2. Characterization and quantification of the main LMWC in commercial green teas from

different origins.

3. Evaluation of the LMWC content in CGT as suitable indicators, together with other

established physical and chemical quality indicators, to characterize commercial green

tea.

4. Characterization and quantification of the main LMWC in three types of kale grown

during development of kale and in the procces of cold acclimation.

RESULTS

Part-1 LMWC in cocoa beans (chapters 4-6)

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71

Chapter 4. Profiling, quantification and classification of cocoa beans based on

chemometric analysis of carbohydrates using hydrophilic interaction liquid

chromatography coupled to mass spectrometry.

Roberto Megías-Pérez , Sergio Grimbs, Roy N. D’Souza, Herwig Bernaert, Nikolai Kuhnert

Manuscript published in Food Chemistry Volume 258, 30 August 2018, Pages 284-294

https://doi.org/10.1016/j.foodchem.2018.03.026


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72

ABSTRACT

Fifty-six cocoa bean samples from different origins and status of fermentation were analyzed

by a validated hydrophilic interaction liquid chromatography-electrospray ionization-time of

flight-mass spectrometry (HILIC-ESI-TOF-MS) method. The profile of the low molecular

weight carbohydrate (LMWC) was analyzed by high resolution and tandem mass spectrometry,

which allowed the identification of mono-, di-, tri- and tetrasaccharides, sugar alcohols and

iminosugars.

This study provides for the first time in a large sample set of samples, a comprehensive absolute

quantitative data set for the carbohydrates identified in cocoa beans (fructose, glucose,

mannitol, myo-inositol, sucrose, melibiose, raffinose and stachyose). Differences in the content

of carbohydrates were observed between unfermented (range of 0.9 - 4.9 g/g DM) and

fermented (range 0.1-0.5 g/g DM) cocoa beans.

The use of multivariate statistical tools allowed the identification of biomarkers suitable for

cocoa bean classification according to the status of fermentation, procedure of fermentation

employed and number of days of fermentation.

Keywords: carbohydrates, cocoa beans, Theobroma cacao, HILIC-ESI-TOF-MS, multivariate

statistical analysis.

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73

1. INTRODUCTION

Cocoa beans from the tree Theobroma cacao are a sought-after commodity used by the

chocolate industry for the production of cocoa powder, a key ingredient in the production of

chocolates and related products. In 2016, the global market for chocolate reached an economic

value of USD 98.3 billion [1].

The production of cocoa powder starts with the harvesting of the cocoa pods by the farmers.

Once the cocoa pods are opened, unfermented cocoa beans are piled up with the pulp in

platforms, heaps or boxes. In these containers, spontaneous fermentation is induced by

microorganisms, including yeasts, acetobacters and lactobacilli. This crucial step results in the

formation of cocoa flavor and aroma precursors [2]. The standard duration of the fermentation

is five to six days on the average. Longer fermentation time frequently produces undesirable

mold. After fermentation, the cocoa beans are dried under the sun to avoid microbial

contamination during transportation to the industry. The process continues in the factory with

the steps of roasting and alkalization, which also contribute to the formation of the characteristic

color, flavor and aroma of cocoa powder. Once the roasted and alkalized beans are separated

from the shells, the beans are ground at elevated temperatures to yield cocoa liquor. By

hydraulic pressing, cocoa butter is separated from the cocoa cake that is ground into cocoa

powder [3].

The estimated chemical composition of unfermented cocoa beans is 32–39% water, 30–32%

lipids, 10–15% proteins, 5–6% polyphenols, 4–6% starch, 4–6% pentosans, 2–3% cellulose, 2-

3% of sucrose, 1–2% theobromine, 1% organic acids and 1% caffeine [4]. During fermentation

and subsequent drying of the cocoa beans, several reactions take place, resulting in a reduction

of water content, decrease in pH level and significant changes in lipid, carbohydrate, polyphenol

and protein composition [5]. The analysis of fermented cocoa beans by Fourier transform ion

cyclotron resonance mass spectrometry (FTICR-MS) has shown the chemical complexity of

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74

cocoa beans, concluding that the number of different compounds in cocoa is around or even

more than 40000 [6].

The study of carbohydrate composition in cocoa beans has been neglected in comparison to

other primary metabolites present in cocoa beans, such as lipids [7], proteins and peptides [8,9].

Secondary metabolites in cocoa beans, such as flavonoids, have been exhaustively studied due

to their health promoting properties [10-13].

The literature on carbohydrate analysis in cocoa beans to date is limited to few contributions.

In unfermented cocoa beans, the presence of fructose, glucose and sucrose has been reported

previously by gas chromatography-mass spectrometry (GC-MS) and HPLC. In fermented

beans, in addition to the carbohydrates previously mentioned, pentitol, sorbose, mannitol, myo-

inositol and oligosaccharides such as raffinose, stachyose and verbascose have been reported

[14-16]. The presence of myo-inositol and oligosaccharides such as raffinose in cacao beans is

noteworthy due to the bioactive properties attributed to these compounds [17,18].

HILIC, used in this work for the analysis of carbohydrates, has the advantages of ample

chromatographic resolution of polar compounds and easy coupling to MS due to the use of a

hydroorganic mixture as mobile phase over reversed-phase HPLC.

Three objectives were aimed at in this work. The first objective was to perform a comprehensive

characterization of the profile of low molecular weight carbohydrates (LMWC) in cocoa beans.

The second objective was to assess the content of the carbohydrates in unfermented and

fermented beans subjected to different fermentation processes and collected from different

locations. The third objective consisted in the evaluation of the LMWC as indicators of

fermentation status, beans origin, procedure of fermentation employed or the number of days

of fermentation.

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75

2. MATERIALS AND METHODS

2.1. Chemicals

Dichloromethane and LC-MS grade acetonitrile were supplied by Aplichem Panreac

(Darmstadt, Germany). Ammonium hydroxide solution was provided by Sigma Aldrich

(Steinheim, Germany). Deionized water was obtained using a Milli-Q water filtration system.

NH2-SPE cartridges of 500 mg were purchased from Macherey Nagel (Düren, Germany). Asp-

Phe methyl ester, fructose, glucose, myo-inositol, sucrose, raffinose, melibiose and stachyose

were acquired from Sigma Aldrich (Steinheim, Germany) and mannitol was donated by

Bermpohl Apotheke (Bremen, Germany).

2.2. Cocoa bean samples

All samples were provided and certified by Barry Callebaut Belgium in regards to their location,

type of fermentation, duration of fermentation and hybrid name. The samples were collected

between 2014 and 2015.

24 unfermented cocoa beans were collected in Tanzania (n = 1), Ecuador (n = 7), Malaysia (n

= 3), Brazil (n = 1), Indonesia (n = 4) and Ivory Coast (n = 8). Unfermented samples were kept

at -80°C from the collection time until their corresponding processing for analysis.

Fermented cocoa bean samples were classified according to the different fermentation

processes: spontaneous fermentation and other fermentation (OF). The spontaneous

fermentation was induced by piling up the unfermented cocoa beans with the pulp. The beans

were rotated during the first 4 days of fermentation to ensure homogenization. 25 cocoa samples

corresponding to spontaneously fermented beans were analyzed. These samples were collected

in Tanzania (n = 1), Ecuador (n = 7), Malaysia (n = 3), Brazil (n = 1), Indonesia (n = 7) and

Ivory Coast (n = 6). The duration of the fermentation process for spontaneously fermented

beans was variable: 4 days (n=1), 5 days (n=6), 6 days (n=11) and 7 days (n=4). No data on

the duration of the fermentation process was provided for 3 samples.

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76

The term OF groups samples that were fermented by two different procedures: pre-drying and

controlled fermentation. The procedure of pre-drying consisted in drying the unfermented beans

(n=4) under the sun for six to eight hours before the usual spontaneous fermentation. Controlled

fermentation procedure consisted in the spraying of dedicated yeast cultures over the

unfermented beans (n=3) to boost fermentation process. Both procedures of fermentation had a

length of four days.

After fermentation, all cocoa beans, independently of the procedure of fermentation employed,

were dried under the sun for a period of time from 7 to 10 days. After that, the fermented

samples were stored in falcon tubes and kept at 4°C until their corresponding processing for

analysis.

The cocoa beans were de-shelled manually and ground using a mechanical grinder Retsch

(Haan, Germany).

2.3. Estimation of the dry matter content and the content of lipids

Dry matter content was calculated as the difference in weight of 2 g of cocoa powder before

and after heating at 105 °C for 20 hours.

5 g of cocoa powder was defatted for 18 hours by an automated Büchi Soxhlet instrument

(Flawil, Switzerland) using 150 mL of dichloromethane as solvent. The defatted powder was

vacuum dried for one hour with a pressure of 20 mbar and kept in a desiccator for 24 hours.

The lipid content was calculated as the difference in weight of cocoa before and after the process

of defatting.

2.4. Extraction of carbohydrates

150 mg of cocoa powder, previously defatted as described in section 2.3., were weighed and

the carbohydrates were extracted following a protocol available in the literature [19]. First, the

sample was extracted with 2 mL of Milli-Q water under constant stirring for 20 min at room

temperature. Then 8 mL of absolute ethanol was added and stirred continuously for 10 min.

RESULTS

77

Samples were centrifuged at 4400g for 6 min and the supernatant was collected. Precipitates

were subjected to a second extraction with 10 mL of 80% ethanol under the same conditions.

The two supernatants were pooled together.

2.5. Sample preparation: Solid Phase Extraction (SPE).

2 mL of the ethanolic extract was purified by SPE using an NH2-cartridge. The cartridges were

preconditioned with 10 mL of methanol followed by 10 mL of Milli-Q water. The polar fraction

that contains the carbohydrates was eluted using 6 mL of methanol, dried under N2 flow and

then suspended in 2 mL of acetonitrile: water (50:50). 10 µL of a solution of 1 mg/mL of Asp-

Phe methyl ester was added to 1 mL of the above solution as internal standard.

The efficiency of SPE purification for each carbohydrate was evaluated in triplicates using the

apparent recovery (A.R.) formula:

A.R = ((A-B) x 100)/C

A is the amount of carbohydrate quantified after the addition of 0.5 mL of the concentration C

to 1.5 mL of the ethanolic fraction of carbohydrates. B is the amount of carbohydrate detected

in 1.5 mL of the ethanolic fraction and 0.5 mL of 80% ethanol.

2.6. HILIC-ESI-TOF-MS and HILILC-ESI-MSn analysis.

The analyses were performed on an Agilent Technologies 1100 Series HPLC (Karlsruhe,

Germany). Chromatographic separation was achieved on a BEH X-Bridge amide column from

Waters (Hertfordshire, UK) with the following characteristics: 150 × 4.6 mm, 3.5 μm particle

size and 135 Å pore size. The solvents were water with 0.1% of ammonium hydroxide (solvent

A) and acetonitrile with 0.1% of ammonium hydroxide (solvent B). The final pH of the solvents

was adjusted to pH = 10. Gradient elution was performed as follows: 0-5 min 74% B; 37 min

60% B; 37.5 min 74% B. Injection volume was set to 10 µL. The flow rate used was 0.4

mL/min.

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78

A microTOF mass spectrometer fitted with an ESI source (HCT Ultra, Bruker Daltonics,

Bremen, Germany) (HILIC-ESI-TOF MS) operating in positive and negative ion mode in the

range of 50-1200 m/z was used to perform the identification of the molecular formula of the

compounds. For the quantitative and chemometrics analysis only data obtained in positive ion

mode were considered. Internal calibration was achieved using a solution of 0.1 M sodium

formate, solution injected through a six-port valve prior to each chromatographic run. The

electrospray source parameters were adjusted as follows: spray voltage, 4.5 kV; drying gas (N2,

99.5% purity); temperature = 220 °C; drying gas flow, 12 L/min; nebulizer (N2, 99.5% purity)

pressure, 1.6 bar. Data acquisition was performed using HyStar 3.2 software.

Ion trap mass spectrometer fitted with an ESI source (HCT Ultra, Bruker Daltonics, Bremen,

Germany) (HILIC-ESI-MSn) operating in positive Auto MSn mode in the range of 50-1200 m/z

was used to obtain fragment ions. In some cases, the positive and negative targeted mode were

used to achieve fragmentation of specific m/z values. The electrospray source parameters were

adjusted as follows: spray voltage, 4.5 kV; drying gas (N2, 99.5% purity); temperature = 360

°C; drying gas flow, 12 L/min; nebulizer (N2, 99.5% purity) pressure, 1.6 bar. Data acquisition

was performed using Agilent ChemStation software.

Calibration curves were calculated using the normalized area of the Extract Ion Chromatogram

(EIC) of the different standards with respect to the area of the internal standard. The normalized

area of the EIC of the sodium adduct [M+Na]+ was used for the calibration curves of fructose,

glucose, myo-inositol, mannitol, melibiose and raffinose. The calibration curves of sucrose and

stachyose were determined with the normalized area of the EIC of the ammonium adduct

[M+NH4]+. The range of concentrations is described in table 1.

RESULTS

79

Table 1. Analytical parameters of HPLC-ESI-TOF MS. Carbohydrates detected by HILIC-ESI-TOF MS in cacao

beans.

Carbohydrate Calibration curve Pearson coefficient

Linear working range (µg mL-1)

LOQ (ng mL-1)

LOD (ng mL-1)

Fructose y = 0.0587x + 0.0125 0.9920 0.4 – 8 50 16

Glucose y = 0.0344x + 0.0238 0.9926 0.4 – 10 200 66

myo-Inositol y = 0.0483x + 0.0261 0.9921 0.4 – 10 50 16

Mannitol y = 0.0441x - 0.0202 0.9904 0.6 – 8 200 66

Sucrose y = 0.0084x + 0.0092 0.9844 0.75 – 20 300 100

Melibiose y = 0.008x + 0.0052 0.9876 0.5 – 15 50 16

Raffinose y = 0.0256x - 0.007 0.9921 0.4 -10 150 50

Stachyose y = 0.0222x - 0.001 0.9738 1.0 – 20.0 200 66

Matrix effect for each carbohydrate was calculated per duplicate by the recovery obtained after

the addition of a specific amount of the standard of each carbohydrate (10 µg in the case of

fructose, glucose, myo-inositol and mannitol and 15 µg in the case of sucrose, melibiose,

raffinose and stachyose) to the analytical sample of cocoa bean obtained after SPE.

Chromatographic precision was calculated by means of the chromatographic repeatability or

intra-day precision. The chromatographic repeatability was determined as the average of the

RSD of the concentrations of main carbohydrates in cocoa beans obtained by multiple injections

(n=4) of two different cocoa samples performed the same day.

Limits of detection (LOD) and quantitation (LOQ) for the quantified carbohydrates were

calculated as three and ten times the standard deviation of the noise (σ), respectively.

The reproducibility of the entire method (extraction of the carbohydrates, preparation of the

sample by SPE and chromatographic separation) was evaluated by analyzing eighteen cocoa

samples performed per duplicate and three cocoa samples per triplicate.

2.7. Data processing and chemometric analysis

56 cocoa bean samples were used to perform the chemometric analysis. From these, 3 samples

were performed per triplicates, 18 samples performed per duplicates while the remaining 35

samples were performed by single extraction. The values of the peak areas for the different

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80

carbohydrates were extracted using MZmine2 [20] and double checked with the Data Analysis

4.0 (Bruker) software. The extracted areas for each of the different carbohydrates were first

normalized to the internal standard and subsequently to the measured sample weight.

PCA is frequently used in order to detect patterns in data, based on chemical variations observed

in large data sets. This unsupervised technique allows the visualization of underlying patterns,

by reduction of the data dimensionality, retaining as much as possible the information present

in the original data [21].

Partial least squares (PLS) analysis is a supervised regression method which attempts to extract

latent (or hidden) variables by maximizing the covariance between observed variables X and

response variables Y [22]. PLS discriminant analysis (PLS-DA) is an extension of PLS for

solving classification problems.

PLS-DAs and PCAs were performed using the R-package ropls [23]. In both cases, the data

were mean-centered and scaled to unit variance. The number of latent variables (LVs) to be

used in the PLS-DA models was chosen by 7-fold cross-validation of the respective data set.

The prediction error measure Q2 was used to validate PLS-DA models [24]. Furthermore, the

labels (i.e. classes) were permuted 100 times and Q2 was calculated for each of the

permutations. Subsequently, a one-sample t-test was performed to assess significant differences

between Q2 of the original data set and its permutations. The discriminant power of each

variable was evaluated by variable influence on projection (VIP) scores [25]. Variables with a

high VIP score (greater than one) are considered to be relevant for classification.

MANOVA is a procedure for testing the significant differences between mean values of two or

more response variables (the LMWC) within the evaluated groups (or factors) by comparing

their variances [26]. ANOVA is a statistical method for testing the significant differences

among group means by comparing their variances. The Student’s t-test is a statistical method

for comparing the means of two groups. Countries with a single sample (Brazil and Tanzania)

RESULTS

81

were excluded from the ANOVA analysis. MANOVA, ANOVA and t-test were calculated

using the corresponding functions from R version 3.3.2.

3. RESULTS AND DISCUSSION

3.1. Qualitative analysis

The method developed constitutes a suitable combination of extraction, enrichment, and

chromatographic separation that allows full characterization of the carbohydrate composition

of cocoa beans. Structure assignment of the different carbohydrates was achieved using high

resolution MS data obtained by HILIC-ESI-TOF-MS and fragmentation spectra obtained by

HILIC-ESI-MSn. Tentative identification of the type of glycosidic linkage between the units of

monosaccharides of unknown disaccharides and trisaccharides has been proposed. This

identification was based on the observation that, similar fragmentation profiles, but different

relative ratios of the fragment ions had been previously reported for disaccharides with the same

linkage but different monosaccharide residues [27-30]. Table S1.1 shows MS2 and MS3 data

from the different fragments ions observed from the different carbohydrates detected in cocoa

beans.

Figure 1 shows a representative chromatogram of the carbohydrate profile of unfermented

cocoa beans in positive ion mode. Main carbohydrates in cocoa beans (unfermented and

fermented) as fructose (peak 1), glucose (peak 3), sucrose (peak 6), myo-inositol (peak 10),

raffinose (peak 17) and stachyose (peak 23) and minor carbohydrates as mannitol (peak 2) and

melibiose (peak 13) were identified by comparing the retention time and MS data with those of

commercial standards.

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Figure 1. HILIC-ESI-TOF MS chromatogram of unfermented sample from Brazil in positive ion mode. 1.

Fructose, 3. Glucose, 4. Pentosyl-iminosugar (13.4), 5. Pentosyl-iminosugar (14.3), 6. Sucrose, 7, Alcohol of

tri-pentose, 8. Iminosugar, 9. Disaccharide (19.4), 10. myo-inositol, 11. Alcohol of disaccharide, 12.

Trisaccharide (22), 13. Melibiose, 14. Trisaccharide (23.2), 15. Dihexosyl glicerol, 16. Trisaccharide (24.8),

17. Raffinose, 18. Disaccharide (26.7), 19. Trisaccharide (26.8), 20. Trisaccharide (27.4), 21. Disaccharide

(28.5), 22. Trisaccharide (32.1), 23. Stachyose.

The compounds characterized are summarized in Table S1.2, including retention time,

molecular formulae, label name used for chemometric analysis in the case of isomers,

experimental m/z and error ppm value (calculated as the average value of three samples). No

further peaks corresponding to the sodium adduct of the above mentioned monosaccharides

([M+Na]+, C6H12O6Na) were detected. The presence of galactose could not be confirmed due

to the coelution of the analytical standard with glucose. No further peaks corresponding to the

sodium adduct of monosaccharides ([M+Na]+, C6H12O6Na) were detected.

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83

Peaks 9, 18 and 21 were assigned as disaccharides according to the molecular formula of the

sodium adduct, ([M+Na]+, C12H22O11Na) and their pattern of fragmentation. A glycosidic

linkage 1→1 between the monosaccharide units of the disaccharides structures could be

tentatively attributed due to their base peak of m/z 203 at MS2 [27]. The others fragment ions

detected at MS2 could be attributed to a different monosaccharide composition from the

composition studied by Zhang, Brokman, Fang, Pohl and Yeung [27].

Six peaks (peaks 12, 16, 14, 19, 20 and 22) were identified as trisaccharides according to the

molecular formula of the sodium adduct ([M + Na]+, C18H32O16Na) and their fragment spectra.

Manninotriose, described in roasted cocoa beans by Thin-Layer Chromatography [31], could

not be evaluated due to the lack of the standard. The pattern of fragmentation of the peaks 12

and 16 displayed two consecutive neutral losses of 162 Da in MS2 and MS3, suggesting a

tentative 1→1 glycosidic linkage between the units of the monosaccharides of the trisaccharide

structure [27]. Different minor fragments in MS3 were observed between peak 12 (m/z ions 275,

305 and 347) and peak 16 (m/z ion 245,275,305), suggesting different composition in at least

one of the monosaccharides of each trisaccharide.

Peaks 14 and 19 showed a base peak of m/z 347 (100%) in MS2, compatible with the

consecutive neutral loss of 162 Da and 18 Da, and a base peak of m/z 365 (80%) compatible

with the loss of m/z 162. MS3 showed a base peak of 185, compatible with the loss of a hexosyl

group (162 Da). This fragmentation pattern does not follow the fragmentation pattern of a

glycosidic linkage type 1→X (X=1,2,3,4,6 position) showed by Zhang, Brokman, Fang, Pohl

and Yeung [27] and Hernández-Hernández, Calvillo, Lebrón-Aguilar, Moreno and Sanz [28].

Peak 20 showed a base peak m/z 365 at MS2 (loss of 162 Da), suggesting a tentative glycosidic

linkage type 1→1 in the structure of the trisaccharide. However, the base peak m/z 275 (neutral

loss of 90 Da) at MS3 and the presence of minor fragments (table S1.1) do not allow to

tentatively assign the remaining glycosidic linkage of the trisaccharide structure according to

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84

the data showed by Hernández-Hernández, Calvillo, Lebrón-Aguilar, Moreno and Sanz [28]

and Zhang, Brokman, Fang, Pohl and Yeung [27]. In the case of the peak 22, the fragmentation

pattern of neutral loss of 162 Da at MS2 and a base peak of m/z 305 at MS3 (neutral loss of 60

Da) with the presence of the fragments ions m/z 275, 203 and 245 suggests a tentative glycosidic

linkage between the units of monosaccharide (M) of the type M-(1→1)-M-(1→6)-M [112,

113].

In contrast to the previous publication [31], tetrasaccharides other than stachyose or

oligosaccharides of higher molecular formula were not detected.

Peaks 7, 11 and 15 were identified as alcohol of carbohydrates. Peak 7 was identified as an

alcohol of tripentose. The molecular formulae of peak 7 were C15H28NaO13 and C15H27O13 in

positive and negative ion mode respectively. MS2 in positive and negative ion mode showed a

neutral loss of 132 Da corresponding to the loss of a pentosyl group. Also, MS2 in negative ion

mode showed a neutral loss of 18 Da corresponding to a loss of water and 266 Da which might

correspond to a loss of an alcohol of di-pentose. MS3 in negative ion mode displayed the neutral

loss of 18 Da corresponding to water and 60 Da corresponding to C2H4O2.

Peak 11 was identified as alcohol of a disaccharide. The molecular formula of the peak 11 was

C12H24NaO11 in positive ion mode. MS2 in positive ion mode showed a neutral loss of m/z 60

(C2H4O2) and m/z 90 (C3H6O3). Peak 15 was identified as dihexosyl glycerol considering the

molecular formula C15H28NaO13 and the two consecutive neutral loss of 162 Da (hexosyl group)

in MS2 and MS3. Other alcohols of pentose previously reported in cocoa beans, such as pentitol,

were not detected [14].

Compounds with masses compatible with the quasi-molecular ion of iminosugars [M+H]+ were

detected (peaks 4, 5 and 8). Peaks 4 and 5, with a molecular formula of C11H14NO6 in positive

ion mode, showed in MS2 a neutral loss of 132 Da, which might correspond to the loss of a

pentosyl group. These compounds were not detected in negative ion mode. According to

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85

available literature [32] these two compounds could be tentatively identified as pentosyl-

iminosugars.

The molecular formula of the peak 8 in positive ion mode was C12H16NO7. MS2 in positive ion

mode showed a base peak of m/z 252.1, the neutral loss of 18 Da, which corresponds to a loss

of a water molecule and the neutral loss of 162 Da, which might correspond to a glycosil group.

This compound was not detected in negative ion mode. Taking into account all data, this

compound could be tentatively identified as glycosyl-iminosugar [32]. This is the first

manuscript reporting iminosugars in cocoa beans.

3.2. Quantitative analysis

3.2.1. Lipids and dry matter in cocoa beans

The composition of lipids in unfermented cocoa beans was in the range of 26.6 to 41.5%. In

spontaneously fermented cocoa beans, the range of lipids was 45.7 to 58.0 % and in cocoa beans

fermented by OF procedure showed a lipid content in the range of 49.5 to 57%.

The dry matter content in unfermented cocoa beans was in the range of 54.1 to 71.3%. All

fermented beans, independently of the procedure employed, were dried for a period of seven to

ten days. For this reason, no differences were observed for spontaneously fermented beans

(range 84.8-94.8%) and samples fermented by OF procedure of pre-drying (range 93.9-94.6%)

or controlled fermentation (range 94.4-94.8%).

3.2.2. HPLC analysis.

3.2.2.1. Analytical parameters/Validation

Once the carbohydrate profile was characterized, the HILIC-ESI-TOF-MS proposed method

was assessed for its suitability in carbohydrate quantification of cocoa beans. To perform the

assessment, first the matrix effect was evaluated. Using reference standards of fructose, glucose,

mannitol, myo-inositol, sucrose, melibiose, raffinose and stachyose, the corresponding

calibration curves were obtained with satisfactory Pearson R2 values and a suitable linear

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86

working range. The recovery values (94-106%) obtained for each carbohydrate suggest that

matrix effects had little to no influence on the quantitative analysis. Recovery values obtained

for each compound are available in Table S2.1 (supplementary information).

LOD and LOQ values determined for fructose, myo-inositol and melibiose (16 and 50 ng mL−1,

respectively) were low, whereas higher values were observed for sucrose (100 and 300 ng mL−1,

respectively). Suitable values of intra-day precision were observed for all carbohydrates under

study (values of RSD lower than 7.0%). Table1 shows the analytical parameters of the method

of analysis (calibration curve, Pearson coefficient, linear working range, LOD and LOQ). .

According to the values of the A.R. for each carbohydrate, excellent efficiencies of SPE

purification were observed for fructose (92%), myo-inositol (84%), mannitol (83%), sucrose

(91%), melibiose (89%), raffinose (94%) and stachyose (97%). Acceptable values of

purification efficiency were determined for glucose (73%). The values of purification efficiency

for fructose and myo-inositol were in agreement with the values reported by other authors for

monosaccharides [33,34], while the value of purification efficiency for glucose was slightly

lower.

The reproducibility of the method (extraction of carbohydrates, sample preparation by SPE and

chromatographic separation) was also evaluated as the average of RSD of the main

carbohydrates in cocoa beans of the different samples analyzed per duplicate (n=18) and

triplicate (n=3). Average values of RSD of the main carbohydrates were in the range of 5.2-

7.9% and 3.8-14.6% for the cocoa samples analyzed per duplicate and triplicate of sample

preparation. RSD values obtained for each compound are available in the supplementary

information (Table S2.2a and S2.2b).

These data, together with the absence of matrix effect, indicated the suitability of the method

for the determination of carbohydrates in cocoa beans.

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87

3.2.2.2. Carbohydrate composition in cocoa beans.

No differences in the global content of fructose were observed between unfermented and

fermented beans. However, differences among the mean content of glucose in unfermented

(74.5 mg/100 g DM, range 4.3-175.0), spontaneously (28.3 mg/100 g DM, range 0-104.8) and

OF fermented beans (48.1 mg/100 g DM, range 23.7-59.8) were observed.

The average content of myo-inositol in unfermented beans (53.1 mg/100 g DM, range 7.7-

121.2) was higher than the values observed in spontaneously fermented (31.9 mg/100 g DM,

range 0-84.7) and OF fermented beans (35.3 mg/100 g DM, range 0-80.0).

Table 2 shows values of fructose, glucose and myo-inositol, which were in the same order of

magnitud if compared to other authors [16,33] ).

In unfermented beans, sucrose (average value of 1165.7 mg/100 g DM, range 230-4086.8), was

the main carbohydrate in all cases, followed by raffinose and stachyose, which showed an

average value of 399.7 mg/100 g DM (range 66-1392.6) and 211.4 mg/100 g DM (range 41.3-

377.4) respectively. Values of sucrose were in accordance with values described elsewhere

[35]. The wide variability in the content of sucrose, raffinose, and stachyose could be explained

by the diversity of the samples under study, including different hybrids and collection places.

In fermented beans, lower average content was determined for sucrose, raffinose and stachyose

with respect to the value of unfermented beans, which showed values of 57.1 mg/100 g DM

(range 0-212.3), 17.1 mg/100 g DM (0-36.1) and 34.8 mg/100 g DM (range 4.6-80.4)

respectively. These data agree with the values reported previously [35]. The content of these

carbohydrates in OF fermented cocoa (n=7) was superior to the values determined

inspontaneously fermented beans, suggesting that this procedure produces less degradation of

sucrose and oligosaccharides (table 2).

Melibiose and mannitol are minor carbohydrates present in cocoa. Melibiose was not detected

in all samples. The average content of melibiose in unfermented beans (44.5 mg/100 g DM,

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88

range 0-308.3) was higher than the content determined in spontaneously (7.4 mg/100 g DM,

range 0-43.7) and OF fermented beans (27.5 mg/ 100 g DM, range 0-75.6).

Mannitol was detected only in fermented beans. Differences in the average content of mannitol

were observed between spontaneously (30.9 mg /100 g DM, range 5.8-105) and OF fermented

(8.5 mg/100 g DM, range 5.8-13.6) cocoa beans.Values of mannitol in spontaneously fermented

beans were in the same order of reported values, 40 mg/100g [136]. The reason for the presence

of mannitol only in fermented beans has not been elucidated. In cocoa pulp, the formation of

mannitol has been attributed to the reduction of fructose to mannitol by Lactobacillus

fermentum [36]. The differences in the content of mannitol observed in fermented beans

obtained by different procedures could be attributed to the differences in the microbiota

involved in the fermentation procedure or to the different conditions (pH and temperature) of

each fermentation procedure that could produce the reduction of fructose to mannitol.

In unfermented beans, the average total carbohydrate content determined ( 2g/100 g DM, range

0.9-4.9) was in the same order than the content described elsewhere [37]. The mean total

carbohydrate content in spontaneously (0.3 g/100g DM, range 0.1-0.5) and OF fermented beans

(0.4g/100g DM, range 0.2-0.5) were 6.6 and 5 time less the value determined in unfermented

beans.

According to the average total carbohydrate content, Malaysia and Ivory Coast were the

countries with the highest content determined in unfermented and fermented beans respectively.

The countries with the lowest content determined in unfermented beans were Ecuador and

Brazil and for fermented beans were Indonesia, Brazil and Tanzania.

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Table 2. Mean and range values of each carbohydrate per country, status of fermentation (U: unfermented and F: fermented spontaneously) and procedure of fermentation

(CF: Controlled fermentation, PD: pre-drying beans before fermentation). n.d.: not detected

Carbohydrate

mg/100 g DM g/ 100 g DM

Fructose Glucose Myo-inositol Mannitol Sucrose Melibiose Raffinose Stachyose Total

Mean Range Mean Range Mean Range Mean Range Mean Range Mean Range Mean Range Mean Range Mean Range

Ivory Coast U ( n = 8) 38.5 14.8 - 86.3 33.8 4.3 - 75.2 59.4 7.7 - 121.2 n.d. - 1180.6 230 - 2634.6 4.9 0 - 22.7 339.5 114.9 - 531.5 116.5 79.9 - 172.3 1.8 0.9 - 3.1

F (n = 6) 172.3 34.5 - 296.4 57.0 16.2 - 104.8 53.4 20.7 - 84.7 54.6 28.1 - 105 58.2 26.8 - 151.7 0 - 20.2 15.1 - 24.1 26.6 17.8 - 45.4 0.4 0.2 - 0.5

Malaysia U (n = 3) 12.3 10.0 - 15.6 29.0 24.2 - 37.5 30.8 24.0 - 38.7 n.d. - 2887.0 1433.2 - 4086.8 11.8 0 - 35.5 622.5 541.2 - 782.6 244.5 158.7 - 325.9 3.8 2.4 - 4.9

F (n = 3) 24.8 21.3 - 27.6 7.7 4.1 - 11.1 12.7 8.2 - 15.7 21.7 17.8 - 23.9 137.9 59.3 - 212.3 0 - 11.7 8.5 - 14.3 37.7 25.2 - 47.0 0.3 0.2 - 0.3

Indonesia U (n = 4) 41.3 32 - 66.6 127.3 81.5 - 173.7 47.6 24.4 - 61.9 n.d. - 1203.1 464.4 - 1851.3 197.6 102.7 - 308.4 571.6 66.0 - 1392.6 266.6 41.3 - 366.3 2.5 1.4 - 3.6

F (n = 7) 25.6 10.3 - 36.9 6.1 0 - 34.5 14.2 0.0 - 29.2 30.6 15.7 - 55.9 36.6 0.8 - 119.3 4.4 0 - 18.3 14.5 0 - 29.8 36.2 4.6 - 80.4 0.2 0.1 - 0.3

Brazil U (n = 1) 69.1 - 94.1 - 85.4 - n.d. - 265.0 - 28.4 - 149.7 - 161.4 - 0.9 -

F (n = 1) 45.5 - 36.7 - 46.7 - 8.5 - 0.0 - 27 - 21.5 - 27.7 - 0.2 -

Tanzania U (n =1) 49.2 - 103.6 - 55.1 - n.d. - 1044.6 - 114.3 - 396.8 - 351.0 - 2.1 -

F (n = 1) 18.7 - 1.4 - 10.4 - 32.2 - 39.8 - 0.1 - 9.7 - 40.0 - 0.2 -

CF (n = 3) 36.3 22.9 - 53.4 44.7 23.7 - 57.7 24.9 13.4 - 37.0 6.7 5.8 - 8.3 144.0 104.3 - 184.5 11.6 0 - 31.3 43.5 27.3 - 62.1 66.1 40.6 - 82.5 0.4 0.3 - 0.4

Ecuador U (n =7) 65.7 21.6 - 103.4 103.3 38.5 - 175 53.8 32.9 - 74.0 n.d. - 535.4 266.3 - 1037.1 8.6 0 - 44.1 310.9 117.2 - 762.5 261.4 179.3 - 377.4 1.3 0.9 - 1.8

F (n = 7) 41.3 24.0 - 55.5 37.4 15.6 - 76.7 40.4 13.2 - 78.4 18.0 5.8 - 44.3 52.7 39.0 - 106.0 18.3 0 - 43.7 19.7 11.4 - 36.2 39.5 13.5 - 60.9 0.3 0.2 - 0.4

PD (n = 4) 49.9 27.3 - 64.5 50.7 34.0 - 59.8 43.2 0 - 80.0 9.9 6.9 - 13.6 87.2 37.5 - 164.1 39.3 14.9 - 75.6 35.8 2.0 - 48.8 79.0 41.4 - 160.0 0.4 0.2 - 0.5

Global

Content

U ( n = 24) 45.3 10.0 - 103.4 74.5 4.3 - 175.0 53.1 7.7 - 121.2 n.d. - 1165.7 230.0 - 4086.8 44.5 0 - 308.3 399.7 66.6 - 1392.6 211.4 41.3 - 377.4 2.0 0.9 - 4.9

F (n = 25) 65.7 10.3 - 296.4 28.3 0 - 104.8 31.9 0 - 84.7 30.9 5.8 - 105.0 57.1 0 - 212.3 7.4 0 - 43.7 17.1 0 - 36.1 34.8 4.6 - 80.4 0.3 0.1 - 0.5

OF (n = 7) 44.1 22.9 - 64.5 48.1 23.7 - 59.8 35.3 0 - 80.0 8.5 5.8 - 13.6 111.5 37.5 - 184.5 27.5 0 - 75.6 39.1 2.0 - 62.1 73.5 40.6 - 160.0 0.4 0.2 - 0.5

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3.3. Chemometric analysis

3.3.1. Classification of beans according to the status of fermentation

PCA was employed to evaluate sample clustering according to their fermentation status. A clear

separation between fermented and unfermented samples was observed in the PCA score plot

(Figure 2). The variance explained by the first component of PCA was 33.84%, while the

second component explained 21.84%. The first component could be considered as mainly

demarcating unfermented and fermented samples.

The carbohydrates stachyose, raffinose, sucrose, disaccharide (28.5), disaccharide (19.4),

dihexosyl glycerol, disaccharide (26.7) and melibiose have negative loadings values with

respect to the first principal component on the loading plot, indicating that these carbohydrates

are putative markers of unfermented cocoa beans. On the other hand, mannitol, which is entirely

absent in unfermented samples, and glycosyl-iminosugar, which shows relatively high

intensities in fermented samples, have positive loading values with respect to the first principal

component. Therefore, these compounds could be assigned as candidate markers of fermented

cocoa beans.

The differences observed with PCA analysis of all dataset were corroborated with the extremely

low p-value (<2.10-16) obtained from a one-way MANOVA using the bean type

(unfermented/fermented) as a factor.

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Figure 2. PCA score (left) and loading plot (right) from LC-MS data of carbohydrate extracts of all cocoa bean

samples. Fermented (red) and unfermented beans (green) are clearly separated. Carbohydrates located on the far

left or right in the loading plot are indicative for unfermented or fermented beans, respectively.

PLS-DA was used to establish a statistical model for the classification of cocoa beans according

to the status of fermentation. The results of PLS-DA on the entire dataset can be seen in Figure

S3.1 (supplementary information). The number of latent variables in the model was set to 4

using cross-validation (prediction error measure Q2=0.87). Fermented beans can be clearly

distinguished from unfermented beans with a sensitivity and specificity of 100% (no

misclassified sample). A permutation test confirmed the high significance of the PLS-DA

model (p-value < 10-16). According to the VIP scores, mannitol and glycosyl-iminosugar are

suitable biomarkers for fermented beans. Sucrose, raffinose, and, to a lesser extent,

disaccharides (19.4 and 28.5) and dihexosyl glycerol are indicators for unfermented beans.

3.3.2. Classification of unfermented/spontaneously fermented cocoa beans with respect to

their origin

Further PCA analyses were carried out on HILIC-ESI-TOF-MS data from spontaneously

fermented and unfermented cocoa beans separately to identify carbohydrates as indicators of

the country of origin.

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PCA of unfermented beans (figure S3.2, supplementary information) showed that cocoa beans

from Malaysia were clearly separated from the rest of the cocoa samples. The samples from

Ecuador fell into two subgroups. The loading plot indicates that one of the Ecuadorian

subgroups was characterized by high intensities of fructose and glucose. Certain trend of

separation of Ivory Coast samples from the group of Ecuador was observed. The variance

explained by the first and second principal component was 32.96 % and 17.23%, respectively.

PCA of spontaneously fermented beans shows a perceptible trend of separation of the samples

from Ivory Coast and Indonesia/Malaysia from the rest of the groups (Figure S3.3,

supplementary information). The variance explained by the first and second principal

component was 41.33 % and 14.31%, respectively.

MANOVA test using the countries as factor revealed differences in the sample means for

unfermented beans (p-value 0.043) and fermented beans (p-value 0.0002). Table S3.5 and S3.6

show which carbohydrates had significant differences among countries using an ANOVA test.

According to this data, the LMWC profile (unfermented and spontaneously fermented) suggest

differences between countries, however, indicators to discern cocoa beans depending on a

specific origin could not be proposed.

3.3.3. Classification of fermented samples according to the fermentation procedure.

A visible separation according to the different process of fermentation performed was observed

from the corresponding PCA score plot restricted to fermented samples (Figure S3.4,

supplementary information). The variance explained by the first and second principal

component was 41.33% and 14.31%, respectively. Samples tend to separate between

spontaneous and OF procedure (controlled fermentation and pre-drying the beans before

fermentation).

The carbohydrates with significant differences between fermentation procedures, using a t-test,

are shown in the Table S3.7 (supplementary information). Mannitol and glycosyl-iminosugar

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93

could be considered as markers of spontaneous fermentation procedure due to the elevated

normalized area showed in spontaneously fermented cocoa beans. These carbohydrates

coincide with the information obtained from the loadings plots. The rest of carbohydrates from

the Table S3.7 could be considered as markers of OF procedure due to the higher normalized

area detected in the loading plots of fermented cocoa beans obtained by this method of

fermentation (Figure S3.4).

The fact that the LMWC profile of cocoa beans fermented by OF procedure is similar to the

profile of unfermented cocoa beans suggests that the OF procedure might lead to incomplete

fermentation of cocoa beans. The incomplete fermentation in samples from Ecuador could be

due to the drying effect on the pulp, which could produce a decreased yeast population in

comparison to spontaneous fermentation. In the case of controlled fermentation, the

introduction of an exogenous yeast booster into the pulp and beans could alter the biochemistry

of fermentation.

PLS-DA was used to classify fermented cocoa beans according to the procedure of

fermentation. Figure 3 shows the PLS-DA model on the subset of fermented beans. Using cross-

validation (Q2=0.68) the number of latent variables was set to 2. The sensitivity and specificity

of predicting the fermentation type was 100% and 94.5%, respectively. A permutation test

confirmed the high significance of the PLS-DA model (p-value < 10-16).

As in the case of classifying the fermentation status, sucrose, and raffinose have the highest

VIP scores and are therefore considered to be also indicative of the fermentation procedure. In

other words, high intensities of these two carbohydrates can be found in either unfermented

beans or in beans that were not spontaneously fermented. Glycosyl-iminosugar and mannitol

also showed a high VIP score, what is in good accordance with their role as putative

fermentation status markers mentioned previously.

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Figure 3 (LEFT) Scores plot of PLS-DA based on the subset of fermented beans. Spontaneously fermented beans

are colored in red, while beans from other types of fermentation are shown in blue. (RIGHT) Corresponding

weight plot. Colors denote VIP scores of each variable, which indicate their discrimination power.

3.3.4. Evaluation of the profile of LMWC according to the duration of fermentation.

The duration of fermentation has consequences on the enzymatic processes involved in the

fermentation. In function of the number of days of fermentation, cocoa beans can be classified

as under-fermented, fully-fermented and over-fermented. Cocoa beans fermented for a period

shorter than 5 days have been classified as under-fermented, characterized by the incomplete

degradation of the proteins [9]. A fermentation period of five-to-six days is considered as

optimal for the production of flavor precursors (fully fermented cocoa beans). Cocoa beans

fermented for more than six days are considered as over-fermented beans, characterized by a

hammy off-flavor [2], darker appearence [37], the absence or low content of protein and high

peptide content.

All fermented samples under study were obtained through different fermentation procedures

with a variable duration, from four to seven days. This information was not available for three

samples.

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95

In order to determine if the profile of the LMWC in fermented beans (spontaneously and OF

fermented beans), is different depending on the length of fermentation of cocoa, a MANOVA

test was performed using the number of days of fermentation as a factor. The result, p-value of

0.0007, suggests that there are differences in the profile of LMWC among the different days.

Performing ANOVA, using as a factor the duration of fermentation, revealed that the

carbohydrates fructose, mannitol, sucrose, disaccharide (26.7), disaccharide (19.4), melibiose,

raffinose, trisaccharide (32.1) and stachyose showed significant differences depending on the

number of days of fermentation (Table S3.8 shows the p-values of these carbohydrates). Figure

4 shows the normalized area of the carbohydrates with significant differences depending on the

number of days of fermentation.

Cocoa beans fermented for four days are characterized by an intermediate normalized area of

sucrose, raffinose and stachyose as well as by the presence of melibiose, disaccharide (26.7)

and disaccharide (19.4). This profile has similarity with the profile observed in unfermented

beans, suggesting that the beans are under-fermented. However, mannitol, described as an

indicator of fermentation in this work, was detected in these samples, suggesting that the

fermentation procedure has already started.

The LMWC profile of cocoa beans fermented for five and six days has differences with the

profile determined for unfermented beans. This profile, characterized by low normalized areas

of sucrose, raffinose, stachyose and the practical absence of disaccharide (19.4) and

disaccharide (26.7), suggests that the cocoa beans are fully fermented.

Cocoa beans fermented for seven days are characterized by a higher normalized area of

mannitol and fructose and the absence of melibiose, disaccharide (19.4) and disaccharide (26.7).

This profile suggests that the longer fermentation procedure, with its acidic pH value and

elevated temperature, produces degradation of disaccharides and oligosaccharides.

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The constant presence of the trisaccharide (32.1) in the fermented samples suggests that this

carbohydrate could be a product of the degradation of polysaccharides of cocoa beans or a

product of the enzymatic reactions during the fermentation.

4. CONCLUSION

An analytical method was validated for the analysis of carbohydrates in cocoa beans. The

method involves a solid-liquid extraction followed by a step of purification of carbohydrates

by SPE using an amino cartridge phase, with high efficiency in the purification of disaccharides

and oligosaccharides.

A comprehensive characterization of the carbohydrate composition in cocoa beans was

performed using HILIC-ESI-TOF-MS and HILIC-ESI-MSn .

The composition of the main carbohydrates of cocoa beans from different status of

fermentation, subjected to different fermentation processes and collected from different

locations is now reported for the first time in an elevated number of samples.

Unfermented cocoa beans were characterized by higher concentrations of sucrose, raffinose,

and stachyose. Spontaneously fermented cocoa beans were defined by the presence of mannitol

and lower concentrations of disaccharides and oligosaccharides. The LMWC profile of cocoa

beans fermented by OF procedure differs from the profile of spontaneously fermented beans in

their elevated content of disaccharides and oligosaccharides and lower levels of mannitol.

Interestingly, this profile has similarities with the profile of unfermented cocoa beans,

indicating that the OF procedure leads to an incomplete fermentation of the cocoa beans.

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97

Figure 4. Boxplot of the several sugars fructose, mannitol, sucrose, disaccharide (26.7), disaccharide (19.4),

melibiose, raffinose, trisaccharide (32.1) and stachyose. Unfermented beans (n=24), Fermented beans 4 days

(n=8), 5 days (n=6), 6 days (n= 11) and 7 days (n=4). Three fermented samples were not consider for the absence

of data of the length of fermentation. The group of fermented beans of 4 days group samples spontaneously

fermented (n=1) and OF fermented (n=7).

The chemometric analysis reveals that the carbohydrate profile captures information about the

fermentation status, fermentation procedure performed and the number of days of fermentation

of the cocoa bean. Disaccharides (sucrose, melibiose, disaccharide (19.4), and disaccharide

(26.7)), raffinose and stachyose can be considered as indicators of the unfermented status of

cocoa beans. In fermented samples, elevated MS signal of disaccharides, raffinose and

stachyose suggests incomplety fermentation of cocoa beans either due to the fermentation

procedure (OF procedure) or the shorter period of fermentation procedure. Additionally,

mannitol is a clear indicator of the fermentation status of cocoa beans. It is clearly observable

CHAPTER 4

98

how the MS signal of this carbohydrate is elevated in samples fermented for longer periods of

time.

A useful classification of the cocoa beans according to the fermentation status and type of

procedure of fermentation was successfully achieved by PLS-DA.

The results of this study demonstrate remarkable diversity in the profile of the carbohydrates of

unfermented and fermented cocoa beans. In conjunction with other factors, this could be one of

the factors involved in the diversity of cocoa flavor from different origins.

RESULTS

99

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Chapter 5. Analysis of minor low molecular weight carbohydrates in cocoa beans

by chromatographic techniques coupled to mass spectrometry

Roberto Megías-Pérez, Ana Isabel Ruiz-Matute, Marcello Corno, Nikolai Kuhnert

Manuscript published in Journal of Chromatography A,Volume 1584, 11 January 2019, Pages

135-143 https://doi.org/10.1016/j.chroma.2018.11.033

https://doi.org/10.1016/j.chroma.2018.11.033

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ABSTRACT

The low molecular weight carbohydrate (LMWC) profile of cocoa beans has recently been

studied using hydrophilic interaction liquid chromatography coupled to electrospray ionization-

time of flight mass spectrometry (HILIC-ESI-TOF MS) and HILIC-ESI-tandem mass

spectrometry (HILIC-ESI-MSn). However, different LMWC could not be unambiguously

identified. Thus, as a first approach in this paper, gas chromatography coupled to mass

spectrometry (GC-MS) was used as a complementary analytical technique to characterize

LMWC of cocoa beans. Different mono-, di-, tri- and tetrasaccharides, as well as myo-inositol,

galactinol and a diglycosil glycerol were detected. scyllo-Inositol, 1-kestose and 6-kestose were

identified in unfermented cocoa beans for the first time. Moreover, other minor LMWC were

tentatively assigned as fructosyl-fructose, fructosyl-glucose and glucosyl-sucrose. As a second

step, in order to evaluate new possible indicators of cocoa bean origin or fermentation status,

scyllo-inositol, 1-kestose and galactinol were selected as target compounds and a HILIC-ESI-

TOF MS method was optimized for their analysis. The optimized conditions, using an

acetonitrile:water gradient with 0.05% ammonium hydroxide at 40°C showed narrow peaks

(wh: 0.3-0.5 min) with good resolution values (Rs: 0.83–2.83). The validated HILIC-ESI-TOF

MS method was applied to the analysis of 35 cocoa bean samples from different origins and

fermentation status. The content of scyllo-inositol, 1-kestose and galactinol in unfermented

beans (n=21) was in the range of traces-504.9, 36.1- 133.5 and traces-1970.4 µg g-1 cocoa DM

respectively. In fermented beans (n=14), the content of scyllo-inositol and 1-kestose was in the

range of 15.5-491.9 and traces-115.5 µg g-1 cocoa DM respectively. Galactinol was absent in

fermented beans, indicating that it could be a potential indicator of fermentation status. The

methodology proposed could be used for quality control of natural products and other food

ingredients containing inositols and oligosaccharides.

Keywords: scyllo-inositol, 1-kestose, galactinol, cocoa bean, HILIC-ESI-TOF MS, GC-MS.

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1. INTRODUCTION

Cocoa beans, the seeds of the cocoa tree Theobroma cacao L., are used as raw material in the

manufacturing of chocolate and other derived products. They constitute the principal

agricultural export commodity for the producing countries and they are also the main source of

income for about 6 million smallholders around the world. [1, 2].

In recent years, the study of cocoa beans has received much attention from the scientific

community. Different research lines have been focused on the characterization of unknown

compounds[3], the search for indicators of bean origin or status of fermentation [4], the

evaluation of quality parameters to distinguish among hybrid cultivars [5] and the development

of a sustainable production of high-quality beans and cocoa products [6], among others. Most

of these researches are based on the study of polyphenols [7,8], volatile compounds [9], lipids

[10] or proteins/peptides [11] in cocoa beans and their evolution during fermentation processes

and roasting. However, despite the significant role of carbohydrates in the Maillard reaction,

which takes place during cocoa bean roasting [12], few studies have been reported regarding

low molecular weight carbohydrates (LMWC) in cocoa beans.

Among the different analytical techniques that can be used for carbohydrate analysis, gas

chromatography (GC) and liquid chromatography (LC) coupled to mass spectrometry (MS) are

the most extensively employed [13].

GC-MS has been widely used for the analysis of LWMC due to its high-resolution power,

sensitivity and potential for structural identification. In GC-MS, the combination of GC

retention times (or retention indices) and specific electron impact ionization (EI) mass spectra

of derivatives provide valuable information about the chemical structure of a molecule. This

information is specifically useful for complex mixtures of carbohydrate isomers, where

compounds with the same molecular weight differ only in the configuration of their hydroxyl

groups and the position of their glycosidic linkages [14]. GC-MS has been applied for the

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structural characterization of different types of bioactive carbohydrates such as inositols [15],

cyclitol glycosides [16], iminosugars [17] or trisaccharides [18]. However, the main

disadvantage of the analysis of LMWC using GC-MS is that a previous derivatization step is

required [19], which can be a tedious and expensive task for routine analysis.

Conversely, in LC-MS the sample preparation for carbohydrate analysis is usually much

simpler and does not require a lengthy time, which is an advantage over GC. Among the

different operation modes, hydrophilic interaction liquid chromatography (HILIC) is

considered an efficient alternative to reverse-phase liquid chromatography for the analysis of

complex oligosaccharide mixtures. This technique provides appropriate resolution between

isomers and good peak shapes [20]. Furthermore, HILIC is easily coupled to MS due to the

mobile phases used, which consist of water mixtures with a high percentage of organic solvents,

enhancing the ionization and increasing sensitivity [20]. Successful application of HILIC-MS

to the analysis of carbohydrates from different matrices can be found in the literature, such as

the analysis of iminosugars in plants [21] or oligosaccharides in milk [20], among others.

However, applications of this technique in the analysis of cocoa bean LMWC are very scarce.

We have recently developed a methodology based on HILIC-electrospray ionization-time of

flight MS (HILIC-ESI-TOF MS) and HILIC-ESI-tandem mass spectrometry (HILIC-ESI-MSn)

to monitor changes in cocoa bean LMWC according to their origin and fermentation status [22].

That study has demonstrated the suitability of the carbohydrate profile as an indicator of

fermentation status, fermentation procedure and duration of fermentation. Monosaccharides

(fructose, glucose), polyols (myo-inositol and mannitol), disaccharides (sucrose and melibiose),

trisaccharides (raffinose) and tetrasaccharides (stachyose) were detected. Furthermore, the

presence of other minor LMWC, which could not be unambiguously identified, was reported.

This lack of assignment of identities is mainly a consequence of the inability to identify co-

eluting peaks due to the non-specific fragmentation spectra of the carbohydrates.

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Consequently, the present paper aims to develop a new methodology which allows to

investigate the content of unknown minor LMWC detected in cocoa beans. GC-MS has been

used to complement the structural information previously reported using HILIC-ESI-MSn. An

appropriate methodology using HILIC-ESI-TOF MS has been developed and validated to

analyze the selected target LMWC in different types of cocoa bean samples and therefore, to

evaluate these LMWC as putative potential indicators of origin or fermentation status.


2.1. Chemicals and standards

Dichloromethane and LC-MS grade acetonitrile (ACN) were supplied by Aplichem Panreac

(Darmstadt, Germany). Ammonium hydroxide solutions, Asp-Phe methyl ester (used as

internal standard in HILIC-ESI-TOF MS analysis), fructose, glucose, galactose, maltose,

melibiose, myo-inositol, phenyl-β-D-glucoside (used as internal standard in GC-MS analysis),

sucrose, raffinose and stachyose were obtained from Sigma Chemical Co. (St. Louis, USA).

Scyllo-inositol, 1-kestose and galactinol were purchased at Carbosynth (Compton, UK).


Cocoa bean samples (n = 35) were provided and certified with regard to their location and status

of fermentation by Barry Callebaut Belgium (Table S1, supplementary information).

Unfermented cocoa beans (n = 21) were collected from different geographical origins: Ecuador

(n = 5), Malaysia (n = 3), Brazil (n = 3), Indonesia (n = 4) and Ivory Coast (n = 6). Unfermented

samples were kept at -80°C from the collection time until the beans were processed for analysis.

Fourteen spontaneously fermented cocoa beans collected in Ecuador (n = 3), Malaysia (n = 3),

Brazil (n = 2), Indonesia (n = 3) and Ivory Coast (n = 3) were as well analyzed. The spontaneous

fermentation was induced by piling up the unfermented beans with the pulp in platforms. The

beans were rotated during the first 4 days of fermentation to ensure homogenization. Ivory

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Coast samples (n=2) were fermented for 7 days. There was no data regarding the fermentation

duration from one sample from Ivory Coast. Samples from Indonesia, Malaysia and Brazil were

fermented for 6 days and samples from Ecuador for a period ranging from 4 to 5 days. After

fermentation, cocoa beans were dried under the sun for a period of 7 to 10 days. The samples

were stored in falcon tubes at 4°C until the beans were processed for analysis.

Cocoa beans were de-shelled manually and ground using a mechanical grinder Retsch (Haan,

Germany). Dry matter (DM) content was calculated as the difference in weight of 2 g of cocoa

powder before and after heating at 105 °C for 20 hours.

2.3. Extraction of carbohydrates

For the extraction of LMWC from cocoa, defatted cocoa powder (150 mg) was subjected to

two cycles of solid-liquid extraction (SLE) using ethanol 80% as extracting solvent, following

the procedure described by Megias-Perez et al. [23].

All samples were prepared in duplicate.

2.4. GC-MS analysis

A two-step derivatization procedure (oximation + silylation) of carbohydrates was carried out

prior to GC–MS analysis according to the method described elsewhere [16]. Briefly, 0.1 mL of

70% methanolic solution of phenyl--D-glucoside (1 mg mL-1; internal standard) was added to

2 mL of cocoa sample extracts and to 0.5 mL of solutions of carbohydrate standards (1 mg mL-

1 in methanol: water 70:30, v/v). Thereafter, samples were evaporated under vacuum and treated

with 350 µL of 2.5% hydroxylamine chloride in pyridine at 75 °C for 30 min. Then, 350 µL of

hexamethyldisilazane (HMDS) and 35 µL of trifluoroacetic acid (TFA) (both from Sigma

Aldrich) were added and the solution was kept at 45 °C for 30 min. Samples were centrifuged

at 7000 g at 5 °C for 5 min. The derivatization procedure employed converts carbohydrates to

oximes prior to trimethylsilylation, thereby reducing the number of chromatographic peaks for

each reducing carbohydrate to two (corresponding to E- and Z- oxime isomers), and one peak

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109

for the non-reducing carbohydrates. Thus, simpler chromatographic profiles are obtained to

facilitate the identification of target compounds in complex mixtures [18].

Qualitative analysis of carbohydrates was carried out using a 7890A gas chromatograph

coupled to a 5975C quadrupole mass detector (Agilent Technologies, Palo Alto, CA, USA)

operating in EI mode at 70 eV. A high-temperature polycarborane–siloxane HT-5 capillary

column (25 m × 0.22 mm i.d. × 0.1 µm film thickness; SGE, Ringwood, Australia) was used

for the qualitative analysis. Helium at 1 mL min−1 was used as carrier gas. The oven temperature

was programmed as follows: 180 °C for 10 min, followed by 200 °C at a heating rate of 5°C

min-1, afterwards 270 °C at a heating rate of 15 °C min-1, then 290 °C at 1 °C min-1, 360 °C at

5 °C min-1 and held for 30 min. The transfer line was set at 280 ºC and the ionization source at

230 ºC. Subsequently, 1 µL of sample was injected in the split mode (split ratio of 1:10) at 300

°C. Data acquisition was performed using a HPChem Station software (Agilent Technologies).

Samples were analyzed in duplicate.

Linear retention indices (IT) were calculated from the retention times of LMWC trimethylsilyl

oxime (TMSO) derivatives and those of suitable n-alkanes (from C17 to C40) as described by

Messadi et al. (1990) [24].

Carbohydrates were identified by comparison of experimental linear retention indices (IT) and

mass spectra with the available standards. Compounds for which commercial standards were

not available were tentatively identified on the basis of their mass spectral information and data

from the literature [14,15,25].

2.5. HILIC-ESI-TOF MS analysis.

For HILIC-ESI-TOF MS analysis, 1 mL of the sample was filtered through a CHROMAFIL

Xtra PTFE-45/25 filter (Macherey-Nagel, (Macherey Nagel, Düren, Germany ) and 10 µL of

an internal standard solution of 1 mg mL-1 of Asp-Phe methyl ester were added.

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An Agilent 1100 Series HPLC (Agilent Technologies, Karlsruhe, Germany) was used to

perform the chromatographic analysis. A BEH X-Bridge column, with a trifunctionally-bonded

amide phase (Waters Company, USA) and the following characteristics: 150 mm × 3.0 mm;

3.5 μm particle size and 135 Å pore size was chosen to perform the chromatographic analysis.

Water (solvent A) and acetonitrile (solvent B) with 0.05% ammonium hydroxide were used as

mobile phase. Injection volume was set to 3 µL. A flow rate of 0.4 mL min-1 was used.

A microTOF mass spectrometer fitted with an ESI ion source (Bruker Daltonics HCT Ultra,

Bremen, Germany) operating in positive ion mode in the range of 50-1200 m/z was used to

perform the identification of the molecular formula of the compounds and the quantitative

analysis. Internal calibration of the instrument was carried out by the injection of 0.1 M sodium

formate solution before starting the sequence run. Additionally, sodium formate solution was

injected automatically through a six-port valve prior to each chromatographic run to perform a

posterior calibration. The ESI source parameters were adjusted as follows: spray voltage, 4.5

kV; drying gas (N2, 99.5% purity); temperature = 220 °C; drying gas flow, 12 L min-1; nebulizer

(N2, 99.5% purity) pressure, 1.6 bar. Data acquisition was performed using HyStar 3.2 software

(Bruker, Bremen, Germany).

Optimization of the chromatographic method was carried out considering different parameters

such as chromatographic resolution (Rs), retention time (tR) and peak width at half height (wh)

for the target compounds. The chromatographic resolution was calculated as 2(tR2 − tR1)/(wb1 +

wb2), where tR1 and tR2 refer to retention times of two consecutive eluting carbohydrates with

the same m/z value and wb is the peak width at base of each of them. Different binary gradients

were evaluated (Table 1). The optimal conditions were achieved using the following gradient:

0–5 min, 17.5 % A; 5–37 min, 17.5–40% A; 37–50 min 17.5% A. The effect of column

temperature was also studied (25–55°C).

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111

The identification of carbohydrates in cocoa beans was performed by comparing the retention

time and exact mass data obtained for each carbohydrate operating in positive ion mode with

those of commercial standards. The mass error detected was below 5 ppm.

For the quantitative analysis, the areas of the Extracted Ion Chromatogram (EIC) of the sodium

adduct [M+Na]+ of scyllo-inositol, 1-kestose and galactinol (m/z 203.05, 527.15, 365.10

respectively) of standards and samples were normalized with respect to the area of the internal

standard. Calibration curves were calculated using the normalized areas of each standard. The

calibration curves and linear range of concentration of each standard are shown in Table 2.

Results were expressed as µg g-1 DM cocoa bean. Matrix effect for each carbohydrate was

evaluated as the average of the recovery obtained after the addition of two different amounts of

the standard to the carbohydrate extract of cocoa bean. The recovery for each amount of

standard was performed in triplicate.

The chromatographic precision of the method was measured on the basis of intra-day precision

and inter-day precision. Three different samples were selected for the determination of intra-

day precision, calculated as the average of the relative standard deviations (RSD, %) in 5

independent measurements from each sample for the concentrations of scyllo-inositol, 1-

kestose and galactinol. Inter-day precision was determined as the average of RSD of the

concentrations of scyllo-inositol, 1-kestose and galactinol of three different samples, measured

on three different days each.

The evaluation of the reproducibility of the entire method (extraction of LMWC and

chromatographic separation) was based on the average RSD values of each compound

measured on the 35 samples performed in duplicate of sample preparation.

Signal to noise ratio (S/N) of three and ten was used as criteria to determine the limit of

detection (LOD) and quantification (LOQ).

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Table 1. HILIC parameters: retention times (tR), peak widths (wh) and resolution (Rs) obtained under different chromatographic conditions. UT: unknown trisaccharide; UD:

unknown disaccharide. Rs calculated: myo-Inositol scyllo-Inositol; UT 1 1-Kestose; 1-Kestose UT 2; UD 2 Galactinol.

Method conditions (A % water) T

(°C)

myo-Inositol scyllo-Inositol UT 1 1-kestose UT 2 UD 2 Galactinol

0-5 min, 26%; 5-37 min 26-40%;

40

tR (min) 8.9 9.0 10.5 11.5 12.6 13.7 15.2

37-50 min 26% wh (min) 0.2 0.2 0.3 0.3 0.5 0.3 0.3

Rs 0.3 1.3 1.3 2.1

0-5 min, 24%; 5-37 min 24-40%;

40

tR (min) 10.5 10.8 12.9 14.1 15.6 16.4 18.1

37-50 min 24% wh (min) 0.3 0.2 0.3 0.3 0.5 0.4 0.3

Rs 0.6 2.0 2.1 2.3

0-5 min, 20%; 5-37 min 20-40%;

40

tR (min) 14.6 15.0 18.3 19.6 21.1 21.7 23.6

37-50 min 20% wh (min) 0.3 0.4 0.3 0.3 0.4 0.3 0.3

Rs 0.6 2.2 2.1 2.9

0-5 min, 17.5%; 5-37 min 17.5-40%,

40

tR (min) 17.1 17.6 21.3 22.6 24.0 24.6 26.3

37-50 min 17.5% wh (min) 0.3 0.3 0.3 0.3 0.5 0.3 0.3

Rs 0.8 2.4 2.3 2.8

0-5 min, 17.5%; 5-37 min 17.5-40%,

25

tR (min) 19.4 19.8 22.8 23.9 25.4 26.3 27.7

37-50 min 17.5% wh (min) 0.3 0.3 0.3 0.3 0.3 0.3 0.3

Rs 0.7 1.7 2.1 2.0

0-5 min, 17.5%; 5-37 min 17.5-40%,

55

tR (min) 15.6 16.1 20.3 21.7 22.9 23.2 25.0

37-50 min 17.5% wh (min) 0.3 0.3 0.4 0.3 0.5 0.3 0.3

Rs 0.8 2.3 2.0 3.0

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Table 2. Analytical parameters of HPLC-ESI-TOF MS.

n* = number of samples analyzed. For intra-day precision, each sample was injected five times on the same day. For inter-day

precision samples were injected in three different days.

Calibration curve R2 Linear

working

range

(µg mL-1)

L.O.Q

(µg mL-1)

L.O.D

(µg mL-1)

Intra-day

Precision

(% RSD)

(n*=3)

Inter-day

Precision

(% RSD)

(n=3)

scyllo-Inositol y = 0.0157x + 0.0222 0.9859 0.50 - 40 0.50 0.17 6.61 4.83

1-Kestose y = 0.013x + 0.0125 0.9955 0.25 -25 0.25 0.08 7.53 5.43

Galactinol y = 0.0101x + 0.0086 0.9942 0.75 - 40 0.75 0.25 4.98 5.81

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2.6. Statistical analysis.

GraphPad Prism 7.0 software (San Diego, California, USA) was used for statistical analysis. A

one-way analysis of variance (ANOVA) followed by a Fisher test as a post hoc comparison of

means was used to determine significant differences (P < 0.05) in scyllo-inositol, 1-kestose and

galactinol content among cocoa beans collected from different countries. A t-test was applied

to evaluate the possible differences in the content of each carbohydrate per fermentation status

(unfermented versus fermented).


3.1. Qualitative analysis of cocoa bean LMWC

As mentioned above, GC-MS was selected as a powerful tool for the characterization of LMWC

of cocoa bean extracts and therefore, complement the structural information previously reported

using HILIC-ESI-TOF MS and HILIC-ESI-MSn [22].

Different LMWC were detected, including mono-, di-, tri-, tetrasaccharides and cyclitols.

Figure 1 shows the GC-MS profile of TMSO derivatives of carbohydrates obtained from an

unfermented cocoa bean extract (see Table 3 for peak identification, experimental and literature

reported IT values for the different compounds).

The monosaccharide profile previously reported in the literature [22,26,27,28], including

fructose, glucose and galactose, was corroborated (peaks 1 to 5 in Figure 1) by the comparison

of the experimental retention times and mass spectra with the corresponding standards.

Peaks 6 and 7 (IT of 1957 and 2042, respectively) showed typical spectra of free inositols, with

a pair of characteristic ion fragments at m/z 305 and 318, which were similar to the intensity of

the pair of m/z 191 and 217 [29]. These compounds were identified as scyllo-inositol (peak 6)

and myo-inositol (peak 7). It must be pointed out that this is the first time that scyllo-inositol is

reported in cocoa beans.

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Figure 1. GC–MS profile of TMSO derivatives of carbohydrates of unfermented cocoa bean extract. For peak

identifications see Table 3.

Figure 1A shows different peaks eluting in the disaccharide zone. Sucrose and melibiose, which

have been previously reported in cocoa beans [22,26] were identified (peaks 8, 18 and 20

respectively). Different peaks with mass spectra compatible with disaccharides were also

detected (see Figure 2).

Peaks 12 and 13 (IT 2691 and 2711 respectively) were identified as maltose isomers E and Z.

Peaks 9 (IT 2566) and 11 (IT 2675) showed a mass spectrum similar to sucrose, with m/z

fragments at 437 and 451. The high intensity of the m/z 217 fragment indicated the presence of

fructose as the non-reducing unit of the disaccharide [14]. Moreover, m/z fragments at 361

(characteristic of glycosidic linkages) and at 538 (characteristic of the whole oxime chain) were

also observed. Thus, these compounds were tentatively assigned as fructosyl-glucoses. Peak 10

mass spectrum showed ions at m/z 307, (characteristic of disaccharides with a reducing ketose

substituted in C1 or C3) and m/z 437 (characteristic of ketohexoses in both pyranose and

furanose forms, free and monosubstituted) which is compatible with a fructosyl-fructose [30].

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Due to the absence of standards for these compounds, these identifications could only be

considered as tentative.

Table 3. Peak assignation and linear retention indices (IT) of different carbohydrates detected in unfermented cocoa

bean extracts by GC–MS.

Peak

number

Compound Retention time

(min)

Experimental IT Literature IT

1 Fructose 1 4.963 1830 1846 [212]

2 Fructose 2 5.187 1844 1858 [212]

3 Galactose E 5.913 1887 1888 [212]

4 Glucose E 6.163 1901 1896 [212]

5 Glucose Z + Galactose

Z

6.720 1923 1920 [212]

6 Scyllo-inositol 7.588 1957 1979 [219]

7 Myo-inositol 10.090 2042 2048 [212]

8 Sucrose 18.385 2508 2517 [212]

9 Fructosyl-glucose* +

unknown

18.846 2566 -

10 Fructosyl-fructose* 19.124 2602 -

11 Fructosyl-glucose* 19.708 2675 -

12 Maltose E 19.836 2691 2697 [212]

13 Maltose Z 19.999 2711 2715 [212]

14 Unknown 20.162 2729 -

15 Unknown disaccharide 20.230 2737 -

16 Unknown 20.406 2757 -

17 Unknown 20.637 2784 -

18 Melibiose E 20.718 2793 -

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Peak

number

Compound Retention time

(min)

Experimental IT Literature IT

19 Galactinol 21.274 2849 2874 [212]

20 Unknown + Melibiose

Z

21.539 2875 -

21 Unknown 22.468 2958 -

22 Diglycosyl glicerol 22.766 2983 -

23 Raffinose 25.405 3166 3158[212]

24 6-Kestose 25.622 3179 3170 [92]

25 1-Kestose 25.818 3191 3198 [92]

26 Planteose 27.188 3265 3278 [92]

27 Unknown trisaccharide

(Glc-(X→X)-Glc-

(1→2)-β-Fru)

28.016 3307 -

28 Stachyose 44.843 3976 3976 [212]

* Peaks tentatively identified

Mass spectrum of peak 19 was characterized by the triplet m/z ions 191/204/217, (characteristic

of silylated pyranose rings), 305 and 318 (typical of cyclitols) and a low abundance of m/z ion

361 (related to glycosidic linkages). This fragmentation pattern has been described to be typical

of cyclitol glycosides [16]. Considering that myo-inositol was the most abundant free inositol

detected in cocoa beans, this compound could be a glycosyl-myo-inositol and it was identified

as galactinol (O- -D-galactopyranosyl-(1→3)-D-myo-inositol) by comparing its retention

time and mass spectrum with that of its standard. The presence of galactinol has been

previously described in cocoa beans by Wang et al. [31], however, this compound has been

scarcely studied in this matrix.

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Figure 2. Electron impact ionization mass spectra of TMSO derivatives of different unknown carbohydrates.

Peak 22 could be tentatively assigned as diglycosyl-glycerol due to the presence of specific m/z

ion at 337 [32].

The presence of raffinose family oligosaccharides (RFOs) such as raffinose (peak 23) and

stachyose (peak 28), previously reported in the literature [22,26,27], was confirmed in cocoa

bean extracts by comparison with the corresponding standards. Unknown peaks were also

detected in the trisaccharide eluting zone (Figure 1, B). Peak 25 could be identified as 1-kestose,

while peak 24 and 26 were identified as 6-kestose and planteose respectively by comparing

their IT values and spectra with data reported in the bibliography [92]. Peak 27 (IT 3307) showed

a mass spectrum similar to theanderose (-Glc-(1→6)--Glc-(1→2)-β-Fru) but with lower

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119

retention time, thus this peak was tentatively identified as glucosyl-sucrose (Glc-(X→X)-Glc-

(1→2)-β-Fru).

Overall, although the metabolic functions of the LMWC have extensively been studied in higher

plants [33, 34], few studies can be found regarding the role of the LMWC in T. cacao.

From a nutritional point of view, the presence of inositols (scyllo-inositol and galactinol) and

fructooligosaccharides (FOS), as 1-kestose or 6-kestose in cocoa beans, is noteworthy due to

their nutraceutical properties. For example, scyllo-inositol has been reported as an inhibitor of

the amyloid plaque formation in Alzheimer disease [35], while FOS have been described as

prebiotics [36].

3.2. Quantitative analysis of cocoa bean LMWC

Once LMWC composition of unfermented cocoa beans was identified employing GC-MS, to

evaluate their potential utility as indicators of cocoa bean origin or fermentation status,

quantitative analysis of those carbohydrates scarcely studied (galactinol) or reported for the first

time in cocoa bean (scyllo-inositol and 1-kestose) were carried out using HILIC-ESI-TOF MS.

This technique does not require previous derivatization of the sample and it could be

advantageous for routine analysis. However, a previous optimization of the method was

required to avoid overlapping of the peaks.

3.2.1. Optimization of HILIC method

An unfermented cocoa bean extract and carbohydrate standards were used to optimize the

HILIC-ESI-TOF MS method. The selection of the optimal chromatographic conditions was

based on the Rs, tR and wh values determined for the sodiated molecular ion adducts of the target

compounds (see Table 1).

Different gradient conditions and column temperatures were used to evaluate the

chromatographic separation of scyllo-inositol, 1-kestose and galactinol from other interferent

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carbohydrates. In all the conditions, 50 min was considered as an appropriate time for the

chromatographic analysis.

The acetonitrile mobile phase content was first evaluated at a constant temperature of 40°C.

Table 1 shows the different Rs values calculated for the different chromatographic conditions

tested. As it can be observed in Table 1, the chromatographic conditions using high water

content with 0.05% ammonium hydroxide (26% in 0-5 min, 26–40% in 5-37 min and 26% in

37-50 min) resulted in the lowest resolution between myo- and scyllo-inositol, with an Rs value

of 0.25. A higher resolution between both inositols was achieved as the initial acetonitrile

content increased. The same tendency was observed for the Rs values considered for the other

compounds. Among the different chromatographic conditions tested, the best baseline

separation of the target carbohydrates was achieved under the following water gradient: 17.5%

in 0-5 min, 17.5–40% in 5-37 min and 17.5% in 37-50 min. In general, narrow peaks were

obtained for all the conditions tested (0.2-0.5 min).

The effect of the temperature (25, 40 and 55°C) was also evaluated using the gradient mentioned

above. According to Table 1, the use of higher temperatures (55°C) did not enhance the Rs and

wh values obtained at 40°C for inositols. However, decreased Rs values were observed at room

temperature (25°C). In general, no pronounced differences in the resolution values and peak

widths of the other target compounds were observed with the different temperatures. Thus,

40°C was selected as the optimum temperature to perform the chromatographic separation.

Figure 3 shows the profile of EIC corresponding to cocoa bean monosaccharides ([M+Na]+,

m/z 203.05), disaccharides ([M+Na]+, m/z 365.10) and trisaccharides ([M+Na]+, m/z 527.15) of

an unfermented cocoa bean under optimized chromatographic conditions. Under these

conditions, tR of the target carbohydrates was 17.6 min for scyllo-inositol, 22.6 min for 1-

kestose and 26.3 min for galactinol.

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121

Figure 3. Extracted ion chromatograms obtained by HILIC-ESI-TOF MS of unfermented cocoa bean from

Malaysia corresponding to: A) 203.05 m/z ions, B) 365.10 m/z ions and C) 527.15 m/z ions. Peak numbers: 1)

Fructose, 2) Glucose + galactose, 3) Sucrose, 4) myo-Inositol, 5) scyllo-Inositol, 6) 1-Kestose, 7) Raffinose, 8)

Galactinol, 9) Maltose + unknown disaccharide (UD), 10) Melibiose, UT) Unknown trisaccharides.

In comparison with other methods employed in LMWC analysis of cocoa beans previously

reported, which employ for sample preparation tedious procedures such as solid phase

extraction (SPE), ion exchange or ultrafiltration followed by purification with Sep-pak C18

cartridge [22,27,37], the proposed method requires a minimum sample preparation, offers a

better resolution between inositols (myo-, scyllo-inositol) and would allow the quantification of

an elevated number of LMWC in cocoa beans.

3.2.2. Analytical parameters

The suitability of the optimized HILIC-ESI-TOF MS method for the quantitation of the target

carbohydrates was evaluated. Using reference standards of scyllo-inositol, 1-kestose and

galactinol, a calibration curve was obtained for each carbohydrate with good R2 values (higher

than 0.98), confirming the linearity of the calibration. After that, the influence of the matrix

effect on the quantitation of target compounds was evaluated. The average recovery values of

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122

105.1 %, 102.0%, 104.6 % for scyllo-inositol, 1-kestose and galactinol respectively confirmed

the absence of matrix effect.

Table 2 shows the analytical parameters of the chromatographic analytical method. The lowest

LOQ and LOD values were determined for 1-kestose (0.25 and 0.08 µg mL−1, respectively) and

the highest values were observed for galactinol (0.75 and 0.25 µg mL−1, respectively).

Regarding repeatability of the method, good values of intraday precision (range 4.98 - 7.53%)

and interday precision (range 4.83 - 5.81%) were obtained for the target carbohydrates under

study.

The reproducibility of the whole method (extraction of carbohydrates from the cocoa bean and

chromatographic separation) was evaluated based on the average RSD values of each analyte

measured on the 35 samples performed in duplicate of sample preparation. The average RSD

values for scyllo-inositol, 1-kestose and galactinol were 5.41%, 6.07%, 6.37% respectively.

These data suggested lower variability related to the extraction method and good reproducibility

of the measurements.

Overall, all parameters indicated the suitability of the proposed HILIC-ESI-TOF MS method

for the quantification of scyllo-inositol, 1-kestose and galactinol in cocoa beans.

3.3. Analysis of the content of scyllo-inositol, 1-kestose and galactinol in cocoa beans.

Table 4 shows the different values of scyllo-inositol, 1-kestose and galactinol content from 21

unfermented beans and 14 spontaneously fermented cocoa beans from five different origins.

Supplementary information (tables S2, S3 and S4) shows the individual scyllo-inositol, 1-

kestose and galactinol values of the samples analyzed.

3.3.1. scyllo-Inositol

The content of scyllo-inositol was in the range of traces - 504.9 µg g-1 cocoa DM in unfermented

and 15.5 - 491.9 µg g-1 cocoa DM in spontaneously fermented beans. The values of scyllo-

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123

inositol determined in cocoa beans were lower in comparison to the content reported in other

food matrices, such as Apiaceae family (values close to 2 mg g-1 dry weight) [38].

In unfermented beans, one-way ANOVA test showed significant differences among bean origin

(p-value = 0.0450). Fisher test revealed significant differences in the content of scyllo-inositol

(p < 0.05) between the following pair of countries: Ecuador-Ivory Coast, Ecuador-Brazil and

Ecuador-Indonesia (individual p-values of these comparisons are shown in Figure 1S,

supplementary information). Inositols are a family of compounds crucial for development and

signalling in plants. They function mainly as either metabolic mediators or participating in

various signalling pathways in response to environmental conditions (stress, hormones, and

nutrients) through transcriptional regulation of the stimuli-responsive genes [39]. Thus, the

variability observed in unfermented beans could be attributed to the different environmental

conditions, such as soil pH in the different farming locations of the samples under study [40].

Regarding fermented cocoa beans, one-way ANOVA test identified significant differences

among countries (p = 0.0058). Fisher test revealed significant differences in the content (p <

0.05) between the following pair of countries: Ecuador-Ivory Coast, Ecuador-Brazil, Ecuador-

Indonesia, Malaysia-Ivory Coast, Malaysia-Brazil, Malaysia-Indonesia (individual p values of

these comparisons are shown in Figure 3S, supplementary information).

Figure 2S (supplementary information) shows the comparison within each country of the

scyllo-inositol concentration in the fermented and unfermented samples. Differences in the

content from Brazil samples were detected using t-test.

Further studies need to be carried out to understand the variability observed in scyllo-inositol

content according to the origin and to identify the role of scyllo-inositol during fermentation.

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Table 4. Mean ± standard deviation (Min-Max) values of each carbohydrate under study (scyllo-inositol, 1-kestose and galactinol) per country and status of fermentation ( U:

unfermented beans, F: fermented beans).

Country

group size

Mean ± SD (Min-Max) µg /g DM

scyllo-Inositol 1-kestose Galactinol

Unfermented Fermented Unfermented Fermented Unfermented Fermented

Ivory Coast

U*: n=6

F: n=3

54.9 ± 85.3 (tr** – 219.0) 96.4 ± 80.5 (15.5 – 176.5) 101.2 ± 19.2 (73.2 – 127.9) 54.2 ± 33.1 (16.1 – 75.7) 1014.4 ± 658.0 (361.5 – 1970.4) 0.0 ± 0.0 (0.0 – 0.0)

Indonesia

U: n=4

F: n=3

77.3 ± 56 (17.7 – 145.3) 140.6 ± 31.8 (108.2 – 171.7) 74.4 ± 41.7 (36.1 – 133.5) 48.7 ± 59.8 (tr – 115.5) 11.8 ± 3.1 (8.6 – 16.0) 0.0 ± 0.0 (0.0 – 0.0)

Malaysia

U: n=3

F: n=3

190.1 ± 57.1 (131.5 – 245.6) 381.2 ± 83.4 (303.6 – 469.4) 88.3 ± 37.9 (48.2 – 123.6) 35.0 ± 36.6 (tr – 73.1) 383.1 ± 351.9 (163.6– 789.1) 0.0 ± 0.0 (0.0 – 0.0)

Brazil

U: n=3

F: n=2

32.4 ± 16.3 (13.5 – 42.5) 77.1 ± 19.0 (63.6 – 90.5) 92.9 ± 18.3 (76.1 – 112.4) 14.9 ± 21.1 (tr – 29.9) 56.6 ± 75.1 (tr– 141.8) 0.0 ± 0.0 (0.0 – 0.0)

Ecuador

U: n=5

F: n=3

235.2 ± 175.2 (57.1 – 504.9) 338.4 ± 136.4 (231.0 – 491.9) 85.0 ± 18.9 (57.0 – 108.4) 11.0 ± 19.0 (tr– 32.9) 597.2 ± 676.5 (128.7 – 1770.1) 0.0 ± 0.0 (0.0 – 0.0)

*U: unfermented; F: fermented; **tr: traces

RESULTS

125

3.3.2. 1-Kestose

The content of 1-kestose determined in unfermented and fermented beans was in the range of

36.1 - 133.5 and traces - 115.5 µg g-1 cocoa DM respectively. These values were lower in

comparison to the content determined in other food matrices such as green and ripe fruits of

ackee, carambola and jun plum (range of 0.1 - 0.5 g/100 g dry weight) [41] or bananas from

different cultivars (values range from 297 - 1630 µg g-1 DM) [42].

One-way ANOVA analysis was not able to detect significant differences in 1-kestose content

among the different countries of origin, neither for the unfermented nor the fermented samples.

The comparison within each country of 1-kestose concentration in the pair fermented and

unfermented samples is shown in the Figure 4S (supplementary information). The t-test analysis

showed significant differences in Ivory Coast, Ecuador and Indonesia samples.

To date, the mechanism of LMWC degradation during the fermentation of cocoa beans is

unknown. The LMWC degradation could be attributed to the elevated temperatures during the

fermentation and the diminution of pH in the bean as a consequence of the production of organic

acids by the microorganisms.

3.3.3. Galactinol

The content of galactinol in unfermented beans was in the range of traces -1970.4 µg g-1 cocoa

DM. These values were in the same order of magnitude to the values described in other seeds,

e.g., lentils (0.12 % DM) [43]. No galactinol content was determined in spontaneously

fermented beans. Thus, this compound might be considered as a potential marker for the

fermentation status.

To the best of our knowledge, there are not previous studies focusing on the evaluation of

galactinol during the spontaneous fermentation of cocoa beans. As commented before, during

the fermentation process, a diminution of the pH in the beans consequence of the organic acid

production by microbial activities and the relative high temperatures (up to 50°C) could produce

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126

the degradation of some compounds present in unfermented beans, which could explain the

absence of galactinol in the fermented bean samples.

One-way ANOVA analysis did not show any significant differences per country in unfermented

beans. The wide variability observed in the content of galactinol might be attributed to the

different ripening degree of the unfermented beans collected in different origins [31].

4. CONCLUSIONS

In this study, the information reported about LMWC using GC-MS has been shown to be

complementary to the data previously reported employing HILIC-ESI-TOF MS and HILIC-

ESI-MSn. Different carbohydrates such as scyllo-inositol, 1-kestose and 6-kestose have been

reported for the first time in unfermented cocoa beans. Moreover, tentative identifications of

other minor LMWC have been proposed.

Furthermore, the optimized and validated HILIC-ESI-TOF MS method allowed the separation

and quantitation in different cocoa bean samples of scyllo-inositol, 1-kestose and galactinol,

compounds interesting for their remarkable biological functions. The content of these LMWC

in cocoa beans from different locations and different fermentation status has been reported for

the first time in this manuscript. The results showed significant differences in scyllo-inositol

content among the different origins, independently of the fermentation status of the beans. Also,

the results indicated that galactinol content seems to be related with the different fermentation

status of the bean. However, further studies with a higher number of samples or a detailed study

of the changes during the fermentation would be necessary to confirm this affirmation.

The proposed methodology has demonstrated to be highly sensitive, with minimum sample

preparation, which may be used for the quality control analysis of cocoa beans or other matrixes

containing inositols and oligosaccharides.

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5. ACKNOWLEDGEMENTS

Authors would like to mention their gratitude to Sabur Badmos for proofreading of the

manuscript and Dr Gorka Ruiz de Garibay for the fruitful and valuable discussions on the

statistical analysis. Also, the authors would like to acknowledge to Anja Müller for her

assistance during the measurements of the samples as well as to Diana Sirbu and Britta

Behrends for performing the defatting process of all samples of the study.

This work was supported by Barry Callebaut (Belgium), by Ministerio de Economía, Industria

y Competitividad of Spain (project AGL2016-80475-R, AEI/FEDER, UE), by Comunidad de

Madrid (Spain) and European funding from FEDER program (S2013/ABI-3028

AVANSECAL-CM).

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128

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Chapter 6. Monitoring the changes of low molecular weight carbohydrates in

cocoa beans during spontaneous fermentation: a chemometric and kinetic

approach

Roberto Megias-Perez, Mauricio Moreno-Zambrano, Britta Behrends, Marcello Corno,

Nikolai Kuhnert

Manuscript under revision in Food Chemistry (retrieved 08/05/2019)

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ABSTRACT

The spontaneous fermentation process of cocoa beans is considered the crucial step in the

formation of typical coca aroma precursors. In this manuscript, we have utilized a

chromatographic method, HILIC-ESI-TOF MS, to monitor the low molecular weight

carbohydrates (LMWC) changes and acquire absolute quantitative data of the main LMWC

from five different types of cocoa beans from different origins during spontaneous

fermentation. A sequential degradation of tetra-, tri- and disaccharides and an increase of the

monosaccharide concentration was determined during spontaneous fermentation.

The chemometric evaluation considering the LMWC data (quantities and areas), pH values, dry

matter content and total lipid values indicated that no significant changes had accurred during

the first 48 hours.

The results from the kinetic evaluation of the main LMWC offered useful information (reaction

order, the different rates (kobs) and half-life values (t1/2)) that enable an ample and better

understanding of the mechanism behind spontaneous fermentation of cocoa beans.

Keywords: cocoa beans, spontaneous fermentation, carbohydrates, chemometrics, kinetics

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1. INTRODUCTION.

The production of chocolate constitutes a multi-step process involving different processing

steps including fermentation, drying, roasting, conching and tempering. The sensorial

properties of cocoa beans are in part related to the genotype of the cocoa tree (Theobroma cacao

L.) yielding cocoa beans [1]. However, the spontaneous fermentation is the crucial step yielding

aroma precursors, subsequently transformed to active aroma volatiles during roasting [2].

Spontaneous fermentation is induced by the microbiota naturally present in the environment of

the producing cocoa country. This procedure represents the most common type of fermentation

used globally despite recent advances in cocoa fermentation induced by controlled starter

cultures. The diversity of the microbiota according to the location has been previously

addressed [3]. During the spontaneous fermentation, a series of microorganisms, such as yeast,

lactic and acetic acid bacteria proliferate in the carbohydrate-rich pulp surrounding the

unfermented cocoa bean [4]. Primary fermentation metabolites produced by the microbial

activity (mainly ethanol, lactic and acetic acid) diffuse into the bean, causing a decrease in the

pH value, structural changes inside the cotyledon and different reactions both enzymatic and

chemical. As a consequence of these conditions, the death of the cocoa bean embryo occurs [5].

The duration of the spontaneous fermentation ranges between five to six days, longer

fermentation time has been suggested to facilitate the proliferation of spore-forming bacteria

and filamentous fungi producing metabolites with negative influence in the flavour of the beans,

and therefore, in their quality [3].

Among the different metabolic reactions occurring in this complex matrix [6] during the

fermentation, it is worth mentioning the gradual degradation of proteins to oligopeptides [7],

the degradation of anthocyanins [8] and procyanidins [9, 10] and the increase in bioactive amine

content, as spermidine [11]. However, the changes in the low molecular weight carbohydrates

(LMWC) during fermentation have been scarcely studied, monitoring mainly the changes in

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134

fructose, glucose and sucrose concentrations during fermentation [12]. Recently our group has

developed analytical approaches based on hydrophilic interaction liquid chromatography and

gas chromatography coupled to mass spectrometry (HILIC-MS and GC-MS) aiming to identify

the LMWC profile and quantify monosaccharides (fructose, glucose), disaccharides (sucrose,

melibiose, maltose), polyols (mannitol, myo-inositol, scyllo-inositol, galactinol) and

oligosaccharides ( raffinose, 1-kestose, stachyose). Those studies have revealed differences in

the LMWC profile comparing unfermented and fermented cocoa beans [13].

The study of the LMWC during the spontaneous fermentation of cocoa beans is considered

scientifically relevant due to the significant reactions of LMWC with amino acids and peptides

in Maillard reactions yielding Amadori compounds [7], compounds precursor of volatile

compounds via Strecker reaction.

Also, the study of the LMWC changes might contribute to a better understanding of the

biochemical reactions occurring during the fermentation of cocoa beans. To the best of the

author's knowledge, no studies focusing on a comprehensive characterization of the changes in

each LMWC during the spontaneous fermentation has been reported.

Based on the points above mentioned, this study has been aimed to investigate the LMWC

changes during the spontaneous fermentation of cocoa beans. To achieve this goal, five

spontaneous fermentations collected in different geographical origins have been evaluated

using a chemometric and kinetic approach.


2.1. Chemicals and standards.

Dichloromethane and LC-MS grade acetonitrile (ACN) were provided by Aplichem Panreac

(Darmstadt, Germany). Ammonium hydroxide solutions, fructose, glucose, melibiose, myo-

inositol, sucrose, raffinose, stachyose, Asp-Phe methyl ester (internal standard), were supplied

RESULTS

135

by Sigma Chemical Co. (St. Louis, USA). scyllo-Inositol, 1-kestose and galactinol were

obtained from Carbosynth (Compton, UK).


Cocoa beans at different fermentation points from five different cocoa hybrid trees and origins

(G11UTA402XT413 from Ivory Coast, Comum from Brazil, German from Cameroon, EET

103 from Ecuador and PBC 159 from Malaysia) were analyzed. The samples were collected at

24-hour intervals during fermentation. The duration of the spontaneous fermentation ranged

between 120 h and 168 h, according to the techniques used at each origin. After sample

collection, these were frozen at − 20 °C and shipped to Jacobs University Bremen (Germany)

on dry ice. Further storage was done at − 20 °C. Table 1S shows the characteristics (time points

and hybrid) of the different samples under study.

2.3. Determination of DM and pH

Cocoa beans were de-shelled manually using a mechanical grinder Retsch (Haan, Germany).

Dry matter (DM) content was calculated as the difference in weight before and after heating 2

g of cocoa powder at 105 °C for 20 hours. The LMWC analysis was performed using cocoa

beans defatted according to the method described by D’Souza et al. [9].

The pH measurement of the bean was performed on the supernatant obtained after vortexing 1

g of cocoa with 9 mL of Milli-Q for 2 min and posterior centrifugation at 4400 rpm for 5 min.

2.4. LMWC extraction

LMWC were extracted from 150 mg of defatted cocoa powder following the protocol described

by Megías-Pérez, Grimbs, D'Souza, Bernaert and Kuhnert [13]. Briefly, the extraction method

comprised two steps. In a first step, the LMWC were extracted at room temperature with 2 mL

of Milli-Q water under constant stirring for 20 min followed by the addition of absolute ethanol

(8 mL) with continuous stirring for 10 min. After that, the samples were centrifuged at 4400

rpm for 5 min. The second step consisted of extraction for 10 min of the precipitates with 10

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136

mL of 80% ethanol under the same conditions. The supernatants from the two extraction steps

were mixed. All samples were prepared in duplicate.

1 mL of the sample was filtered through a CHROMAFIL Xtra PTFE-45/25 filter (Macherey-

Nagel, (Macherey Nagel, Düren, Germany ) and 10 µL of an internal standard solution of 1 mg

mL-1 of Asp-Phe methyl ester were added.

2.5. HILIC-ESI-TOF MS analysis.

An Agilent 1100 Series HPLC (Agilent Technologies, Karlsruhe, Germany) was used to

perform the chromatographic analysis. A BEH X-Bridge column, with a trifunctionally-bonded

amide phase (Waters Company, USA) was chosen to perform the chromatographic analysis.

Column characteristics were as follows: 150 mm × 3.0 mm; 3.5 μm particle size and 135 Å

pore size. Water (solvent A) and acetonitrile (solvent B) with 0.05% ammonium hydroxide

were used as mobile phase. Injection volume was set to 3 µL and a flow rate of 0.4 mL min-1

was used.

The identification of the molecular formula of the compounds and the quantitative analysis was

performed using a microTOF mass spectrometer fitted with an ESI ion source (Bruker Daltonics

HCT Ultra, Bremen, Germany) operating in positive ion mode in the range of 50-1200 m/z.

Sodium formate 0.1 M was injected before starting the sequence run to perform internal

calibration of the instrument. A posterior calibration was also performed by injecting sodium

formate solution automatically through a six-port valve prior to each chromatographic run. The

ESI source parameters were adjusted as follows: spray voltage, 4.5 kV; drying gas (N2, 99.5%

purity); temperature = 220 °C; drying gas flow, 12 L min-1; nebulizer (N2, 99.5% purity)

pressure, 1.6 bar. HyStar 3.2 software was used for data acquisition (Bruker, Bremen,

Germany).

Regarding quantitation of the different target LMWC, the area of the Extract Ion Chromatogram

(EIC) of the sodium adduct [M + Na]+ from the different standards and target compounds was

RESULTS

137

normalized with respect to the area of the internal standard. The normalized area was used to

calculate the different calibration curves. The total carbohydrate content was determined as the

sum of the quantities of the different LMWC under study. The percentual variation in the

content of each LMWC was determined between the value of the last point of the fermentation

and the value determined for unfermented cocoa beans.

All measurements were performed in duplicate of sample preparation. The average RSD

determined for each analyte in all samples was considered to evaluate the reproducibility of the

entire method (extraction of the carbohydrates and chromatographic separation).

Chromatographic parameters (calibration curves, the Pearson coefficient, the range of linearity,

precision) and matrix effect evaluation for scyllo-inositol, 1-kestose and galactinol were

previously reported (chapter 5). Calibration curve, Pearson coefficient and range of linearity

are shown in Table S1.

2.6. Data analysis, chemometric evaluation and determination of kinetic parameters.

Quant Analysis software (Bruker, Bremen, Germany) was used to extract the area values of the

different LMWC.

LMWC quantities, average area values normalized with respect to the area of internal standard

of the unknown LMWC, pH value and dry matter content from the different cocoa bean samples

under study were subjected to chemometric evaluation. As these variables have different units,

a previous step of auto-escalation (transformation into z-scores, calculated as z = x – median /

SD) was performed to standardize the statistical relevance of all variables [14].

Data of the transformed variables (n = 28) from each sample (n = 35) were arranged in a matrix

with the samples in the columns and variables in the rows. This matrix was used to perform

principal component analysis ( PCA) analysis using the tool Metaboanalyst 4.0 [15].

The process of cocoa bean fermentation is a spontaneous process where the lack of controlled

conditions might influence the LMWC composition. For instance, the effect of mass and turning

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138

time has been reported previouslz to affect the concentration of several compounds during

fermentation [16]. In this manuscript, to reduce errors associated with both different physical

and microbial factor that depend on the origin of the samples, the determination of kinetic orders

and their corresponding parameters was performed using linear mixed modellin. This model

asssumes that the slopes of the lineariyed forms of the reaction zero-, first- and second-order

equations (Eq 1 to 3 respectively) are common for each LMWC regardless of its origin.

Eq (1) C = C0 – kobst

Eq (2) C = C0 exp(-kobst)

Eq (3) 1/C - 1/C0= kobst

In this equations, t is the fermentation time (h); C0 and C are the LMWC content of each

carbohydrate (mg/g dry matter) at time zero and at time t, respectively; kobs is the rate constant.

Linear mix model is a method based on the assumption that random errors occur additively at

two levels; in each experiment and for each substance independently [17]. Also, the Akaike

information criterion (AIC) values were determined. AIC is a tool used for model selection

which is based on the relative quality of a set of statistical models [18].

Linear mixed model and correlation analyses were performed using R software version 3.4.2.

For the linear mixed model, package “nlme” version 3.7-137 was used.

The half-life values (t1/2) of each the LMWC were determined according to the following

equations:

Eq (4) t1/2 = C0/ 2kobs Zero order

Eq (5) t1/2 = ln2/ kobs First order

Eq (6) t1/2 = 1/ C0kobs Second order

The value of C0 used for equations 4 and 6 was the average concentration of the compound in

unfermented cocoa beans. In the case of mannitol, compound which was absent in unfermented

beans, the value of C0 was considered as the average of the values at 24 hours.

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139


3.1. Monitoring the changes in the LMWC content during spontaneous fermentation.

A total of five spontaneous fermentation were investigated. The different fermentations were

conducted in five different origin countries over a period ranging from five to seven days. These

origin countries cover the different world areas of cocoa production and thus, capturing possible

differences in microbiota among locations.

Carbohydrates included for quantitative analysis were selected based on their previously

reported identification in cocoa beans [13]. The suitability of the chromatographic method

employed for quantitation purpose has been previously addressed for scyllo-inositol, 1-kestose

and galactinol. Table 1S shows the different chromatographic parameters (calibration curves,

Pearson coefficient and linearity range) for the rest of compounds. The data suggest the

suitability of the method employed to quantify the concentration of each LMWC during the

spontaneous fermentation.

A gradually decreasing trend was determined for sucrose and oligosaccharide concentration in

all fermentations, with a percentual average decrease percentage related to unfermented cocoa

bean content of 95% for sucrose, 94 % for raffinose, 90% for 1-kestose and 81% for stachyose

(see Figure 1). The trend observed for sucrose was in line with the data reported previously by

Hashim, Selamat, Muhammad and Ali [16]. The degradation observed for sucrose has been

putatively attributed mainly to the presence of endogenous invertase in the cotyledon of the

cocoa bean [20]. However, data reported by Hansen, Del Olmo and Burri [21] showed null

enzymatic activity of this invertase after 48 hours of fermentation. Thus, another additional

mechanism for this degradation, such as the influence of other glycosidases or conditions of

low pH and elevated temperatures [22] should not be discarded.

The data shown for melibiose do not allow establishing any reproducible trend due to the low

content of this LMWC. For example, the fermentation processes from Brazil and Ivory Coast

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140

showed an oscillating decreasing trend while for the rest of the fermentation process this trend

was increasing.

Regarding monosaccharides, an increasing trend was observed for fructose and coeluting

glucose/galactose content in all fermentations, with a percentual average increase of 69% and

97% respectively (see Figure 1).

The data did not show a clear trend in the behaviour of polyols during fermentation. Mannitol,

previously described as an indicator of fermentated beans [13] showed, in general, an increasing

trend from 24 hours until the end of the fermentation, except for the spontaneous fermentation

process from Ivory Coast, which showed an oscillating behaviour. The cause of the increasing

trend observed for mannitol remains still unclear. The trend might be attributed to the diffusion

of this metabolite, produced by the microorganisms [23], from the pulp to the bean or the

reduction of fructose in acid conditions and elevated temperatures.

A different trend was observable for galactinol, myo-inositol and scyllo-inositol. Galactinol

concentration decreased until it disappeared between the range of 48-96 hours after

fermentation start. Also, a decrease in concentration was observed for myo-inositol and scyllo-

inositol (average diminution of 4% and 22% respectively).

Additionally, a sum parameter adding up all experimental LMWC concentrations was

determined, revealing an average decrease of 54% at the end of fermentation. This trend is in

line with the data reported elsewhere [24].

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141

Figure 1. Graphical representation of the LMWC content in cocoa beans with respect to the time (hours) of the different fermentation process under study.

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142

3.2. Contextual data changes in lipid content, pH and dry matter

The values of lipid content, pH and dry matter of all samples are shown in Table 2S

(supplementary information). The composition of lipids in unfermented cocoa beans were in

the range of 33.5-40.8%. At the end of the fermentation, the lipid content was in the range of

33.2 - 37.4%.

The pH determined in unfermented cocoa beans was in the range of 6.5 - 6.8. In all cases, during

the fermentation, the pH dropped to values in the range of 4.3-5.2.

The dry matter content during the spontaneous fermentation decreased slightly from

unfermented cocoa beans (range 65.7-68.4%) to the end of fermentation (range 56.9-60.2%).

3.3. Chemometric approach

The data collected from carbohydrates (average quantities and areas of unknown

carbohydrates), lipids, dry matter and pH were subjected to PCA to unravel key chemical

differences during the time-resolved points in the spontaneous fermentation process of cocoa

beans.

Figure 2. PCA score (left) and loading plot (right) from LMWC profile data, dry matter content, pH values and

lipid content.

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143

Figure 2 shows the score and loading plots obtained. In PC1, according to the score plots, a

resolution of samples is observed according to the fermentation time, being located the

fermentation starting point on the left and ending point on the right. The individual time series

from each fermentation are connected by different coloured lines. Mainly, the distribution on

PC1 could be considered to demark a separation among cocoa samples fermented up to 48 hours

(located in positive PC1 values) and samples fermented longer than 96 hours (located in

negative PC1 values). Cocoa samples fermented for 72 hours were distributed around the 0

value in PC1. The information obtained from this distribution can be interpreted as the absence

of significant changes in the LMWC profile and pH during the first 48 hours of the spontaneous

fermentation. The time interval ranging from 48 hours to 96 hours could be considered as the

period in which the fermentation process produces significant changes in the LMWC profile.

In all cases, after 96 hours, the changes produced in the LMWC and pH were notably

significant. This interpretation was in line with the information previously reported for proteins,

where the proteolysis process became evident after 72 hours and concerned peptides

particularly with higher molecular weight ( >15 kDa) [25].

On the other hand, the distribution observed on PC2 reflects the country of origin (score plot of

19%) indicating, for example, a clear separation of the Ecuador and Ivory Coast cocoa samples

from the rest of the samples under study.

The loading plots evidence the influence of different variables to characterize samples

fermented during a period lowest of 48 hours and highest of 96 hours. According to the figure

2, different variables such as carbohydrates (di-, tri-, tetrasaccharides and pentosyl-

iminosugars), DM content, pH value and lipid content could be used to characterize samples

fermented during a period shorter than 48 hours. Also, other variables such as monosaccharides,

mannitol and glycosyliminosugars could be used to describe cocoa beans fermented during a

period longer than 96 hours.

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144

3.4. Kinetic approach

Different mechanisms responsible for the LMWC changes during fermentation were tentatively

investigated in this study. Three pathways could describe the LMWC changes reported. The

first pathway could be an enzymatic mechanism, as an example, the enzymatic hydrolysis of

oligosaccharides by glycosidases. A second pathway of the changes described could be purely

chemical hydrolysis catalyzed by the acidic primary fermentation products such as lactic and

acetic acid. The last pathway that requires consideration is the reaction of reducing

carbohydrates with free amino groups from amino acids and oligopeptides to yield Amadori

compounds via Maillard reaction.

In this context zero order kinetic reactions might correspond to reactions catalyzed by a low

concentration of Michaelis-Menten enzymes. Reactions of acid hydrolysis might follow a first-

order kinetic model, although an enzymatic mechanism catalyzed by enzymes following a first-

order kinetic model should not be discarded. Maillard reactions might follow a second-order

kinetic model.

The determination of the relation between pH and LMWC content, as well as the reaction order,

rate constant and half-time value of the LMWC changes will allow distinguishing the diverse

possible mechanisms.

3.4.1. Pearson correlation between pH and LMWC content.

Previous studies have shown the influence of pH in the hydrolysis of model oligosaccharides

[26]. According to this information, a Pearson correlation analysis between the values of pH

and the LMWC content in each stage of the five spontaneous fermentation processes under

study was performed. The values of the Pearson Coefficient Correlation and the p-values are

shown in Table 1.

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145

Table 1. Pearson coefficient values determined between the LMWC determined and the pH values

Compound Pearson’s Coefficient p-value

Fructose -0.4 0.01131329

Glucose -0.6 0.00012526

Mannitol -0.4 0.02450085

myo-Inositol -0.2 0.36140542

scyllo-Inositol -0.1 0.50267047

Sucrose 0.6 0.00008333

Melibiose -0.5 0.00571380

Galactinol 0.6 0.00003903

1-kestose 0.6 0.00002951

Raffinose 0.8 0.00000000

Stachyose 0.7 0.00000047

Total 0.4 0.01414849

The degradation of myo-inositol and scyllo-inositol and the formation of mannitol were not

correlated with the values of pH (Table 1). The degradation of galactinol during the spontaneous

fermentation showed a correlation with the pH values (PCC = 0.62, p-value< 0.005).

Except for melibiose, the rest of di-, tri-, tetrasaccharides showed a correlation with the pH

values. It is worth mentioning the strong correlation determined between the content of

raffinose and stachyose with the pH values (PCC = 0.83 and 0.74, p-value < 0.005 respectively).

Regarding the monosaccharide content, fructose showed a weak negative correlation with the

pH values. However, a considerable negative correlation was observed between the increment

of the glucose+galactose content and the drop in pH values during the spontaneous fermentation

(PCC = -0.61, p-val< 0.005). This negative correlation might be tentatively attributed to the

increased monosaccharide content, consequence of putative hydrolysis of disaccharides and

oligosaccharides observed during the fermentation.

During the spontaneous fermentation, the total carbohydrate content, calculated as the sum of

all LMWC, showed a weak correlation with respect to the drop in pH values (PCC= 0.41, p-

value = 0.01). This weak correlation might be attributed to the fact that this parameter is the

sum of the different LMWC considered, showing each compound different reaction patterns.

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146

3.4.2. Reaction order.

The different r2 and AIC values of the fitted data to the zero, first and second kinetic models

are shown in Table 2. According to Akaike [18], minimum AIC values allow the best choice of

the fit in a set of data.

The reactivity of polyols during fermentation followed different kinetic orders. Mannitol, a

compound tentatively formed during the fermentation, showed similar values of r2 for the tested

orders. However, the AIC indicated that the formation of mannitol might follow a zero order

kinetic. The data showed for scyllo-inositol degradation an evident zero order kinetic. In the

case of myo-inositol, the values of r2 for zero and first kinetic order were similar (0.74 and 0.76

for zero and first order kinetic respectively). However, the negative AIC values indicated a

better fitting of the data to an equation of zero-order kinetic. Regarding galactinol, the data

indicated a first-order kinetic to describe the degradation of this compound.

Table 2. Different r2 and AIC values determined for the fitting of the different LMWC data to the corresponding

equations of the different reaction order.

Compound

Order 0 Order 1 Order 2

R2 AIC R2 AIC R2 AIC

Fructose 0.38 127.23 0.30 77.88 0.13 59.79

Glucose 0.40 146.08 0.44 79.12 0.36 44.23

Mannitol 0.66 73.77 0.71 112.57 0.47 298.03

myo-Inositol 0.74 -12.07 0.76 29.34 0.76 81.80

scyllo-Inositol 0.73 -76.78 0.70 53.73 0.55 210.57

Sucrose 0.65 164.29 0.68 104.55 0.19 217.30

Melibiose 0.40 -93.28 0.28 128.89 0.06 528.03

Galactinol 0.38 3.61 0.75 78.31 0.46 414.00

1-kestose 0.63 -61.08 0.55 80.98 0.42 270.26

Raffinose 0.72 81.03 0.80 78.27 0.29 223.14

Stachyose 0.82 66.52 0.78 44.07 0.52 76.82

Total 0.75 202.32 0.73 40.88 0.54 -82.85

RESULTS

147

The disaccharides sucrose and melibiose followed different order kinetics. The data indicated

a first-order kinetic for the degradation of sucrose and an order zero-order kinetic for the

degradation of melibiose.

Regarding the kinetics order for trisaccharides in cocoa beans during fermentation, different

trends were observed. The degradation of 1-kestose during fermentation suggested a clear zero-

order kinetics (r2 = 0.63 and AIC = -61.08). On the other hand, according to Table 2, the

degradation of raffinose followed an unequivocal first-order kinetic (r2 = 0.8 and AIC = 78.27).

Degradation of stachyose during spontaneous fermentation did not show a considerable

difference between the r2 values (Table 2). However, the lower AIC suggests a first-order

kinetic for the degradation of this compound.

According to Table 2, the monosaccharides (fructose and glucose+galactose) did not offer any

clear fitting with any of the reaction order evaluated. This behaviour might be attributed to an

increase of the concentration consequence of putative hydrolysis of di-, oligosaccharides and a

decrease in the content consequence of a putative reactivity of monosaccharides in Maillard

reaction to produce Amadori compounds [7].

Regarding the total LMWC content, the results indicated a first-order kinetic (r2 = 0.73 and AIC

= 40.88) to describe the diminution of the total content during spontaneous fermentation.

Overall, the fitting of the quantitative data from sucrose, galactinol, raffinose, stachyose during

spontaneous fermentation to the first-order kinetics is consistent with the higher PCC values

determined for these compounds with the pH (PCC > 0.6). The comparison of this fitting results

with the reaction order determined in model studies showed similarity with the data reported in

hydrolysis models of sucrose and fructooligosaccharides (n=2-4) in acid conditions [246]. On

the other hand, except for 1-kestose, the fitting of mannitol, myo-, scyllo-inositol and melibiose

to zero order kinetics is in agreement with the lower correlation with pH determined (PCC <

0.6).

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148

3.4.3. Rate constant and half-time value.

Table 3 lists the rate constant (kobs) and half-life values (t1/2) determined for each of the

compounds. The t1/2 were calculated according to the best fitting order reaction determined

previously. As the compounds fructose and glucose+galactose did not follow any of the reaction

order evaluated, the parameters kobs and t1/2 were not determined for these compounds.

Regarding kobs and t1/2 of polyols in cocoa beans during fermentation, myo- and scyllo-Inositol

showed the lowest kobs (2.2 ± 4.1 and 1.2 ± 1.6 mg/ h respectively) and highest t1/2 values

(1260.1 and 688 h respectively ) of all the compounds. These results suggest a low reactivity of

these carbohydrates during the spontaneous fermentation. Conversely, galactinol showed the

highest kobs value (565.6 h-1) and the lowest t1/2 (12.2 h) of all compounds under study. In the

case of mannitol, this compound showed the highest kobs and lowest t1/2 of all compounds

assigned as zero order reaction.

The values of kobs and t1/2 determined for sucrose and raffinose were similar. In the case of

stachyose, the kobs and t1/2 values were lower and higher respect to the above compounds. Values

of raffinose and stachyose were similar to the values reported in a model system of sucrose

hydrolysis at pH 4 and temperature of 80 °C [22].

In the case of melibiose, lower kobs and high t1/2 values were determined in comparison to the

degradation of sucrose, raffinose and stachyose or the values determined for disaccharides in

hydrolysis models determined by L'Homme, Arbelot, Puigserver and Biagini [22].

Regarding 1-kestose, the lower values of kobs and higher t1/2 determined might indicate a

different degradation pathway to the mechanism of sucrose, raffinose and stachyose.

Regarding total LMWC content, the results from Table 3 showed a kobs of 59.9 ± 9.5 h-1 and a

t1/2 of 115 h, indicating that the content of LMWC decreases to a half-content after 4.8 days of

fermentation. Except for myo-and scyllo-inositol, all experimental t1/2 values are in the range of

the total fermentation time.

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149

Table 3. Rate constant (kobs) and half-life values (t1/2) determined for each of the compounds.

Compound

Order 0 Order 1

K average ± SD ( mg / h*10-4) t 1/2 (h ) Compound K average ± SD ( 1/h *10-4) t 1/2 (h )

Mannitol 58.7 ± 18.7 23.0 Sucrose 235.4 ± 34.4 29.4

myo-inositol 2.2 ± 4.1 1260.2 Galactinol 565.6 ± 75.7 12.3

scyllo-inositol 1.2 ± 1.6 688.1 Raffinose 231.9 ± 20.4 29.9

Melibiose 2.2 ± 1.2 62.9 Stachyose 127.4 ± 11.4 54.4

1-kestose 11.2 ± 1.9 73.1 Total 59.9 ± 9.5 115.7

4. CONCLUSION

The present manuscript has shown for the first time an absolute quantification of the main

LMWC during the spontaneous fermentation, crucial step for the development of volatile

precursors defining the characteristic aroma of chocolate.

This study has evaluated the spontaneous fermentation process from five different countries

and different hybrid cultivar. In all cases, it is worth mentioning the remarkably gradual

decrease in the concentration of di-, tri- and tetrasaccharides during the spontaneous

fermentation.

The chemometric approach employing the LMWC data, DM, lipid content and pH has proved

to be a suitable combination able to track the different stages of the cocoa bean fermentation.

This study has reported for the first time a kinetic study to establish the reaction order, kobs and

t1/2 of the different LMWC during the spontaneous fermentation of cocoa beans.

According to the data reported, the fit of sucrose, galactinol, raffinose and stachyose to first-

order kinetic model and the high correlation of these compounds with the pH values might

suggest either an acid hydrolysis mechanism or an enzymatic mechanism of first-order kinetic

model tentatively. Conversely, the fit of mannitol, myo-,scyllo-inositol, melibiose and 1-kestose

to zero order kinetic model and the low correlation of these compounds with the pH values

might suggest tentatively an enzymatic mechanism to describe the changes of this compounds

This study provides useful information for a better understanding of the multiplex reaction in

the cocoa beans during the spontaneous fermentation.

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150

5. ACKNOWLEDGEMENTS

The authors would like to mention their gratitude towards Mrs Anja Müller for her assistance

during the measurements of the samples.

This work was conducted in the framework of the COMETA Project, which is financially

supported by Barry Callebaut AG.

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151

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degradation of fructo-oligosaccharides, European Food Research and Technology, 228 (2009)

355-365.

153

Part 2 – LMWC in commercial green tea and kale

(chapters 7 and 8)

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155

Chapter 7. Characterization of commercial green tea leaves by the analysis of low

molecular weight carbohydrates and other quality indicators.

Roberto Megías-Pérez , Anastasiia Shevchuk, Yeweynwuha Zemedie, Nikolai Kuhnert

Manuscript published in Food Chemistry Volume 290, 30 August 2019, Pages 159-167



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ABSTRACT

A comprehensive characterization of commercial green tea (CGT) with the employment of

independent variables such as low molecular weight carbohydrates (LMWC), soluble solids,

color and antioxidant capacity has been performed in this manuscript.

Within the manuscript, a convenient HILIC-MS method, suitable to perform a simultaneous

identification and quantification of all mono-, di-, oligosaccharides and cyclitols observed in

green tea was introduced. The method covers all key analytes in a single chromatographic

analysis.

Fifty-six samples from different origins (n = 10) were evaluated to explore differences based on

origin. In addition, commercial samples processed by pan-firing and steaming were used for

comparative purposes, allowing the identification of putative processing markers.

The results obtained contribute to gain a better knowledge of the variations, according to origin

and processing, in composition and quality of CGT, commodity widely appreciated by the

consumers.

keywords: green tea, HILIC-ESI-TOF MS, carbohydrates, quality parameters, multivariate

statistical analysis.

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1. INTRODUCTION

Commercial green tea (CGT), the processed leaves of Camellia sinensis (L.) O. Kuntze, is a

commodity with growth prospects of 7.1 per cent annually, expected to reach 750,981 tons by

the year 2023. This projection highlights the economic relevance of this product for tea-

producing countries [1].

The interest of consumers for this commodity might be attributed to the several health beneficial

effects associated with a regular consumption, such as protection against cancer, cardiovascular

diseases, oral health, regulation of body weight and improvement of cognitive performance

[2,3]. Different plant secondary metabolites as caffeine, theanine and catechins, have been

identified to be involved in the diverse health-promoting effects reported [3].

The production of commercial green tea (CGT) starts with the step of plucking green tea leaves

from the plant, followed by withering. Withering involves spreading the tea leaves over bamboo

mats or other surfaces for a short period of time (between 1 and 3 h), with a consequent average

moisture loss of 30% [4]. Thereafter, the fixing step is carried out to inhibit the polyphenol

oxidase (PPO) and peroxidase (POD) enzymes, involved in the formation of black tea [5].

Although there is no universal specific procedure in the tea industry, pan-firing and steaming

are the most standard fixing processes employed. Both processes produce on average a 40%

moisture loss in a short period of time (10–15 min), but, differ in the temperature applied [5].

In the pan-firing process, commonly employed in the Chinese and South Korean tea industry,

heat is applied through a warm and dry pan exposed to a high-temperature source of about

180 °C. In the steaming process, commonly employed in the Japanese tea industry, heat is

applied through water steam (Ahmed and Stepp, 2013, Xu and Chen, 2002). The manufacturing

of CGT is completed with the rolling and drying steps to inhibit the microbial growth and thus

increase the shelf life of the product [4,5].

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158

The quality of CGT is determined through different sensory properties such as color, taste and

aroma. However, other parameters, such as leaf size, soluble solids, antioxidant capacity and

caffeine content, have been proposed to evaluate the green tea quality [6,7]. Mainly, the

parameters proposed to evaluate green tea quality are related to its chemical composition. Green

tea chemical composition is affected by the tea leaf processing and by other factors such as

botanical variety (Sinensis, Assamica), climate, season and development degree of the leaf [5].

On average, the estimated chemical composition of green tea in terms of dry weight (DW) is

30% phenolic compounds, 26% fibre, 15% proteins, 7% carbohydrates, 7% lipids, 5% minerals,

4% free amino acids, 2% pigments and 4% of other compounds such as organic acids or caffeine

[2].

Regarding the low molecular weight carbohydrates (LMWC), scarce information has been

reported. Data on LMWC can contribute to a better understanding of the chemical composition,

reinforcing the perception that consumers have about green tea as a health-promoting beverage.

In addition, for the industry, LMWC knowledge could mainly contribute to the identification

of tea quality markers and the elucidation of the chemical reactions taking place during the

processing of green tea leaves into commercial black tea [8].

In terms of composition, few studies using a limited number of samples have reported the

identification and quantification of common LMWC such as fructose, glucose, sucrose and

maltose [9-11]. Moreover, the presence of α-galactooligosaccharides (α-GOS) such as

raffinose and stachyose has been reported. These compounds are well-known for their prebiotic

properties [12,13]. Also, different cyclitols such as 2-O-(β-l-arabinopyranosyl)-myo-inositol,

galactinol and myo-inositol have been reported in green tea. Some of these compounds have

medical uses, for example, myo-inositol is known for its use in infertility and mental disorder

treatments [14,15].

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In terms of tea quality, the compound 2-O-(β-l-arabinopyranosyl)-myo-inositol has been

described as an indicator of high-quality in Chinese teas elaborated from young tea shoots, a

part of the plant considered to produce teas with high quality [16]. On the other hand, the

LMWC content has a role in the sensory perception, contributing to the green tea sweetness

[17].

Based on the relevance of the information provided by LMWC, the determination of LMWC is

scientifically justified. The analytical methodologies most commonly employed to analyze

LMWC in CGT are liquid chromatography (LC), gas chromatography (GC) and Nuclear

Magnetic Resonance (1H NMR) [16].

Previous studies on CGT dealing with the characterization of commercial samples or the

evaluation of the processing conditions have employed highly dependent parameters, such as

the combination of chemical parameters (polyphenol profile) and quality indicators (antioxidant

capacity) [18,19]. These approaches provide limited information in terms of characterization,

since the changes produced in one parameter directly affect the dependent parameters.

Consequently, this manuscript reports an approach to characterize commercial green tea

through the analysis of different parameters including LMWC, color, soluble solids and

antioxidant capacity. With respect to previous studies on characterization of CGT, this approach

has the novelty of the employment of independent parameters. In addition, this approach

provides in-depth knowledge of LMWC from CGT, information not previously addressed

exhaustively in scientific literature.

While the methods for the analysis of soluble solids, color and antioxidant capacity are

techniques routinely employed, the different methods previously reported for LMWC analysis

present as limitation not to offer a simultaneous identification and quantification within the

same chromatographic analysis for all mono-, di-, oligosaccharides and cyclitols described in

tea. This manuscript introduces a convenient chromatographic method employing hydrophilic

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160

interaction liquid chromatography coupled to mass spectrometry (HILIC-MS) to solve this

limitation. The selection of HILIC is based on the absence of tedious sample preparation,

appropriate resolution, good peak shapes and easy coupling to MS detectors.

This approach has been applied to an extensive set of samples (n = 56) from different origins

corresponding to the main tea producing countries (n = 10). This selection of samples aims to

explore possible differences in function of the origin. For comparative purposes, commercial

samples processed by pan-firing and steaming were used, allowing the identification of putative

markers of processing.

2. MATERIAL AND METHODS

2.1. Chemicals

LC–MS grade acetonitrile (ACN) was purchased at AppliChem Panreac (Darmstadt,

Germany). Ammonium hydroxide solutions, Asp-Phe methyl ester (used as internal standard in

HILIC-ESI-TOF MS analysis), fructose, glucose, sucrose, maltose, myo-inositol, sucrose,

raffinose, stachyose, gallic acid (GA), iron oxide hexahydrate, 2,4,6-Tris(2-pyridyl)-s-triazine

(TPTZ), 6-Hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid (Trolox) were acquired

from Sigma Chemical Co. (St. Louis, USA). Galactinol was provided by Carbosynth (Compton,

UK). Mannitol was kindly donated by Bermpohl Apotheke (Bremen, Germany).

2.2. Tea samples

The different CGT samples (n = 56) were certified according to the origin using the information

provided in the label of the product purchased from different tea distribution companies, local

supermarket chains from Germany (Bremen and Cologne), Sri Lanka, Iran, Portugal and

factories in the countries of origin (South Korea). In all cases, the selection of the samples was

performed avoiding CGT blended with aromas.

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161

The origin of the CGT samples was as follows: Japan (n = 8), South Korea (n = 7), Iran (n = 6),

Nepal (n = 5), Sri Lanka (n = 6), China (n = 8) and Portugal (n = 3) and India (n = 13), covering

the different origins of the main tea-producing countries. Samples from India were further

classified into the three producing regions: Assam (n = 4), Darjeeling (n = 3) and South India

(n = 6). Samples from Portugal (n = 3) were fixed by steaming and most samples from South

Korea (n = 6) by pan-firing, according to the information provided by the manufacturers.

The particle size of the samples was determined manually with a standard metric ruler.

2.3. Extraction of LMWC and sample preparation

One gram of CGT was subjected to extraction with 100 mL of deionized water at a temperature

of 95 °C under constant stirring for 10 min [20]. Afterwards, 1.5 mL of the water extract was

collected and the rest was used for the analysis of quality indicators.

For LMWC analysis, the sample was subjected to two additional extractions with deionized

water. The water extract collected in each extraction were pooled.

For quality indicators, the collected water extract was filtered through a Whatman 45 paper

filter (Omnilab, Bremen, Germany). Unless otherwise specified, all the determinations of the

different quality indicators were performed in less than 6 h after the extraction.

2.4. Analysis of LMWC using HILIC-ESI-TOF MS and HILIC-ESI-MSn

Two mL of the pooled water extract was filtered through a nylon filter (pore size of 0.45 μm)

(Macherey-Nagel, Düren, Germany). For chromatographic analyses, 10 µL of internal standard

(solution of 1 mg mL−1 of Asp-Phe methyl ester) was added to one mL of the filtered sample.

The chromatographic analyses were performed using an Agilent 1100 Series HPLC (Agilent

Technologies, Karlsruhe, Germany). The column employed was a BEH X-Bridge amide

column (Waters Company, USA), with a trifunctionally-bonded amide phase

(150 mm × 3.0 mm; 3.5 μm particle size and 135 Å pore size). The composition of the mobile

phase was water (solvent A) and acetonitrile (solvent B), both solvents with ammonium

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162

hydroxide (0.1% v/v). The elution gradient was: 22% A in 0–5 min. 22–40% A in 5–30 min

followed by a re-equilibration of 13 min using the initial solvent composition. The analyses

were carried out at a constant temperature of 30 °C using a flow rate of 0.4 mL min−1. Injection

volume was set to 3 µL.

A microTOF mass spectrometer fitted with an ESI ion source (Bruker Daltonics HCT Ultra,

Bremen, Germany) operating in positive ion mode (range of 50–1200 m/z) was used to perform

the identification of the molecular formula and the quantitative analysis of target analytes.

Internal calibration of the mass spectrometer was carried out before starting the sequence run

by injection of 0.1 M sodium formate solution. Additionally, sodium formate solution was

injected automatically through a six-port valve prior to each chromatographic run to perform a

posterior calibration. The ESI source parameters were adjusted according to the conditions

published elsewhere [21]. Data acquisition was performed using HyStar 3.2 software (Bruker,

Bremen, Germany).

The presence of LMWC was corroborated using an Ion trap mass spectrometer fitted with an

ESI source (HCT Ultra, Bruker Daltonics, Bremen, Germany) (HILIC-ESI-MSn) operating in

positive targeted ion mode (range of 50–1200 m/z). The ESI parameters were adjusted

according to the conditions published previously [22]. Data acquisition was performed using

Agilent ChemStation software (Agilent, Karlsruhe, Germany).

Calibration curves were determined using the normalized area of the Extracted Ion

Chromatogram (EIC) from the different standards with respect to the area of the internal

standard. Signal to noise ratio (S/N) values of three and ten were used as criteria to establish

the limit of detection (LOD) and quantification (LOQ). Quantities of 2-O-(β-l-

arabinopyranosyl)-myo-inositol were determined using the calibration curve from galactinol.

RESULTS

163

Matrix effect for each LMWC was evaluated as the average of the recovery determined after

the addition of two different LMWC quantities to the carbohydrate extract of green tea. The

recovery for each quantity of standard was carried out in triplicate.

The repeatability of the method was evaluated through intra-day precision and inter-day

precision. Inter-day precision was estimated as the RSD average of the LMWC concentrations

obtained from 5 samples injected in three different days each. Intra-day precision was

determined as the RSD average of the LMWC concentrations determined in three different

samples injected five times each, within the same day.

The evaluation of the reproducibility of the whole method (extraction of LMWC and

chromatographic separation) was based on the average RSD value of each target compound

measured on randomly selected samples performed in duplicate (n = 9) and triplicate (n = 5) of

sample preparation.

2.5. Analysis of quality indicators

2.5.1. Soluble solids

The measurement of the soluble solids was performed in triplicate, following this procedure:

2 mL of the water extract was transferred into a vial and dried overnight in an oven at a constant

temperature of 110 °C. The soluble solids were determined using the formula below described

previously [23]:

Soluble solids (%) =(D1 − D0) × V0 × 100

V1 × W1

D1= mass of empty vial + dried tea extract, D0 = mass of empty vial, V0 = initial volume of tea

sample (100 mL), V1 = final volume of tea used to solid extract analysis (2 mL), W1= initial

mass of tea sample (1 g).

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164

2.5.2. Analysis of color

The measurements of the color parameters L*, a* and b* were performed in duplicate. The

colorimeter employed was a Konica Minolta CM-3500d colorimeter (Liechtenstein, Germany)

calibrated with specific black and white color reference. These measurements were carried out

using Illuminant D65 and 10° observer angle. A volume of 10 mL of the water extract was used

for the measurements.

The L*, a* and b* parameters represent the lightness (0 = black, 100 = white), the redness ((−)

green to (+) red) and the yellowness ((−) blue to (+) yellow) respectively. The color parameters

chromaticity (C*), saturation (S*) and hue angle (H°) were determined as follows:

C* = (a*2 + b*2)0.5, S* (S* = C*/L*) [24] and (H° = arc tan (b*/a*) + 180°) [25].

2.5.3. Analysis of antioxidant capacity

The method employed is an adaptation of the method reported by Benzie and Strain [26]. FRAP

reagent was prepared by mixing 5 mL of TPTZ solution (10 mM in 40 mM HCl), 5 mL of

aqueous FeCl3·6H2O solution (20 mM) and 40 mL of 0.5 N acetate buffer (pH = 3.6).

Stock standards of Trolox (1.25 mg/ mL) and GA (0.5 mg/ mL) were prepared in 70% methanol.

Different dilutions of the stock standards with H2O were carried out to obtain standards in a

range of 0 – 0.25 mg/mL. The water extract was diluted with H2O in a ratio 1:5 for the

measurement.

FRAP reagent (200 µL) was added to 10 μL of the diluted water extracts and standards into a

96-well plate. Reactions were incubated in the dark for 10 min and absorbance was measured

at 593 nm on a 96-well Biochrom EZ Read 2000 microplate reader (Cambridge, UK). Each

measurement was performed at least in duplicate. Antioxidant capacity was expressed in terms

of gallic acid equivalent (GAE) (g GAE/g tea) and Trolox equivalent (TE) (g TE/g tea).

RESULTS

165

2.6. Data analysis and statistics

Peak area values of the different LMWC were extracted using Quant Analysis software (Bruker,

Bremen, Germany).

Kruskal Wallis test followed by Benjamin Hochberg post hoc test was performed using

GraphPad Prism 7.0 software (San Diego, California, USA).

Values of the LMWC content, color parameters, antioxidant capacity and soluble solids from

the 56 commercial samples under study were subjected to chemometric evaluation. The

variables were auto-scaled (transformation into z-scores, z = x − median/SD) to standardize the

statistical importance of all variables [27].

Hierarchical cluster analysis (HCA) (linkage: Ward’s method, distance measure: Pearson) and

principal component analysis (PCA) were performed using Metaboanalyst 4.0 [28].


3.1. Analysis of LMWC using HILIC-ESI-TOF MS and HILIC-ESI-MSn

A standard chromatographic method previously reported [21] was used to identify the main

LMWC described previously in tea (fructose, glucose, myo-inositol, sucrose, maltose,

galactinol, raffinose and stachyose). The identification of the target LMWC was performed

using the high-resolution mass data of the sodium adduct, the retention time (tR) and tandem

MS fragmentation data of the different peaks compared to their corresponding commercial

standards. The mass error detected was below 5 ppm.

Other uncommon LMWC were also identified. The first compound identified was 2-O-(β-l-

arabinopyranosyl)-myo-inositol (tR = 16.5 min, m/z 335.0958) a compound previously reported

by Sakata, Yamauchi, Yagi, and Ina (1987) [29], identified as a quality indicator of tea [16] and

related to early stages of tea leaf development [30]. The fragmentation of this compound, with

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166

a molecular formula of C11H20O10Na, showed a base peak of m/z 203, corresponding to the

neutral loss of 132 Da (pentosyl group). Fig. 1S (supplementary information) shows the HILIC-

ESI-MS2 spectra of this compound in positive ion mode.

The second compound identified (tR = 18.4 min, m/z 335.0951) showed a molecular formula of

C11H20O10Na. The fragmentation of this compound was not achieved due to the low peak

intensity. Based on the similarity in tR and molecular formula with respect to the aforementioned

compound, this peak was tentatively identified as unknown inositol with a structure similar to

2-O-(β-l-arabinopyranosyl)-myo-inositol.

The last compound (tR = 7.3 min, m/z 205.068, molecular formula C6H14O6Na) was positively

identified as mannitol considering the retention time and the fragmentation pattern of the

corresponding standard.

After the identification, different gradient conditions were evaluated to reduce the time for each

chromatographic analysis. Among the different conditions evaluated, the chromatographic

conditions mentioned in section 2.5 (22% A in 0 – 5 min; 22 – 40% A in 5 – 30 min and 22%

A in 30–43 min) were considered suitable based on an adequate chromatographic separation of

the different LMWC within the first 30 min and the narrow peaks observed (peak width at half

height (wh) of the different peaks was in the range of 0.2–0.5 min). Fig. 1 shows the EICs of

the different LMWC identified under the optimized conditions of HILIC-ESI-TOF MS.

3.1.2. Analytical parameters of HILIC-ESI-TOF MS

Analytical parameters of the chromatographic method are shown in Table 1. The different

calibration curves obtained using reference standards showed satisfactory R2 values (higher

than 0.99), confirming the linearity of the calibration curves.

The recovery data obtained from the evaluation of the matrix effect for each LMWC under

study were in the range of 92.7–103.7%, suggesting the absence of matrix effect on the

quantitation of LMWC in CGT.

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167

Figure 1. Extracted ion chromatograms (m/z 203, 205, 335, 365, 527 and 689) obtained using HILIC-ESI-TOF MS of commercial green tea. Peak

numbers: 1) Fructose, 2), Glucose, 3) Sucrose, 4) Maltose 5) myo-Inositol, 6) 2-O-(β-L-arabinopyranosyl)-myo-inositol, 7) Unknown inositol, 8)

Raffinose, 9) Galactinol, 10) Stachyose

5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 25.0 27.5 Time [min]

Intens.

11

10

9

1

3

2

4

5

6

7

8

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168

Individual recovery values for each LMWC are shown in Table 1S (supplementary

information).

Regarding LOQ and LOD values, the highest and lowest values of both parameters were

determined for stachyose (2.25 and 0.75 µg mL−1 respectively) and galactinol (0.20 and

0.06 µg mL−1 respectively).

In terms of precision, suitable intra-day (range 2.95–6.53%) and inter-day (range 4.70–7.44%)

values were determined for the target LMWC analyzed.

The evaluation of the reproducibility of the entire method (extraction of carbohydrates and

chromatographic separation), determined as the average RSD values of the samples determined

in duplicate (range 3.4–13.4%) and in triplicate (range 5.2–11.3%), indicated low variability in

the measurement associated with the LMWC extraction method.

The different analytical parameters, the absence of matrix effect and the reproducibility of the

method confirmed the suitability of the reported HILIC-ESI-TOF MS method for quantitative

purpose.

Overall, in comparison with other methods previously reported for the LMWC analysis of CGT

[9,10], the method requires a minimum sample preparation and allows the simultaneous

identification and quantification of all LMWC identified in tea.

3.1.3. LMWC analysis of commercial green tea samples

Table 2 lists the average values of each LMWC from the different CGT analyzed grouped

according to the origin, the differences among the different origins (significance, p < 0.05

determined with the Kruskal Wallis test) and the differences between pairs of countries

(significance, p < 0.05 determined with Benjamin Hochberg post hoc). The variability observed

among the different origins for fructose, glucose, sucrose and 2-O-(β-l-arabinopyranosyl)-myo-

inositol might be attributed to the diversity in origin and processing of the different tea leaves

evaluated..

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Table 1. Analytical parameters of the HILIC-ESI-TOF MS proposed

n* = number of samples analyzed. For intra-day precision, each sample was injected five times on the same day.

For inter-day precision samples were injected in three different days.

.

Calibration curve

R2 Linear

working

range

(µg mL-1)

L.O.Q

(µg mL-1)

L.O.D

(µg mL-1)

Intra-day

Precision

(% RSD)

(n*=5)

Inter-day

Precision

(% RSD)

(n=5)

Fructose

Glucose

Mannitol

Sucrose

Maltose

myo-Inositol

Galactinol

Raffinose

Stachyose

y = 0.0208x + 0.0192

y = 0.0268x + 0.0362

y = 0.0328x + 0.0313

y = 0.025x + 0.0245

y = 0.011x + 0.0136

y = 0.0123x + 0.031

y = 0.0204x + 0.049

y = 0.0171x - 0.0378

y = 0.0088x - 0.019

0.9981

0.997

0.9947

0.9968

0.9939

0.9967

0.9917

0.9866

0.9963

0.75 - 75

1.0 - 50

0.75 - 50

0.75 - 90

2.5 - 75

1.0 - 90

1.0 -50

0.75 - 90

2.5 - 75

0.75

0.60

0.25

0.25

1.50

0.75

0.20

0.75

2.25

0.25

0.20

0.08

0.08

0.50

0.25

0.06

0.25

0.75

4.65

5.11

5.09

4.62

5.45

6.53

5.41

5.02

2.95

7.44

5.04

6.91

5.81

5.21

6.56

6.92

5.39

4.70

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170

To the best of the author's knowledge, our manuscript reports for the first time quantitative data

on mannitol, stachyose, galactinol and 2-O-(β-l-arabinopyranosyl)-myo-inositol in CGT

Regarding monosaccharide composition, fructose (average content of 4.55 ± 3.55 mg/g tea) was

higher than glucose (average content of 3.61 ± 3.6 mg/g tea). The highest and lowest fructose

and glucose content were determined in samples from Portugal and South Korea. The

monosaccharide quantities were in the same range of magnitude as the fructose and glucose

values reported previously (4.1–6.7 and 3.7–14.4 mg/g tea respectively) by Moldoveanu et al.,

2015, Shanmugavelan et al., 2013 [9,10].

The average mannitol content was in the range of trace – 4.2 mg/g tea. These values of mannitol

were in the same order of values reported in coffee [31].

Regarding disaccharides, sucrose was the most abundant LMWC in CGT, with an average

content of 29.16 ± 19.61 mg/g tea. Maltose showed a lower content (range: trace – 2.54 mg/g

tea), detecting differences in the mean values between samples from Portugal and Sri Lanka.

These values were in the range of data previously reported in studies using a limited number of

samples, where sucrose and maltose showed values in the range of 7.1–20.5 and not detected-

0.26 mg/g tea respectively [9,10].

Regarding cyclitols, 2-O-(β-l-arabinopyranosyl)-myo-inositol showed the highest average

content (11.89 ± 8.97 mg/g tea) followed by myo-inositol (average content 4.28 ± 2.72 mg/g

tea) and galactinol (average content 1.87 ± 2.18). The content of myo-inositol was in the same

order of values reported for two CGT samples (values of 2.2 – 3 mg/g tea) by Moldoveanu et

al. (2015) [9].

Regarding α-GOS, raffinose content was higher (average 2.64 ± 1.30 mg/g tea) than stachyose

(average 1.00 ± 0.79 mg/g tea). Literature values of raffinose, 0.25 mg/g leave [11], were lower

in comparison with the values obtained in this study. The differences in content could be

attributed to the variety of tea leave studied, the controlled farming conditions and the analytical

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171

method employed (different extraction solvent with tedious sample preparation, including

incubation of the leaves in a buffer at 37 °C followed by washing with water).

3.1.4. Estimation of the caloric input in a green tea cup

The factors of 4 kcal/g for monosaccharides and disaccharides, 1,6 kcal/g for mannitol and

2.4 kcal/g for the cyclitols [32] were used to estimate the caloric input. The average recovery in

each sequential step of the extraction for six different samples was 91.9%, 7.7% and 0.4% for

the first, second and third extraction respectively. The recovery value of the first extraction was

used to perform the estimation of the caloric intake of a cup of green tea, resulting in 0.18 kcal.

This value demonstrates the low caloric input of green tea, supporting the recommendation of

the green tea consumption in weight-loss regimes [3].

3.2. Quality parameters of green tea: soluble solids, color parameters and antioxidant

capacity

Table 3 shows the average values of each quality parameters (soluble solids, color parameters

and antioxidant capacity) grouped according to the origin, the comparison among the different

origins (significant difference with p < 0.05 determined with the Kruskal Wallis test) and the

comparison between pairs of countries (significant difference with p < 0.05 determined with

Benjamin Hochberg post hoc). Individual sample values for each quality parameter are shown

in Tables 4S, 5S and 6S (supplementary information).Table 3 lists the average values of each

quality parameters (soluble solids, color parameters and antioxidant capacity) from the different

CGT analyzed grouped according to the origin.

3.2.1. Soluble solids

This quality indicator, employed in the tea industry (ISO-9768, 1994) [33], showed for the

samples under study an average value of 38.42 ± 6.94%. As shown in Table 3, Japan and Nepal

were the countries with the lowest and highest average soluble solids values (33.07 ± 6.01 and

50.44 ± 5.32 respectively).

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172

Table 2. Mean (standard deviation) values of each carbohydrate under study per country. Asterisk super index accompanying the carbohydrate name indicate significant differences

among countries according to Kruskal Wallis test. Similar letters between countries for the same LMWC indicate significant differences between two countries according to Kruskal

Wallis test followed by Benjamin Hochberg post hoc (p< 0.05 for both statistical test).

Mean (SD) mg /g tea

Japan

(n=8)

South Korea

(n=7)

Portugal

(n=3)

China

(n=8)

Iran

(n=6)

Nepal

(n=5)

South India

(n=6)

Darjeeling

(n=3)

Assam

(n=4)

Sri Lanka

(n=6)

Fructose* 6.28 (2.53)a,b 2.36 (2.62)a,c 11.99 (2.59)c,d 4.29 (2.99) 5.04 (1.64) 2.94 (0.68) 6.05 (5.17) 5.00 (3.41) 3.01 (0.68) 1.55 (0.59)b,d

Galactinol* 3.89 (3.67)a 0.18 (0.47)a,b,c 2.25 (1.39) 0.94 (1.10) 2.49 (1.37)c 0.53 (0.73) 3.06 (3.00)b 2.36 (2.08) 1.46 (0.67) 1.51 (0.91)

Glucose* 4.97 (2.60) 1.57 (2.30)a 9.59 (3.42)a 2.55 (1.85) 4.93 (2.98) 1.86 (0.85) 5.16 (4.38) 3.34 (0.78) 2.61 (0.50) 2.00 (1.51)

Inositol*,+ 10.57 (7.73) 12.56 (2.49) 10.43 (2.17) 19.02 (21.10) 8.00 (3.50) 12.64 (3.83) 9.96 (2.78) 14.53 (3.72) 10.96 (0.92) 8.59 (1.66)

Maltose* 0.46 (0.38) 0.36 (0.37) 1.80 (0.66)a,b 0.20 (0.16) 0.47 (0.24) 0.65 (0.51) 0.72 (0.42)c 0.46 (0.53) 0.12 (0.11)a 0.11 (0.17)b,c

Mannitol* 0.05 (0.07) 0.00 (0.00) 0.27 (0.08) 0.01 (0.03) 0.09 (0.21) 0.48 (0.45) 0.11 (0.27) 1.52 (2.39) 0.06 (0.05) 0.23 (0.39)

myo-Inositol* 3.66 (2.24)a 2.23(0.39)b,c,d 12.94(2.54)a,b,e 4.47 (1.60)c 3.91 (1.30) 4.52 (0.95)d 4.14 (1.37) 5.74 (4.12) 3.58 (0.64) 2.95 (1.01)e

Raffinose* 3.45 (1.43)a,b 1.34 (0.52)a,c,d 4.19 (1.32)c,e 2.84 (1.34) 3.25 (1.12)d,f 1.26(0.28)b,e,f 2.85 (1.19) 3.39 (1.57) 2.55 (0.48) 2.02 (0.50)

Stachyose* 1.55 (0.98)a 0.14 (0.36)a,b 1.47 (0.43) 0.69 (0.78) 1.25 (0.34) 0.40 (0.56) 1.68 (0.73)b 1.44 (0.76) 1.19 (0.09) 0.68 (0.53)

Sucrose* 35.24 (17.45) 19.01(4.54) 67.63 (37.45) 33.32 (18.88) 34.09 (31.77) 18.98 (3.03) 31.10(13.61) 23.42 (9.48) 19.72 (1.63) 18.96 (2.99)

Inositol+ = 2-O-(β-L-arabinopyranosyl)-myo-inositol

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Values in the range of 31.66–41.96 and 39.0–51.7% of soluble solids were reported for

commercial teas from Australia [23] and China from the Fujian province [24] respectively.

Under the conditions described in this manuscript, no significant differences were observed

among the differents CGT samples grouped according to the particle size (big > 0.5 cm,

medium: 0.3–0.5 cm, small < 0.3 cm). The absence of differences for this parameter was

previously observed in commercial Australian teas [23].

3.2.2. Color parameters

Color is used as a sensorial quality indicator in the tea industry. The parameters a* (average

value of −2.00 ± 0.65) and b* (average values of 4.05 ± 3.9) were in the same order as the data

previously reported [34]. The low values determined for L* (average value of 15.03 ± 2.26)

could be attributed to the use in this work of authentic white and black color reference standard,

when other authors used distilled water [24] or a standard white plate [34].

The H° values were in the range of 178.5–180.8°, values in concordance with the theoretical

H° value for the green color (H° = 180°) [25].

Despite the wide variability observed for the chromaticity C* and S* values (average of

5.13 ± 3.08 and 0.34 ± 0.18 respectively), the values were similar to the data previously reported

for CGT from China [24,35]. The variability observed could be attributed to different content

of chlorophylls in the infusion, consequence either of the processing or the origin of tea leaf

[36].

3.2.3. Antioxidant capacity

The antioxidant capacity is an indicator of the relative number of antioxidant compounds in

green tea. The variability observed for GAE (average value of 0.10 ± 0.05 g GAE/g tea) and TE

(0.25 ± 0.12 g TE/g tea) might be attributed to the compositional variability of the CGT from

different origins and the processing. These values showed good concordance with data

previously reported [37,35].

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Table 3. Mean (standard deviation) values of each quality parameter under study per country. Plus super index accompanying the quality parameter name indicate significant

differences among countries according to Kruskal Wallis test. Similar letters between countries for the same quality parameter indicate significant differences between two countries

according to Kruskal Wallis test followed by Benjamin Hochberg post hoc (p< 0.05 for both statistical test).

Country Soluble

solids+ (%)

Color Parameters Antioxidant capacity

L* a*,+ b*,+ C* Saturation Hue angle+ GA-E (g / g tea) Trolox-E (g / g tea)

Japan (n=8) 33.07 (6.01)a 15.81 (1.55) -2.67(0.45)a,b 1.16(2.00) 3.34(1.07) 0.21(0.06) 179.71(0.50) 0.09(0.04) 0.24(0.06)

S*.korea (n=7) 37.50 (3.76) 15.81(1.55) -1.6(0.83)a 2.35(3.52) 3.61(2.70) 0.23(0.17) 179.40(0.75) 0.10(0.04) 0.23(0.09)

Portugal (n=3) 36.88 (1.06) 13.19(1.64) -2.08(0.44) 7.03(0.57) 7.35(0.50) 0.56(0.07) 178.72(0.07) 0.05(0.02) 0.16(0.09)

China (n=8) 38.96 (6.28) 14.30(1.61) -1.86(0.46) 4.14(3.70) 5.23(2.50) 0.37(0.18) 179.22(0.81) 0.08(0.05) 0.27(0.15)

Iran (n=6) 33.83 (7.09)b 14.27 (3.15) -1.88(0.83) 5.96(4.30) 6.54(3.83) 0.45(0.20) 178.91(0.38) 0.09(0.04) 0.18(0.07)

Nepal (n=5) 50.44 (5.32)a,b 16.67(4.04) -2.03(0.70) 4.94(3.68) 5.77(2.83) 0.34(0.13) 179.03(0.62) 0.12(0.07) 0.27(0.08)

S*.India (n=6) 37.13 (5.14) 14.17(1.24) -2.35(0.56) 1.23(2.99) 3.34(2.07) 0.23(0.13) 179.82(0.68) 0.09(0.07) 0.31(0.21)

Darjeeling (n=3) 36.79 (9.86) 15.55(3.54) -1.74(0.58) 5.62(5.29) 5.96(5.20) 0.35(0.23) 178.86(0.24) 0.09(0.03) 0.20(0.04)

Assam (n=4) 40.95 (5.16) 14.74(2.67) -1.52(0.28)b 6.09(3.85) 6.31(3.78) 0.41(0.17) 178.73(0.16) 0.16(0.02) 0.42(0.24)

Sri Lanka (n=6) 41.70 (3.57) 15.14(1.47) -1.90(0.39) 6.25(4.56) 6.70(4.26) 0.43(0.23) 178.86(0.28) 0.08(0.05) 0.25(0.06)

*S. South

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No significant differences between countries were determined using a Kruskal Wallis test. From

the different countries under study, CGT samples from Assam showed the highest GAE and TE

values (0.16 ± 0.02 g GAE/g tea and 0.42 ± 0.24 g TE/ g tea respectively) and samples from

Portugal showed the lowest GAE and TE values (0.05 ± 0.02 g GAE/g tea and 0.16 ± 0.09 TE/g

tea respectively).

3.3. Multivariate analysis

3.3.1. Chemometric evaluation

Hierarchical cluster analysis (HCA) was applied with exploratory purposes, expecting a pattern

dependent of origin from the 56 samples under study. However, the analysis did not reveal any

trend according to the origin. Instead, HCA showed the presence of two main clusters (Fig. 2).

Cluster 1 contains 16 samples, including all the samples known to be processed by steaming

(n = 3, origin from Portugal). Cluster 2 includes 40 samples, including all the samples know to

be processed by pan-firing (n = 6, origin from South Korea).

The employment of t-test to evaluate the differences between cluster 1 and 2 revealed that

cluster 1 had a significantly higher content of fructose, glucose, sucrose, maltose, myo-inositol,

galactinol, raffinose and stachyose. In contrast, cluster 2 had significantly higher GAE, TE,

soluble solids content and high L* and a* values.

Based on the HCA results, the samples were divided into four groups: cluster 1-S, cluster 1-U,

cluster 2-PF and cluster 2-U. Cluster 1-S and cluster 2-PF included the samples with

information regarding the type of processing (1S: steaming and 2-PF: pan-firing). Cluster 1-U

and 2-U contained the samples with unknown manufacturing procedure distributed in cluster 1

and 2 respectively.

Kruskal Wallis test followed by Benjamin Hochberg post hoc test was employed to evaluate

similarities and differences among groups. Cluster 2-U and 2-PF were similar in 14 of the 19

variables under study, while cluster 1-U and 1-S were similar in 17 of the 19 variables (results

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176

are shown in Fig. 2S, 3S, 4S and 5S, Supplementary information). Moreover, the clusters 1-U

and 2-U differ in the following variables: a*, fructose, glucose, galactinol, 2-O-(β-l-

arabinopyranosyl)-myo-inositol, soluble solids, raffinose, sucrose, stachyose and TE.

Figure 2. HCA of the different samples under study considering the LMWC content, soluble solids, color

parameters, antioxidant capacity values data.

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177

Figure 3. PCA score (left) and loading plot (right) from LMWC content, soluble solids, color parameters,

antioxidant capacity values of the samples under study.

PCA was employed to evaluate the sample clustering. No separation was observed between

cluster 2-U and cluster 2-PF (Fig. 3). However, the cluster 1-U in the score plot overlaps with

the cluster 2-U and cluster 1-S. The variance explained by the first component of PCA was

30.1% while for the second component was 22.2%. The first component could be considered

as mainly demarcating the cluster classification according to the processing method.

Some of the variables observed in the loading plots (Fig. 3) defining the different clusters under

study could be tentatively explained based on differences in the tea processing. In the case of

fructose and glucose, samples from cluster 1-S and cluster 1-U showed higher content compared

to the values of the samples from cluster 2-U and cluster 2-PF. These differences might be

attributed to a significant decrease in the monosaccharide content in processes employing high

temperatures such as pan-firing. This hypothesis is based on the data reported for fructose and

glucose loss in processes employing steaming and roasting in other foods [37,39], which can

be considered comparable to the processes performed in the tea industry. However, further

studies should be carried out to confirm this hypothesis.

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Regarding soluble solids, samples from cluster 2-U and cluster 2-PF showed higher percentage

in comparison to the samples from cluster 1-U and cluster 1-S. High soluble solids values might

indicate a significant degradation of tea leaf structure as a consequence of elevated temperatures

during the processing.

Values of the parameter a* closer to zero in samples from cluster 2-U and cluster 2-PF might

indicate a significant degradation of the chlorophyll content in samples from these clusters, as

a consequence of the employment of higher temperature during the tea processing [36].

Another variable that characterizes samples from cluster 2-U and cluster 2-PF was 2-O-(β-l-

arabinopyranosyl)-myo-inositol. The presence of this indicator from earlier development stages

of the plant [30] in these groups of samples might suggest that the samples were manufactured

mainly from tea shoots. In the same way, low antioxidant activity would be expected for these

samples, since the polyphenol content in younger leaves is lower in comparison to older leaves.

However, higher GAE and TE values were detected in samples from cluster 2. These results

might be explained by the fact that antioxidant capacity is not specific to the polyphenol content,

but they can also measure the antioxidant capacity produced by other compounds such as the

Maillard reaction products [40]. Maillard reaction products, such as Amadori compounds, have

been previously identified during the manufacturing of green tea leaves [41].

Based on the results from Kruskal Wallis test and PCA, the similarities determined between

cluster 1-U and 1-S and cluster 2-U and 2-PF suggest a tentative employment of low

temperatures in the step of fixing for cluster 1-U samples (such as steaming process) and

elevated temperatures for cluster 2-U samples (such as pan-firing process) during

manufacturing. This tentative assignation is supported by the presence in cluster 1-U of 4

samples from Japan, country where the manufacturers employ mainly steaming procedure.

Likewise, cluster 2-U contained 5 samples from China, country known for principally using

pan-firing procedure in their industry [5].

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Although the chemical composition of green tea is affected mainly by the processing, other

factors such as variety (Assamica, Sinensis), environmental conditions (climate, growth

altitude) and farming conditions (the use of fertilizers or shading) could also affect the chemical

composition [5] and therefore, have influence in the distribution observed of the samples.

4. CONCLUSION

In this study, the combination of LMWC content, soluble solids, antioxidant capacity and color

parameters allowed a comprehensive characterization of an extensive and diverse sample set of

CGT from different origins. This comprehensive approach has not been previously reported in

tea science.

The method proposed for LMWC analysis solves the main limitation of other previous methods

that did not offer a simultaneous identification and quantification of the different LMWC in tea

within the same chromatographic analysis. In contrast to previous studies evaluating the

composition of LMWC in CGT with a limited number of samples (generally 1 or 2 samples),

this study, based on the diversity in origin and with an extensive number of samples analyzed,

contributes to gain an in-depth knowledge of LMWC composition in CGT from the main tea

producing countries.

The data on the LMWC composition, specifically on bioactive carbohydrates such as cyclitols

and α-GOS content, provides relevant information for consumers and professionals from the

tea industry. According to the data reported, the Portugal and Japan CGT could be considered

as a complementary source of bioactive carbohydrates for their high content determined in

cyclitols and α-GOS. In addition, based on the LMWC quantities determined, our study

estimated the caloric input theoretically in a homemade cup of green tea, confirming this

beverage as a drink recommended in weight-loss regimes.

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180

The variation of three quality indicators, widely used for the characterization of CGT (soluble

solids, color and antioxidant capacity), according to the different origin has been determined

for the first time in an extensive set of samples.

The employment of multivariate statistical analysis to the measured variables (LMWC, soluble

solids, color and antioxidant capacity) lead to the identification of different markers such as

fructose, glucose, soluble solids, GAE and TE to characterize the different CGT groups. The

differences observed among groups were tentatively attributed to the fixing procedure

employed (steaming or pan-firing) during the tea manufacturing.

5. ACKNOWLEDGMENTS

All authors would like to mention their gratitude to Mrs Anja Muller for her assessment during

the measurements of the samples. All authors would like to acknowledge Dr Gorka Ruiz de

Garibay the valuable suggestions for this manuscript. The authors would like to thank Fariba

Sabzi, Prof. Dr Lalith Jayasinghe and Seung-Hun Lee for providing samples from Iran, Sri

Lanka and South Korea.

This research did not receive any specific grant from funding agencies in the public,

commercial, or not-for-profit sectors.

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Chapter 8. Changes in low molecular weight carbohydrates in kale during

development and acclimation to cold temperatures determined by

chromatographic techniques coupled to mass spectrometry

Roberto Megías-Pérez, Christoph Hahn, Ana Isabel Ruiz-Matute, Britta Behrends, Dirk C.

Albach, Nikolai Kuhnert

Manuscript will be submit to Food Research International in the near future (retrieved

09/05/2019)

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ABSTRACT

Kale has gained an increasing attention for the diverse health benefits reported for its

consumption. Despite being a fundamental element in the German cuisine and being employed

in the elaboration of diverse products such as tea or smoothies, the composition of the low

molecular weight carbohydrates (LMWC) has been scarcely studied. Gas chromatography

coupled to mass spectrometry has allowed the identification of new LMWC for the first time in

kale, such as myo-inositol, galactinol, maltose or melibiose. Eight major LMWC have been

quantified using hydrophilic interaction liquid chromatography coupled to mass spectrometry

(HILIC-MS) to evaluate possible differences in LMWC content of three different commercial

kale types, with respect to plant development and changes associated with low temperature.

Overall, for all types of kale plants under study, the content of maltose decreases during the

development while the content of fructose, melibiose, maltose, raffinose and galactinol is

increased in all types of kale plants as a consequence of the acclimation to cold temperatures.

These results underline the importance of controlling the temperature during cultivation in order

to obtain a higher content of bioactive carbohydrates in this vegetable.

Keywords: Brassica oleracea, HILIC-MS, GC-MS, carbohydrate, freezing tolerance, plant

development

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1. INTRODUCTION

Brassica oleracea, a member of the cruciferous vegetable family, is one of the most widely

cultivated vegetables in the world [1] Despite looking very different, cabbage, kale, kohlrabi,

cauliflower, broccoli and Brussels sprouts are all varieties of Brassica oleracea. The variety best

adapted to northwestern European climate is kale (Brassica oleracea var. sabellica), a tough

and hardy plant, able to withstand freezing temperatures and water shortages. On the market, a

large variation of kale types can be found. These varieties differ in terms of habit, growth height,

color and leaf morphology [2].

A recent study corroborates the classification of kale (in the broader sense) in at least three

different types of kale cultivars: curly kales, Italian kales and Collards [3]. Curly kales (Scotch

type, Brassica oleracea var. sabellica) are mostly grown in Northern Europe and considered as

kale in the narrow sense. In contrast, the Italian kales of the Lacinato type (Brassica oleracea

var. palmifolia) have dark green savoyed blade shape leaves and are grown mostly in Tuscany

with a long tradition in Italian cuisine. The third group, Collards (Brassica oleracea acaphala),

constitute the varieties mainly found in the United States, especially in the Southern part. They

are characterized by large and flat roundish leaves. They most resemble wild and feral cabbages,

big-leaved, bitter tasting shrub [3].

The farming conditions of this vegetable have an influence on the organoleptic properties. Local

(non-commercial) farming practice in Northwestern Germany claim that low temperature plays

a role in terms of the palatability of the vegetable, often suggesting to harvest kale after the first

frost. As a consequence of its characteristic organoleptical properties, kale is a traditional

vegetable widely consumed in winter times, especially in Northwestern Germany and adjacent

regions. Looking beyond Northern Germany, there are other countries that in recent time

discovered kale as a valuable and healthy vegetable. One example are the United States, where

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this vegetable is widely employed in the preparation of salads and smoothies, among others,

making kale an everyday food item [4].

The interest on kale consumption for the consumers, corrobotated by the elevated production

of this vegetable [5], is based on the different health-promoting benefits reported, such as

antigenotoxic activity [6], anticancerogenic activity [7] and protection of the cardiovascular

system and gastrointestinal tract [8, 9]. The different beneficial effects of kale are consequence

of its chemical composition. On average, the main composition is 89% moisture, fiber (4 %),

proteins (3 %), lipids (1.5 %) and LMWC (1 %) [10]. Interestingly. an average serving of this

vegetable provides 25 %, 100 % and 40 % respectively of the calcium, vitamin A and C

recommended daily uptake (RDI) [11]. Another significant group of bioactive kale metabolites

are glucosinolates. On average, the concentration of glucosinolates in kale is in the range of

2.25-93.90 μmol/g DW [4]. Glucosinolates are activated by the enzyme myrosinase after cell

damage, leading to the production of isothiocyanates, nitriles, thiocyanates, epithionitriles, and

oxazolidinethiones, compounds biologically active with direct health-promoting effects [12].

Diversity in the glucosinolate content between different genotypes of kale from different origins

has recently been reported [3].

Regarding low molecular weight carbohydrates (LMWC) in kale, their study has been

overlooked in comparison to other metabolites. Some recent studies have reported the LMWC

content in kale from 25 different genotypes grown in the USA [13] and evaluated their changes

under moisture stress [14]. In terms of LMWC composition, the presence of pentoses (xylose,

arabinose), hexoses (fructose, glucose, mannose), sugar alcohols (sorbitol, mannitol),

disaccharides (sucrose), and oligosaccharides (α-galactooligosaccharides (α-GOS) such as

raffinose, stachyose, verbascose and fructooligosaccharides such as 1-kestose) have been

reported [13-15]. However, oligosaccharides have only been reported for kale cultivated under

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stress conditions. The interest in this type of LMWC is based on the bioactive effects, such as

the prebiotic effect reported for α-GOS [16, 17].

In view of the so far limited information available on kale LMWC, new studies aimed at

performing a comprehensive characterization might provide the identification of new LMWC

in kale, information on plant metabolism and elucidation of the sensory properties of the

vegetable.

Regarding plant metabolism, LMWC play a role in molecular signalling during plant

development [18] and as indicators in response to abiotic conditions [19]. In terms of sensorial

perception, carbohydrates contribute significantly to flavour and sweetness of kale. Therefore,

understanding the LMWC composition of the different kale genotypes and the impact of

farming conditions into the LMWC profile might contribute to the identification of optimal

farming conditions of kale that provide an adequate palatability to this vegetable.

Based on the above points, the LMWC analysis in kale is quite essential for a better knowledge

of this vegetable. The methodological approach employing different techniques such as GC-

MS and HILIC-ESI-TOF MS has resulted suitable for the identification and quantification of

LMWC in other food matrices [20]. In terms of structural identification, gas chromatography

coupled to mass spectrometry (GC-MS) is a suitable technique for the identification of

unknown LMWC based on its high-resolution power and sensitivity. GC-MS uses the

combination of GC retention times (or retention indices) and fragmentation specific spectra of

the derivatives obtained with electron impact ionization (EI) to elucidate the chemical structure

of carbohydrate isomers [21]. Several applications of this technique such as the analysis of

inositol in edible legumes [22], cyclitol glycosides in chickpeas and adzuki beans [23] and

LMWC in cocoa beans [20], among other applications have been reported. However, the

application of this technique is limited by the tedious sample preparation [24].

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In contrast, the increasing employment of hydrophilic interaction liquid chromatography

(HILIC) for quantification purpose is based on the minimum requirement of sample

preparation, the appropriate resolution between isomers, good peak shapes and easy coupling

to mass spectrometers [25]. HILIC-MS for the analysis of LMWC has been applied in many

fields of food science, for example the analysis of commercial green tea [26], goat colostra

[27] and medicinal plants [28].

The present paper aims at investigating the LMWC profile and content of three main different

kale types and at evaluating the changes in LMWC during plant development and the effect

associated with cold temperature. As a first approach, GC-MS has been used to identify

different LMWC in kale. Thereafter, HILIC-ESI-TOF MS has been employed to analyze the

main LMWC content in the different samples under study to monitor the changes during kale

development and those associated with low temperatures. To the best of the author’s

knowledge, no studies have so far investigated the quantitative distribution of LMWC in kale

under controlled environmental conditions in response to cold stress.

2. MATERIALS AND METHODS.

2.1. Chemicals and standards

Fructose, glucose, galactose, maltose, melibiose, myo-inositol, sucrose, raffinose, phenyl-β-D-

glucoside ammonium hydroxide solutions, ammonium formate, hexamethyldisilazane (HMDS)

and trifluoroacetic acid (TFA) were provided by Sigma Chemical Co. (St. Louis, USA).

Dichloromethane and LC-MS grade acetonitrile (ACN) were purchased from Aplichem

Panreac (Darmstadt, Germany). Galactinol was obtained from Carbosynth (Compton, UK).

Mannitol was donated by Bermpohl Apotheke (Bremen, Germany).

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191

2.2. Kale samples

Based on the diversity in kale varieties described above, representative samples of three kale

types (Scotch type, Lacinato type and Feral type) were chosen for this study. Specifically, the

kale cultivars “Frostara” (German kale, Scotch type), “Black Tuscany” (Italian kale, Lacinato

type) and “Helgoländer Wildkohl” (Wild Cabbage, Feral type) were selected [3].

Qualitative and quantitative changes in LMWC asociated with plant development were studied

in plants grown in a temperature chamber at a constant temperature of 25°C with 12 h/12 h

cycle of light/dark at the Botanical Garden Oldenburg (Oldenburg, Germany) The period of

plant development considered as the optimal time when the first leaves are ready to harvest

with characteristics in terms of size similar to the plants grown in the field was stated at 8 weeks.

To evaluate the reproducibility of the plant’s development process under the conditions

mentioned above, the experiments were carried out in two different time intervals. In the first

time interval, leaves from one plant of each kale cultivar were collected weekly from week 4 to

week 8, using each time a different individual plant. Likewise, in the second interval, leaves

from two plants per kale cultivar were collected at the same time points under the same

conditions described above.

The effect of cold temperatures on the LMWC profile of the kale plants was evaluated in

samples that initially had been cultivated under the same conditions (temperature chamber at

25°C for 8 weeks). After 8 weeks, the temperature was changed to 2°C, maintaining the other

conditions the same (irrigation and light parameters). Different leaf samples from two

individual plants were collected at 0, 3, 7 and 11 days after initiating cold temperature (2°C).

As a control, different leaf samples from two individual plants grown at a constant and

controlled temperature of 25°C during the same time period were analyzed.

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The leaves of each time point for each kale cultivar were freeze-dried and pooled before

chromatographic analysis.

2.3. LMWC extraction

100 mg of kale leaves were extracted in 10 mL of water for 1 h under constant stirring at 25 °C.

In all cases, extracts were immediately centrifuged at 4400g for 10 min at 4 °C. After that, 1

mL of the sample was filtered for chromatographic analysis.

2.4. GC-MS analysis

The derivatisation method described by Ruiz-Aceituno, Carrero-Carralero, Ruiz-Matute,

Ramos, Sanz and Martínez-Castro [23] was employed as sample preparation for GC-MS

analysis. Briefly, 1 mL of the aqueous kale extract and 0.1 mL of 70% methanolic solution of

phenyl-β-D-glucoside (1 mg mL-1; internal standard) were evaporated under vacuum. After

that, 350 µL of 2.5% hydroxylamine chloride in pyridine were added and the solution was kept

at 75 °C for 30 min. Then, 350 µL of hexamethyldisilazane (HMDS) and 35 µL of

trifluoroacetic acid (TFA) were added and the solution was maintained at 45 °C for 30 min.

This derivatization method employing a two-step derivatization procedure (oximation +

silylation) has the advantage to reduce the number of chromatographic peaks to two peaks in

reducing LMWC (corresponding to E- and Z- oxime isomers) and to one peak in the case of

non-reducing LMWC.

A 7890A gas chromatograph coupled to a 5975C quadrupole mass detector (Agilent

Technologies, Palo Alto, CA, USA) operating in EI mode at 70 eV was used to perform the

identification of the different carbohydrates. Analyses were performed on a ZB-5 (5%

phenylmethylsiloxane) capillary column (25 m × 0.25 mm i.d., 0.25 µm film thickness;

Phenomenex, Madrid, Spain). Helium at 1 mL min−1 was employed as a carrier gas. The

analyses were performed under the following gradient temperature: 120 °C to 300 °C at a

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heating rate of 5°C min-1 and held for 10 min. The temperature in the transfer line and ionization

source was set to 280 ºC and 230 ºC respectively. The injection volume was set to 1 µL of

sample and the injection was performed in split mode (split ratio of 1:20) at 300 °C. HPChem

Station software (Agilent Technologies) was employed for data acquisition.

Linear retention indices (IT) were determined considering the retention times of LMWC

trimethylsilyl oxime (TMSO) derivatives and those of suitable n-alkanes (from C17 to C36) [29].

The LMWC were identified by comparison of experimental linear retention indices (IT) and

mass spectra with the available standards. In the cases in which commercial standards were not

available, compounds were tentatively identified by their mass spectral information and data

from the literature.

2.5. HILIC-ESI-TOF MS analysis

Prior to analyses, the carbohydrate extracts (1 mL) were filtered through a CHROMAFIL Xtra

PTFE-45/25 filter (Macherey-Nagel, Düren, Germany ).

Chromatographic analyses were performed using an Agilent 1260 Series HPLC (Agilent

Technologies, Karlsruhe, Germany). A BEH X-Bridge column was employed with a

trifunctionally-bonded amide phase (Waters Company, USA) and the following characteristics:

150 mm × 3.0 mm; 3.5 μm particle size and 135 Å pore size. Water (solvent A) and acetonitrile

(solvent B) supplemented both with 0.1 % ammonium hydroxide and ammonium formate (5

mM) were used as a mobile phase. The injection volume and flow rate was set up to 3 µL and

0.4 mL min-1 respectively.

A QTOF mass spectrometer fitted with an ESI ion source (Bruker Daltonics HCT Ultra,

Bremen, Germany), operating in positive ion mode in the range of 50 - 1200 m/z was employed.

The ESI source parameters were adjusted as follows: spray voltage, 4.5 kV; drying gas (N2,

99.5% purity); temperature = 200 °C; drying gas flow, 9 L min-1; nebulizer (N2, 99.5% purity)

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pressure, 1.8 bar. Data was acquired using HyStar 3.2 software (Bruker, Bremen, Germany). A

solution of 0.1 M sodium formate solution was used to calibrate the mass spectrometer prior to

the sequence run. A posterior calibration of the LC-MS spectra was performed using sodium

formate solution injected automatically through a six-port valve prior to each chromatographic

run. The mass spectrometer was employed to identify the molecular formula and for

quantitative analysis. Identification of the compounds was based on the comparison of the

retention time with those of commercial standards. The mass error detected was below 5 ppm.

The Extracted Ion Chromatogram (EIC) areas of the sodium adduct [M+Na]+ of LMWC (m/z

203.05, 527.15, 365.10 respectively) of the standards and samples were used to determine the

different calibration curves and the quantities of each LMWC. Results were expressed as mg/

g DM kale.

Signal to noise ratio (S/N) values of ten and three were the criteria to detemine the limit of

detection (LOD) and quantification (LOQ) of the different LMWC under study.

Matrix effect was evaluated as the average recovery determined after the adition of two different

LMWC standard quantities to the carbohydrate kale extract. The addition of each quantity was

performed in triplicate.

Intra-day precision and inter-day precision were used to asses chromatographic reproducibility.

Inter-day precision was calculated as the relative standard deviation (RSD) of the LMWC

concentrations obtained from 1 sample injected in three different days. Intra-day precision was

calculated as the RSD average of the LMWC concentrations determined in 1 sample injected

five times, within the same day.

Reproducibility of the whole method (extraction of LMWC and chromatographic separation)

was assesed based on the average RSD value of each target compound measured on randomly

selected samples performed in duplicate (n = 11) and triplicate (n = 2) of sample preparation.

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2.6. Statistical analysis

Peak area values of the different LMWC were extracted using Quant Analysis 2.2 software

(Bruker, Bremen, Germany). Kruskal Wallis test followed by Dunn’s test and t-test were

performed using GraphPad Prism 7.0 software (San Diego, California, USA).


3.1. Qualitative analysis of Kale LMWC

As a first approach, considering the high resolving power and high sensitivity provided by GC-

MS, this technique was employed for the characterization of LMWC in kale extracts. Figure 1

shows the GC-MS profile of trimethylsilyl oxime (TMSO) derivatives of LMWC obtained for

samples cultivated under different conditions of temperature (25 ºC and 2 ºC).

Table 1 contains the peak assignation and linear retention indices (IT) of different LMWC

detected in kale extracts by GC–MS. Different monosaccharides and sugar-alcohols previously

reported in the literature such as arabinose, ribose, fructose, glucose, galactose and sorbitol were

detected. Other monosaccharides of 5 and 6 C atom were not detected [13]. Peak 7 (IT 2128),

identified as myo-inositol, showed typical spectra of free inositols due to the presence of the

pair of characteristic ion fragments at m/z 305 and 318 with intensity similar to the pair of m/z

191 and 217 [30].

Regarding disaccharides, the presence of sucrose was corroborated [13]. In addition, further

peaks were detected in the disaccharide zone. Peaks 9 and 10 were identified maltose E and Z

(IT 2929 and 2947, respectively) and melibiose E and Z isomers (IT 3031 and 3068,

respectively).

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Table 1. Peak assignation and linear retention indices (IT) of different carbohydrates detected in Kale extracts by

GC–MS.

Peak number Compound Assignation IT

1 Arabinose E and Z 1786 /1793

2 Ribose 1 and 2 1815/1818

3 Sorbitol 1976

4 Fructose 1 and 2 1983/1991

5 Galactose E and Z 2029/2058

6 Glucose E and Z 2040/2058

7 myo-Inositol 2128

8 Sucrose 2685

9 Maltose E and Z 2929/2947

10 Melibiose E and Z 3031/3076

11 Galactinol 3068

12 Diglycolsyl glycerol 3213

13 Raffinose 3499

Peak 11 showed a typical fragmentation pattern of cyclitol glycosides [23], based on the triplet

m/z ions 191/204/217 (characteristic of silylated pyranose rings), the m/z ions 305 and 318

(typical of cyclitols) and a low abundance of m/z ion 361 (related to glycosidic linkages).The

presence of myo-inositol as a free inositol suggests the assignment of this peak as glycosyl-

myo-inositol, which was confirmed positively as galactinol by comparing retention time and

mass spectrum with a standard.

Peak 12 was tentatively asssigned as diglycosyl-glycerol based on the presence of the specific

m/z ion at 337 [31]. Also, peak 13 was corroborated as raffinose.

This comprensive characterization has allowed the identification of ribose, galactose, maltose,

melibiose, myo-inositol and galactinol for the first time in this vegetable. For some of these

compounds such as melibiose and myo-inositol, bioactive effects have been describied. For

example, melibiose has been described to promote the calcium absorption in the intestines and

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197

Figure 1. GC–MS profile of TMSO derivatives of LMWC obtained for Black Tosacany kale samples cultivated under 2 ºC (A) 25 ºC (B). For peak

identifications see Table 1.

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the improvement of the allergic disease symptoms [32, 33]. In the case of myo-inositol, this

compound has been used in the treatment of mental disorders [34].3.2. Quantitative analysis of

kale LMWC.

3.2.1. Set-up of LMWC determination of kale extract by HILIC-ESI-TOF MS

Figure 2 shows the extracted ion chromatogram profile (HILIC-ESI-TOF MS) of two different

kale carbohydrate extracts from plants grown at different temperatures (25°C and 2°C). Based

on the previous identification by GC-MS, the peak eluting at a retention time (tR) of 4.7 min,

with a molecular formula of the sodium adduct C5H10O5Na, could be assigned tentatively as a

mixture of arabinose and ribose.

Peaks at tR of 7.4 min and 9.6 min were assigned as fructose and glucose by comparison of the

tR and molecular formula with those of commercial standards. Under this condition, galactose

coelutes with glucose and the quantitative data shown for glucose in this manuscript correspond

to the glucose and galactose content. A peak eluting at 9.4 min, with a molecular formula of

C6H14O6Na, was tentatively assigned as sorbitol.

Regarding disaccharides, peaks eluting at 15.8 min, 18.4 min and 21.5 min were also identified

as sucrose, maltose and melibiose by comparison of the tR with those of commercial standards

and molecular formula. Two peaks eluting at tR of 19.7 min and 23.7 min were assigned as

unknown disaccharides according to the molecular formula.

Dihexosyl glycerol was tentatively identified as a peak eluting at 23 min based on molecular

formula C15H28O13Na and by comparison of the tR with the same compound determined

previously in a carbohydrate cocoa bean extract [35]. As regards trisaccharides, raffinose was

assigned to a peak eluting at 25.3 min based on the molecular formula and comparison of the

tR with the commercial standard. Other oligosaccharides such as 1-kestose previously reported

were not identified in the different extracts analyzed.

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199

Figure 2. Extracted ion chromatograms obtained by HILIC-ESI-TOF MS of a kale carbohydrate extract from kale Black Tuscany grown under cold

temperatures during a period of 3 days corresponding to: A) 203.05 m/z ions, B) 365.10 m/z ions and C) 527.15 m/z ions. Peak numbers: 1) Fructose,

2) Glucose + galactose, 3) myo-Inositol, 4) sucrose, 5) maltose, 6) Melibiose, 7) Galactinol 8) Raffinose.

5 10 15 20 25 30 Time [min]

0.0

0.5

1.0

1.5

2.0

6x10

Intens.

1

2

3

4 5

6

7

8

b

3

ca

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In order to ensure the reproducibility of the method reported (extraction and chromatographic

separation) in the quantitation of the main LMWC in kale (fructose, glucose, sucrose, maltose,

melibiose, myo-inositol, raffionse and galactinol) different analytical parameters were

determined.

The different calibration curves employed showed suitable R2 values (ranging 0.97-0.99).

Regarding LOD and LOQ, values ranging from 0.26−1.5 μg mL−1 and 0.08−0.5 μg mL−1

respectively. In terms of chromatographic reproducibility, suitable RSD values were

determined for intra-day precision (ranging 3.1-6.6) and inter-day precision (ranging 4.5-10.1).

In terms of matrix effect, average recovery values close to 100% were determined, confirming

the absence of such effect in the LMWC quantitation.

Adequate RSD values of the samples randomly performed in duplicate (average RSD values in

the range of 6.8-13.4) and triplicate (average RSD values in the range of 4.3-14.9) were

determined in the assesment of the reproducibility of the method proposed (extraction

carbohydrates and chromatographic separation).

Overall, the different analytical parameters determined confirm the suitability of the method

reported for quantitation of LMWC in kale extract. In comparison to other chromatographic

methods employed for kale analyses, the HILIC-ESI-TOF MS method reported allows a better

chromatographic separation of the disaccharides, allowing the separation and quantification of

sucrose, maltose and melibiose, among other LMWC.

3.2.2. Quantitation of the main LMWC at different kale plant development stages

Table 2 lists the content (mg/g DM) of the main LMWC found in the different types of kale

cultivars and, respectively, at the different development stages (4, 5, 6, 7 and 8 weeks). The

wide variability observed for some of the LMWC measured could be explained by the intrinsic

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201

variability of the individual plants. In view of the data determined in leaves of week 8, no

significant differences were observed between the three kale cultivars under study (Table 2).

Regarding the LMWC content in kale, glucose and fructose were the most abundant

carbohidrates in leaves collected at week 8 for all kale cultivars. The values determined for

these LMWC at week 8 were in the same order to the values determined previously in different

Brassica oleracea vegetables such as cauliflower, white cabbage and curly kale [15]. According

to Table 2, myo-inositol was the third most abundant compound. In comparison to the inositol

quantities reported previously in Brassica oleracea [15], the content determined for the three

cultivars was higher. The high content of myo-inositol in comparison to the low concentrations

determined for melibiose, maltose, raffinose and galactinol during the development of the plant

might be attributed to the role of this LMWC in the regulation of cell development and growth

[36].

With respect to the sucrose content, low levels were determined in the different stages of

development of the different kale cultivars. The absence of sucrose content in kale leaves was

previously reported in the extensive study of different kales reported by Thavarajah,

Thavarajah, Abare, Basnagala, Lacher, Smith and Combs [13].

In terms of the changes during plant development, fructose showed an increasing tendency in

Frostara kales, a bimodal tendency with a minimum value at week 7 in Black Tuscany kale and

stable values for Wild cabbage. A similar bimodal tendency was observed in the content of

fructose in leaves of Camellia sinensis [37].

Glucose showed a bimodal tendency with a minimum value at week 7 in Black Tuscany kale

and stable values for Wild cabbage and Frostara kale. myo-Inositol showed a bimodal tendency

with a minimum value at week 7 in Frostara and Black Tuscany kale and stable values for Wild

cabbage.

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Table 2. Concentration (mg/g DM) of the main LMWC analyzed by HILIC-ESI-TOF MS in three different kale cultivars during the development of

the plant (weeks 4-8). Data shown as mean and standard deviation (in brackets). F = Frostara kale, B = Black Tuscany kale, W = Wild cabbage.

Cultivar

LMWC (mg / g DM)

Time Fructose Glucose myo-Inositol Sucrose Melibiose Maltose Galactinol Raffinose

4 weeks

W 63.1 (22.7) 146.6 (84.8) 24.4 (6) 5.6 (4.9) 1.6 (1.1) 9.9 (8) 0.1 (0.1) 1.5 (1.4)

F 23.9 (4.8) 271.8 (167.7) 84.1 (43.6) 12.1 (11.1) 3 (1.9) 15.1 (8.7) 0.2 (0.2) 1.4 (1.2)

B 77.8 (4.2) 162.5 (7) 35.9 (5.7) 25.3 (59.3) 1.5 (0.8) 9 (5) 0 (0) 1.1 (1)

5 weeks

W 37.8 (11.1) 74.7 (36) 19.6 (6.5) 4.6 (4.1) 1.5 (1.3) 8.4 (7.7) 0.4 (0.4) 1.9 (2.1)

F 32.2 (15.6) 254.3 (248.6) 32.3 (3.6) 3.4 (3.1) 3.2 (2.9) 12.1 (8.7) 0.6 (0.7) 1.5 (1.7)

B 39.3 (21) 89.8 (30.6) 34.6 (5.8) 33.3 (87.9) 1.5 (0.9) 6 (2.8) 0 (0.1) 0.8 (0.7)

6 weeks

W 24.5 (6.5) 100.6 (25.8) 18.9 (1.9) 6.6 (5.9) 1.6 (0.7) 5.5 (1.5) 0.5 (0.4) 1.4 (1.3)

F 54.4 (17.3) 123.7 (37.6) 27.4 (10.6) 4.2 (3.7) 2.1 (0.4) 7.3 (3.9) 0.3 (0.3) 0.8 (0.7)

B 31.4 (20.3) 84.5 (17.7) 16.5 (2.5) 34.9 (94.5) 3.9 (4.3) 6.5 (1.3) 0.2 (0.3) 2 (1.7)

7 weeks

W 48.2 (7.3) 54.3 (26.3) 26.6 (1.8) 7.5 (9.8) 0.9 (1.3) 3.9 (5.9) 0.4 (0.6) 2.2 (3.8)

F 55 (15.1) 179.7 (114.9) 19.4 (7.5) 2.8 (3.6) 2.4 (1.6) 6 (5.3) 0.4 (0.4) 1.4 (1.4)

B 27.5 (12.7) 60.9 (9.8) 17.3 (5.6) 24.2 (95.7) 0.7 (0.5) 4.3 (3.2) 0.2 (0.1) 1.7 (1.4)

8 weeks

W 49.1 (29.3) 148.3 (81.1) 40.3 (23.4) 0 (0) 1.8 (0.9) 0.7 (1.1) 0 (0) 0 (0)

F 64.2 (25) 113.3 (38.9) 66.9 (35.3) 0 (0) 2.2 (0.8) 0.3 (0.5) 0 (0) 0 (0)

B 76.6 (36.2) 145.1 (27.1) 38.7 (19.2) 2 (125.8) 1.1 (0.5) 0.1 (0.2) 0 (0) 0 (0)

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Overall, for the rest of LMWC under study (maltose, sucrose, melibiose, raffinose and

galactinol), these compunds showed stable values or a slight tendency to decrease in content

for all analyzed kale cultivars during the period of development studied. In the case of maltose,

the decreasing tendency during development was statistically significant for Black Tuscany

kale.

3.2.3. Quantitation of the main LMWC as consequence of cold acclimation

The LMWC content of the different kale leave samples collected at 0, 3, 7 and 11 days of

acclimation to cold temperature (2°C) and the respective controls at warm temperature (25°C)

is shown in Table 3. Overall, for the kale Frostara and Wild Cabbage cultivars, the content of

fructose, glucose, sucrose, melibiose, maltose, galactinol and raffinose was increased in

response to cold temperature acclimation. Under low temperature conditions, myo-inositol

content decreased in the different kale cultivars. With the exception of Black Tuscany kale, the

behaviour determined for some of the LMWC were in line with the data reported in studies of

cold temperature acclimation for Arabidopsis thaliana and sunflower seedlings, in which the

content of fructose, glucose, sucrose and raffinose were induced as a consequence of the

acclimation of the plant to cold temperatures [38, 39]. With respect to the increased content of

maltose in kale as a consequence to the response to cold acclimation, it might be attributed to

its protective role for proteins, membranes and the photosynthetic electron transport chain,

which has been demonstrated in Arabidopsis thaliana, another member of the Brassicaceae

family [40].

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Table 3. Concentration (mg/g DM) of the main LMWC analyzed by HILIC-ESI-TOF MS in three differents kale cultivars acclimated to cold and

warm temperatures. Data shown as mean and standard deviation (in brackets). F = Frostara kale, B = Black Tuscany kale, W = Wild cabbage.

Day Genotype Treatment LMWC (mg / g DM)

Fructose Glucose myo-Inositol Sucrose Melibiose Maltose Galactinol Raffinose

0

W 64.9 (27.9) 157.9 (47.5) 59.3 (22) 0 (0) 1.6 (0.4) 0.5 (0.8) 0 (0) 0 (0)

F 77 (17) 129.4 (36.2) 66.2 (35.6) 0 (0) 3.6 (0.1) 0.4 (0.8) 0.3 (0.6) 0 (0)

B 85.3 (22.8) 119.8 (64.1) 56.8 (28.6) 0.9 (1.5) 1.2 (0.3) 1.8 (0.9) 0 (0) 0 (0)

3

W Cold 264.4 (0.7) 342.9 (81.8) 18.3 (1.9) 0 (0) 7.8 (0.6) 13 (8) 1.2 (0.2) 5.5 (5.7)

Warm 79.4 (20.1) 216.3 (48.9) 83.8 (0.6) 0 (0) 2.4 (0.3) 0.2 (0.3) 0 (0) 0 (0)

F Cold 284.8 (137.9) 191.8 (57.8) 15.3 (5) 0.6 (0.9) 9.4 (1) 9.4 (2.7) 1.1 (0.1) 6.5 (1.2)

Warm 104.1 (17.4) 168.4 (4.5) 39.4 (42.6) 0 (0) 1.2 (0.7) 0 (0) 0 (0) 0 (0)

B Cold 65 (3.9) 101.6 (25) 18.3 (0.9) 0 (0) 4.9 (0.3) 2.7 (0.8) 1.6 (0.6) 9.2 (2.9)

Warm 80.3 (1.1) 178.3 (51.7) 11.9 (10) 3.1 (1.3) 0.5 (0.3) 0 (0) 0 (0) 0 (0)

7

W Cold 323.5 (25.5) 248.8 (6.4) 192.7 (22.7) 26.9 (18.5) 11.8 (0.4) 9.6 (2.1) 2.6 (1) 31.3 (21.7)

Warm 45.5 (20.5) 186.1 (70) 103 (37.3) 6.5 (4.7) 1.8 (0.2) 0 (0) 0 (0) 0 (0)

F Cold 300.4 (42) 349.9 (48) 24.6 (0.3) 8.2 (7.1) 13 (4.7) 8.1 (3.6) 4.4 (0.7) 39.7 (30.2)

Warm 55.7 (9.8) 119.9 (31.8) 45.2 (17.2) 0 (0) 0.5 (0.1) 0 (0) 0 (0) 0 (0)

B Cold 142.7 (8.5) 164.9 (17.1) 14.4 (4.3) 0.2 (0.3) 8.4 (1.5) 5.3 (0.5) 3.2 (0.3) 30.8 (5)

Warm 46.4 (22) 96.4 (40.7) 13.9 (6.2) 3 (0.8) 0 (0) 0 (0) 0 (0) 0 (0)

11

W Cold 451.8 (1.1) 369.3 (81.5) 23.8 (9.2) 3.8 (4.3) 11.6 (1) 8.2 (1.4) 4.1 (0.8) 66.9 (9.1)

Warm 33.2 (5.7) 141.2 (73.8) 68.2 (11.5) 0 (0) 1.4 (0.3) 0 (0) 0 (0) 0 (0)

F Cold 198.8 (8.7) 207.8 (46.4) 17.7 (0.7) 12.5 (8.9) 11.9 (0.4) 6.8 (3.6) 7 (1.2) 50 (26.3)

Warm 66.8 (4) 132.9 (2.1) 34.3 (6.4) 0 (0) 0.8 (0.1) 0 (0) 0 (0) 0 (0)

B Cold 71.5 (15) 100.2 (16) 17.5 (2.7) 5 (0.6) 6.6 (0.2) 4.4 (0.1) 5.5 (1.3) 48.4 (18.4)

Warm 76 (6.9) 145.6 (58.9) 34.6 (25.3) 2.6 (1.8) 0.6 (0.4) 0.4 (0.5) 0 (0) 0 (0)

RESULTS

205

The decrease in myo-inositol concentration can be explained either by its use as a precursor of

galactinol, compound that increased during cold temperature [41] or in the light of its role in

oxygen radical-scavening functions, since myo-inositol is a precursor in the synthesis of

ascorbate (vitamin C), one of the most effective plant antioxidants [42]. Ascorbate has been

shown previously to increase in cabbage in winter [43].

In addition to the radical-scavenging role of carbohydrates[44], Ito, Shimizu, Nakashima,

Miyasaka, and Ohdoi (2014) [45], reported the suppression of water absorption by roots in

plants exposed to low temperatures and the subsequent induction of soluble LMWC to protect

the plant from dehydration through their osmotic function. This freezing tolerance is a result of

the increment in carbohydrate content, hence increasing the overall molarity of dissolved

sugars, leading to an increase of freezing point depression as a colligative property.

The LMWC profile observed in kale leaves exposed to cold temperatures conforms to the

adaption mechanism to cold temperatures inferred in other plants, especially the related

Arabidopsis thaliana. Consequently, information on the genetic regulation of carbohydrate

synthesis and cold acclimation in this model species should be transferable to Brassica oleracea

to study the genetic basis of cultivar-dependent differences in carbohydrate synthesis in

response to low temperatures.

4. CONCLUSIONS

This study corroborates kale as a potentially good source of bioactive carbohydrates such as

inositols and α-GOS, boosting the high nutritional level of this vegetable. Considering the

potential positive effect on health, this vegetable could be recommended as a material for the

elaboration of other food items such as commercial tea.

CHAPTER 8

206

The information reported in LMWC using GC-MS has been shown to be complementary to the

data previously reported in literature. Additionally, different LMWC such as galactose, maltose,

melibiose have been reported for the first time in this vegetable.

The quantitative data reported for the main LMWC during development of kale plants

contribute to a better understanding of the metabolism of this vegetable. In addition, the changes

associated with LMWC content, consequence to the cold acclimation of the plant, might have

an impact on the sweetness of the vegetable. Consequently, we provide a scientific rationale for

the Northern German practice of harvesting kale following first frost. The results might open

new methodologies of crop production practice to obtain kales with better nutritional quality

and palatability. It also opens the path towards understanding the genetic variation in cold

acclimation in kales and related vegetables.

ACKNOWLEDGEMENTS

Authors would like to mention their gratitude to Dr Gorka Ruiz de Garibay for the valuable

discussions on the statistical analysis. Also, the authors would like to acknowledge to Anja

Müller for her assistance during the measurements.

The identification performed by GC-MS was supported by Ministerio de Economía, Industria

y Competitividad of Spain (project AGL2016-80475-R), by Comunidad de Madrid (Spain) and

European funding from FEDER program (S2013/ABI-3028 AVANSECAL-CM).

RESULTS

207

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GENERAL CONCLUSIONS

GENERAL CONCLUSIONS

213

The different studies included in this thesis have led to the acquisition of new knowledge related

to qualitative and quantitative information about the LMWC profile in cocoa beans,

commercial green tea and kale, dietary foods of considerable economic interest and with health-

promoting properties.

The different health benefit aspects promoted by the different bioactive carbohydrates such as

cyclitols and α-galactooligosaccharides have been discussed in chapter 1. The different

analytical methodologies employed for LMWC analysis were also discussed in chapter 2. Each

of the chromatographic methods employed in the different chapters have been validated in

terms of precision, matrix effect, range of linearity, LOD and LOQ and reproducibility for

selected LMWC. The different studies included in this manuscript have led to the acquisition

of new knowledge related to qualitative and quantitative LMWC content ( mono-, di-,

oligosaccharides and cyclitols) of cocoa beans, commercial green tea and kale, enhancing the

perception of these dietary foods as functional food.

The employment of HILIC, using an amide column with binary mixtures of acetonitrile: water

and ammonium hydroxide or ammonium acetate as additives, has allowed the separation and

sensitive detection of different LMWC in the different dietary food analyzed in this thesis.

These characteristics make the different HILIC methods reported as suitable methods able to

be applied for quality control of natural products and other food ingredients containing inositols

and oligosaccharides.

The combination of GC ( technique suitable for the identification of LMWC due to its high-

resolution power and sensitivity) and HILIC ( technique adequate for quantification due to the

absence of tedious sample preparation, appropriate resolution, good peak shapes and easy

coupling to MS detectors) is a suitable approach for the detection and quantification of minor

LMWC.

GENERAL CONCLUSIONS

214

Chapters 4 and 5 contribute to cocoa science with the following points:

1. Identification of main LMWC groups in cocoa beans :

-monosaccharides: fructose, glucose, galactose.

-disaccharides: sucrose, melibiose,maltose, unknown disaccharides.

-oligosaccharides: 1-kestose, 6-kestose, raffinose, unknown trisaccharides.

-polyols: mannitol, alcohol of tri-pentose and alcohol of disaccharide.

-cyclitols: myo-inositol, scyllo-inositol and galactinol.

-iminosugars.

-dihexosyl glycerol.

2. The different chapters of the results have reported quantitative data not available in the

scientific literature regarding the LMWC composition (mono-, di- tri-, tetrasaccharides

and cyclitol) in a significant number of samples from different origins and fermentation

status.

3. The LMWC profile of cocoa beans captures information about the fermentation status.

The employment of chemometric tools has allowed the identification of different

indicators such as sucrose, melibiose, unknown disaccharides, raffinose and stachyose

to characterize unfermented cocoa beans. Conversely, mannitol has been proposed as a

clear indicator of fermentation in cocoa beans.

4. The LMWC profile of fermented cocoa beans reports information regarding the

fermentation procedure employed. The chemometric tools have reported the

identification of fermented samples characterized by a high content of disaccharides,

raffinose and stachyose, suggesting for this samples an incomplete fermentation either

due to the fermentation procedure (OF procedure) or the shorter period of fermentation

procedure.

GENERAL CONCLUSIONS

215

The results from chapter 6, focused on the LMWC changes during spontaneous fermentation,

indicated:

1. With respect to unfermented beans, the period of fermentation between 48h to 96h has

been identified to be critical to produce differences in the LMWC profile.

2. Except for fructose and glucose, the changes for each LMWC follow either a zero-order

or first-order kinetics.

3. A strong correlation has been determined between the pH of the bean and the content

of sucrose, raffinose and stachyose, among other LWMC.

The study of the LMWC changes during spontaneous fermentation can only be considered as

the starting point to further studies aiming to unravel multiple reactions, such as Maillard

reaction, occurring during the spontaneous cocoa bean fermentation.

Overall, the different studies developed in cocoa beans demonstrate remarkable diversity in the

LMWC profile of unfermented and fermented cocoa beans. In conjunction with other factors,

the diversity observed in the LMWC profile could be one of the factors involved in the diversity

of cocoa flavor from different origins.

The study of CGT (chapter 7) has provided novel data regarding the LMWC composition

(mono-, di- tri-, tetrasaccharides and cyclitols) in a significant number of CGT samples from

different origins and with different processing methods, which suppose a considerable

contribution to the tea science and the general interest for the consumers.

Also, the study on green tea has reported an innovative approach, combining four different

indicators such as LMWC, color parameters, soluble solids and antioxidant capacity, to

characterize CGT samples. The results showed the presence of two groups of samples. The

differences identified have been discussed based on the diversity of processing between the

different tea producing countries.

GENERAL CONCLUSIONS

216

The knowledge generated from the LMWC in CGT opens a window to further investigations

related to the influence of LMWC in thearubigin formation during the production of black tea

or the influence of controlled conditions during the green tea leaves processing to avoid loss of

valuable nutrients, like prebiotic carbohydrates.

The study of LMWC in kale (chapter 8) has lead to the identification of LMWC in kale for the

first time in literature, some of them with bioactivity properties. Also, the study reveals the

dependency of LMWC profile with the farming conditions. This dependency might be

considered as a starting point to control farming conditions with the objective of enhancing

bioactive LMWC content in this functional food.

SUPPLEMENTARY INFORMATION


219

Supplementary information of Chapter 4 “Profiling, quantification and classification of cocoa

beans based on chemometric analysis of carbohydrates using hydrophilic interaction liquid

chromatography coupled to mass spectrometry”.


220

1. Qualitative analysis.

Figure S1.1. HILIC-ESI-MS2 spectra of disaccharide (19.4) (peak 9) in positive ion mode.



221


Figure S1.4. HILIC-ESI-MS3 spectra of trisaccharide (22) (peak 12) in positive ion mode.


222

Figure S1.5. HILIC-ESI-MS3 spectra of trisaccharide (23.2) (peak 14) in positive ion mode.



223




224


Figure S1.10. HILIC-ESI-MS2 spectra of alcohol of disaccharide (peak 11) in positive ion mode.


225

Figure S1.11a. HILIC-ESI-MS2 spectra of alcohol of tri-pentose (peak 7) in positive ion mode.

Figure S1.11b. HILIC-ESI-MS3 spectra of alcohol of tri-pentose (peak 7) in negative ion mode.


226

Figure S1.12. HILIC-ESI-MS3 spectra of dihexosyl glycerol (peak 15) in positive ion mode.

Figure S1.13. HILIC-ESI-MS2 spectra of pentosyl-iminosugar (peak 4) in positive ion mode.


227

Figure S1.14. HILIC-ESI-MS2 spectra of pentosyl-iminosugar (peak 5) in positive ion mode.

Figure S1.15. HILIC-ESI-MS3 spectra of glycosyl-iminosugar (peak 8) in positive ion mode.


228

Table S1.1. Relative abundance for characteristic m/z ratios of specific losses from MS2 and MS3 of

carbohydrates from cocoa beans.

Compound Retention time (min)

MS2 fragmentation MS3 fragmentation

Pentosyl-iminosugar (13.4)

13.4 124(100%)

Pentosyl-iminosugar (14.3)

14.3 124(100%)

Alcohol of tri-pentose 18.7 307 (100%), 349 (10%), 391 (5%)

Glycosyl-iminosugar 19.0 252 (100%), 268 (84%), 154 (51%)

206 (100%), 224 (32%), 234 (25%)

Disaccharide (19.4) 19.4 203 (100%), 275 (67%),

245(15%), 347 (53%), 305(64%)

Alcohol of disaccharide

21.4 307 (100%), 205 (77%), 262 (63%)

Trisaccharide (22.0) 22.0 365 (100%) 203 (100%), 305 (37%), 347 (22%)

Trisaccharide (23.2) 23.2 347 (100%), 365 (95%), 203 (21%)

185 (100%)

Dihexosyl glicerol 23.8 277 (100%) 185 (100%), 115 (46%)

Trisaccharide (24.8) 24.8 365 (100%), 347 (37%), 203 (10%)

203 (100%), 275 (55%), 245 (65%), 305 (31%), 185(35%)

Disaccharide (26.7) 26.7 203 (100%), 347 (33%),

275 (11%)

Trisaccharide (26.8) 26.8 347 (100%), 365 (64%), 275 (5%)

185 (100%), 203 (5%), 245 (5%)

Trisaccharide (27.4) 27.4 365 (100%), 347 (95%), 275 (5%)

275 (100%), 203 (37%), 305 (28%), 245 (10%), 347 (5%)

Disaccharide (28.5) 28.5 203 (100%), 347 (25%)

Trisaccharide (32.1) 32.1 365 (100%), 407 (33%), 467 (83%), 437 (38%), 509 (13%)

305 (100%), 275 (38%), 203 (37%), 335(1%) 245 (7%), 347 (2%)


229

Table S1.2. Carbohydrates detected by HILIC-ESI-TOF MS in cacao beans.

Peak Number

Retention Time (min)

Formula Experimental mass

Theoretical mass

Error ppm

Compound Identified

Label

1 11.3 [C6H12O6Na]+ 203.0525 203.0526 0.8 Fructose

2 12.7 [C6H14O6Na]+ 205.0686 205.0683 3.3 Mannitol

3 13.0 [C6H12O6Na]+ 203.0534 203.0526 3.7 Glucose

4 13.4 [C11H14NO6]+ 256.0821 256.0816 2.1 Iminosugar Pentosyl-

iminosugar (13.4)

5 14.3 [C11H14NO6]+ 256.0822 256.0816 2.6 Iminosugar Pentosyl-

iminosugar (14.3)

6 16.9 [C12H26NO11]+ 360.1495 360.1500 2.2 Sucrose

7 18.7 [C15H28O13Na]+ 439.1421 439.1422 2.0 Alcohol of tri-

pentose

8 19.0 [C12H16NO7]+ 286.0924 286.0921 1.2 Iminosugar Glycosyl-

iminosugar

9 19.4 [C12H22O11Na]+ 365.1051 365.1054 1.8 Disaccharide Disaccharide (19.4)

10 20.1 [C6H12O6Na]+ 203.0535 203.0526 4.4 myo-inositol

11 21.4 [C12H24O11Na]+ 367.1207 367.1211 2.4 Alcohol of

disaccharide

12 22.0 [C18H32O16Na]+ 527.1582 527.1583 2.3 Trisaccharide Trisaccharide (22)

13 22.3 [C12H22O11Na]+ 365.1047 365.1054 2.1 Melibiose

14 23.2 [C18H32O16Na]+ 527.1587 527.1583 1.7 Trisaccharide Trisaccharide

(23.2)

15 23.8 [C15H28O13Na]+ 439.1417 439.1422 1.8 Dihexosyl glycerol


(24.8)

17 26.1 [C18H32O16Na]+ 527.1590 527.1583 1.4 Raffinose



(26.8)


(27.4)



(32.1)

23 35.0 [C24H46NO21]+ 684.2540 684.2557 2.5 Stachyose


230

2. Quantitative analysis

Table S2.1. Individual values of the recovery obtained after the addition of standard.

Compound % Recovery 1 % Recovery 2 Average

Fructose 93.5 94.6 94.0

Glucose 97.4 106.8 102.1

myo-Inositol 100.2 96.1 98.1

Mannitol 98.4 105.8 102.1

Sucrose 95.8 100.0 97.9

Melibiose 109.7 101.4 105.5

Raffinose 106.9 100.2 103.5

Stachyose 99.1 114.3 106.7


231

Table S2.2a. Values of RSD for each compound of samples performed per duplicate of sample preparation.

Sample Fructose Glucose Myo-inositol

Sucrose Raffinose Stachyose Mannitol Melibiose

V-U-3 0.3 7.5 7.5 5.9 7.8 14.0 0.0 0.0

V-F-5 5.6 15.1 22.1 8.7 15.9 12.6 12.6 0.0

I-F-5 7.7 0.0 0.1 7.9 6.8 9.6 4.6 0.0

I-F-8 16.0 0.0 0.0 12.5 7.7 10.0 11.6 0.0

B-F-1 8.4 9.0 2.1 0.0 0.1 7.9 10.6 8.5

B-U-1 9.7 13.3 8.3 0.4 0.5 0.3 0.0 0.1

M-U-1 7.0 5.1 6.2 6.5 0.1 5.0 0.0 0.0

M-F-3 5.6 2.4 9.8 9.6 0.0 11.7 5.6 0.0

E-PD-OF-3 7.3 4.8 2.8 4.8 7.5 1.4 9.0 12.0

E-F-3 6.0 3.8 5.8 0.0 6.1 3.0 1.7 14.3

E-PD-OF-2 3.5 0.5 3.7 20.0 10.2 0.5 14.7 15.5

E-U-2 14.6 18.6 0.3 6.9 5.2 10.7 0.0 0.0

E-U-3 6.2 3.1 3.4 0.3 7.0 3.7 0.0 0.0

E-U-4 4.5 1.5 5.3 18.1 3.0 9.2 0.0 12.2

E-U-5 12.0 2.1 5.8 9.8 0.5 13.9 0.0 13.6

E-U-6 2.4 6.3 5.9 5.3 5.9 5.0 0.0 0.0

E-U-7 5.6 2.2 4.4 3.9 16.6 21.9 0.0 0.0

E-PD-OF-4 6.1 4.1 0.0 12.3 0.1 3.4 2.9 14.8

Average 7.1 5.5 5.2 7.4 5.6 8.0 4.1 5.1

Sample code: L1-S1-N. (L1 = Country, S1: status of fermentation; N: number of sample). PD indicate samples obtained by pre-drying beans

L1 = V (Ivory Coast), B (Brazil), M (Malaysia), E (Ecuador)

S1= U (unfermented), F (Fermented)


232

Table S2.2b. Values of RSD for each compound of samples performed per triplicate of sample preparation.

Sample Fructose Glucose Myo-inositol

Sucrose Raffinose Stachyose Mannitol Melibiose

E-U-1 12.5 14.3 13.4 9.4 7.8 14.8 0.0 0

V-F-2 9.8 16.3 0.2 13.7 1.2 5.3 7.1 0

V-F-3 9.9 13.1 12.1 10.8 2.5 11.2 7.3 0

Average 10.7 14.6 8.6 11.3 3.8 10.4 4.8 0

Sample code: L1-S1-N. (L1 = Country, S1: status of fermentation; N: number of sample).

L1 = V (Ivory Coast), E (Ecuador)

S1= U (unfermented), F (Fermented)


233

Table S2.3. Values of individual carbohydrates in unfermented samples (mg/100 g DM).

Sample Fructose Glucose Myo-inositol Sucrose Melibiose Raffinose Stachyose

V-1 32.6 75.2 112.6 328.3 16.4 366.4 130.7

V-2 27.1 43.9 96.6 230 0 408.5 100

V-3 36.3 ± 0.1 53.1 ± 4.0 121.2 ± 9.1 2317.7 ± 136.0 0 293.6 ± 22.8 103.6 ± 14.5

V-4 14.8 14.2 7.7 1345.8 0 114.9 79.9

V-5 22.1 13.1 17.3 1241.4 0 486 89.1

V-6 30.5 4.3 14.9 2634.6 0 313.7 150.6

V-7 58.4 16 19.3 611.7 0 531.5 106

V-8 86.3 50.8 85.5 735.5 22.7 201.1 172.3

T-5 49.2 103.6 55.1 1044.6 114.3 396.8 351

M-1 10.0 ± 0.7 25.4 ± 1.3 38.7 ± 2.4 3140.9 ± 204.5 0 782.6 ± 1.0 248.9 ± 12.4

M-2 15.6 37.5 29.7 4086.8 n.d. 543.7 158.7

M-3 11.3 24.2 24 1433.2 35.5 541.2 325.9

I-1 32.5 97.7 24.4 963.7 102.7 66.6 366.3

I-2 34.1 173.7 48.4 464.4 308.4 66.8 305.1

I-3 66.6 156.2 55.5 1851.3 201.2 760.2 41.3

I-4 32 81.5 61.9 1533 178.1 1392.6 353.7

E-1 21.6 ± 2.7 38.5 ± 5.5 43.6 ± 5.8 544.6 ± 51.2 0 762.5 ± 59.8 377.4 ± 55.5

E-2 40.3 ± 5.9 61.7 ± 11.5 51.6 ± 0.3 704.3 ± 48.5 0 577.6 ± 30.3 304.4 ± 32.5

E-3 70.6 ± 4.4 175.0 ± 5.4 74.0 ± 2.5 349.4 ± 1.1 0 270.2 ± 18.9 305.7 ± 11.2

E-4 73.3 ± 3.3 170.4 ± 2.5 47.3 ± 2.5 266.3 ± 48.3 16.4 ± 2.0 155.2 ± 4.7 232.1 ± 21.4

E-5 48.2 ± 5.8 56.9 ± 1.2 32.9 ± 1.9 310.3 ± 30.5 44.1 ± 6.0 131.8 ± 0.7 229.7 ± 32.0

E-6 103.4 ± 2.5 49.4 ± 3.1 65.6 ± 3.9 1037.1 ± 54.7 0 161.6 ± 9.6 201.0 ± 10.0

E-7 102.1 ± 5.7 171.3 ± 4.8 61.6 ± 2.7 536.1 ± 21.1 0 117.2 ± 19.5 179.3 ± 39.3

B-1 69.1 ± 6.7 94.1 ± 12.5 85.4 ± 7.1 265.0 ± 1.1 28.4 ± 0.0 149.7 ± 1.4 161.4 ± 0.3

Sample code: L1-N. (L1 = Country, N: number of sample). L1 = V (Ivory Coast), E (Ecuador), T (Tanzania), M (Malaysia), B (Brazil), Indonesia (I)


234

Table S2.4. Values of individual carbohydrates in fermented samples (mg/100 g DM).

Sample Fructose Glucose Myo-Inositol

Mannitol Sucrose Melibiose Raffinose Stachyose

V-2-S 191.7 ± 18.8 51.0 ± 8.3 84.7 ± 0.2 28.1 ± 2.0 48.2 ± 6.6 0 24.1 ± 0.3 22.8 ± 1.2

V-3-S 250.5 ± 24.8 104.8 ± 13.7 53.6 ± 6.5 43.6 ± 3.2 26.8 ± 2.9 0 20.2 ± 0.5 26.8 ± 3.0

V-5-S 296.4 ± 16.7 58.9 ± 8.9 58.5 ± 12.9 47.6 ± 6.0 50.6 ± 4.4 0 15.1 ± 2.4 17.8 ± 2.2

V-6-S 166.9 92.7 63.2 52.1 30 0 19.7 45.4

V-7-S 94.1 18.3 20.7 105 151.7 0 20.6 25.3

V-8-S 34.5 16.2 39.7 51.3 41.7 0 21.6 21.6

M-1-S 25.6 11.1 15.7 23.9 212.3 0 14.3 47

M-2-S 27.6 7.9 14.1 23.4 142.1 0 8.5 25.1

M-3-S 21.3 ± 1.2 4.1 ± 0.1 8.2 ± 0.8 17.8 ± 1.0 59.3 ± 5.7 0 12.4 ± 0.0 41.1 ± 4.8

I-1-S 23.7 3.6 12.5 28 67.2 18.3 22.9 80.4

I-2-S 21 0.8 25 15.7 6.9 12.4 18.2 57.3

I-5-S 33.6 ± 2.6 0 20.8 ± 0.0 28.2 ± 1.3 119.3 ± 9.4 0 25.0 ± 1.7 38.4 ± 3.7

I-6-S 36.9 1.7 0 39.7 43.3 0 0 20

I-7-S 10.3 2.4 12 15.9 1.1 0 12.5 36.7

I-8-S 24.4 ± 3.9 0 0 31.1 ± 3.6 0.8 ± 0.1 0 5.8 16 ± 1.6

I-9-S 29.5 34.5 29.2 55.9 17.8 0 29.8 4.6

E-1-S 24 31.4 13.2 18.5 39 4.1 14.8 52.6

E-2-S 37.8 41.5 24.6 7.5 53.7 16 16.3 29.1

E-3-S 46.5 ± 2.8 15.6 ± 0.6 75.7 ± 4.4 18.1 ± 0.3 0 9.1 ± 1.3 11.4 ± 0.7 13.5 ± 0.4

E-4-S 24.7 39.5 78.4 12.1 0 43.7 13.5 16.9

E-5-S 48.3 20.4 26.6 44.3 102.8 0 24.6 59.5

E-6-S 52.4 37.1 30.6 19.4 67.4 24.7 36.2 60.9

E-7-S 55.5 76.7 33.6 5.8 106 30.7 21.4 44

B-1-S 45.5 ± 3.8 36.7 ± 3.3 46.7 ± 1.0 8.5 ± 0.9 0 27.0 ± 2.3 21.5 ± 0.1 27.7 ± 2.2

T-1-S 18.7 1.4 10.4 32.2 39.8 0.1 9.7 40


235

Sample Fructose Glucose Myo-Inositol

Mannitol Sucrose Melibiose Raffinose Stachyose

T-2-CF 53.4 57.7 24.2 6 104.3 n.d. 41.2 40.6

T-3-CF 32.7 52.8 37 5.8 143.3 31.3 62.1 75.3

T-4-CF 22.9 23.7 13.4 8.3 184.5 3.6 27.3 82.5

E-1.PD 27.3 34 17.4 10 164.1 37.9 48.3 160

E-2-PD 64.5 ± 2.2 57.5 ± 0.3 75.2 ± 2.8 13.6 ± 2.0 42.5 ± 8.5 29.1 ± 4.5 48.8 ± 5.0 41.4 ± 0.2

E-3-PD 46.6 ± 3.4 59.8 ± 2.9 80.0 ± 2.2 8.9 ± 0.8 104.7 ± 5.0 75.6 ± 9.1 44.1 ± 3.3 71.5 ± 1.0

E-4-PD 61.1± 3.7 51.5 ± 2.1 n.d. 6.9 ± 0.2 37.5 ± 4.6 14.9 ± 2.2 2.0 ± 0.0 43.3 ± 1.5

Sample code: L1-N-P. (L1 = Country, N: number of sample, P: procedure of fermentation).

L1 = V (Ivory Coast), E (Ecuador), T (Tanzania), M (Malaysia), B (Brazil), Indonesia (I)

P = S (spontaneous fermentation), CF (controlled fermentation), PD (pre-drying beans).

n.d Not detected


236

3. Chemometric analysis.

Table S3.1. Values of PC loading from PCA unfermented/fermented.

p1 p2

Fructose 0.209201 -0.27659

Glucose 0.065397 -0.26476

Myo-Inositol 0.104152 -0.32901

Mannitol 0.216204 -0.09869

Pentosyl-Iminosugar (13.4) -0.08355 -0.24638

Pentosyl-Iminosugar (14.1) 0.079773 -0.24068

Sucrose -0.28744 -0.00855

Disaccharide (28.5) -0.33373 -0.00558

Disaccharide (26.7) -0.16042 -0.08887

Disaccharide (19.4) -0.30638 -0.01966

Melibiose -0.06266 -0.13047

Alcohol of disaccharide 0.07171 -0.35455

Alcohol of tri-pentose (18.7) -0.08148 -0.33099

Dihexosyl glycerol -0.33141 -0.0695

Raffinose -0.33949 -0.02683

Trisaccharide (23.2) -0.28115 -0.18294

Trisaccharide (24.9) -0.07093 -0.28825

Trisaccharide (22.0) -0.12158 -0.26569

Trisaccharide (26.8) -0.2084 -0.17864

Trisaccharide (27.5) 0.111468 -0.25558

Trisaccharide (32.1) 0.148909 -0.23949

Stachyose -0.29875 -0.0263

Glycosyl-Iminosugar 0.24595 -0.09019


237

Table S3.2. Values of PC loading from PCA of unfermented cocoa beans from different origins.

p1 p2

Fructose -0.24427 0.118709

Glucose -0.23615 0.191199

Myo-Inositol -0.06861 -0.17381

Pentosyl-Iminosugar (13.4) -0.02879 0.486568

Pentosyl-Iminosugar (14.1) -0.02471 0.427353

Sucrose 0.052454 0.467389

Disaccharide (28.5) 0.309259 0.050857

Disaccharide (26.7) 0.198153 0.016984

Disaccharide (19.4) 0.229401 0.054141

Melibiose 0.092381 -0.04547


Alcohol of tri-pentose (18.7) 0.217001 0.1408

Dihexosyl glycerol 0.327431 0.030319

Raffinose 0.288638 0.088833

Trisaccharide (23.2) 0.324858 -0.04999

Trisaccharide (24.9) 0.266088 -0.23061

Trisaccharide (22.0) 0.238134 -0.14621

Trisaccharide (26.8) 0.267677 -0.11413

Trisaccharide (27.5) -0.03335 -0.02032

Trisaccharide (32.1) 0.003014 -0.0787

Stachyose 0.220048 0.323238

Glycosyl-Iminosugar 0.198523 0.208809


238

Table S3.3. Values of PC loading from PCA of fermented beans obtained by different fermentation procedure.

p1 p2

Fructose 0.259596 -0.06995

Glucose 0.239041 -0.19842

Myo-Inositol 0.215625 -0.32334

Mannitol 0.127401 0.071675



Sucrose 0.01308 0.34584

Disaccharide (28.5) 0.052493 -0.06129

Disaccharide (26.7) 0.002952 -0.46543

Disaccharide (19.4) 0.06009 -0.02438

Melibiose 0.024894 -0.3731




Raffinose 0.196179 0.117537

Trisaccharide (23.2) 0.267125 0.196284

Trisaccharide (24.9) 0.278002 0.098071

Trisaccharide (22.0) 0.276293 0.145639

Trisaccharide (26.8) 0.284948 0.08442

Trisaccharide (27.5) 0.282278 0.096305

Trisaccharide (32.1) 0.177148 -0.3703

Stachyose -0.00638 0.269445



239

Table S3.4. Values of PC loading from PCA of spontaneously fermented beans from different origins.

p1 p2

Fructose 0.218057 0.209561

Glucose 0.244307 0.018639

Myo-Inositol 0.243504 -0.11964

Mannitol 0.028712 0.336567

Pentosyl-Iminosugar (13.4) 0.294251 0.074539

Pentosyl-Iminosugar (14.1) 0.211123 0.011582

Sucrose 0.122336 -0.0754

Disaccharide (28.5) 0.040269 0.028114

Disaccharide (26.7) 0.070896 -0.39361

Disaccharide (19.4) 0.098342 -0.02138

Melibiose 0.152285 -0.45134

Alcohol of disaccharide 0.272563 0.070531



Raffinose 0.194464 -0.22268

Trisaccharide (23.2) 0.262322 0.180779

Trisaccharide (24.9) 0.266717 0.134509

Trisaccharide (22.0) 0.239236 0.255226

Trisaccharide (26.8) 0.272138 -0.1364

Trisaccharide (27.5) 0.289387 0.047145

Trisaccharide (32.1) 0.181606 -0.30818

Stachyose 0.06152 -0.20274


Table S3.5. Carbohydrates from unfermented cocoa beans with significant differences among countries using

ANOVA test.

Carbohydrate p-value

Fructose 0.0274

Glucose 0.0131

Pentosyl-iminosugar (13.4) 0.0083

Pentosyl-iminosugar (14.1) 2.06E-05

Sucrose 0.0075

Disaccharide (26.7) 0.0216

Stachyose 0.0373


240

Table S3.6. Carbohydrates from spontaneously fermented cocoa beans with significant differences among

countries using ANOVA test.


Mannitol 0.0024

Pentosyl-iminosugar (13.4) 0.0006

Alcohol of disaccharide 0.0446

Melibiose 0.0005

Alcohol of tri-pentose 0.0013

Dihexosyl glycerol 0.0018

Trisaccharide (22.0) 0.0032

Trisaccharide (23.2) 8.11E-06


Disaccharide (26.7) 0.0167



Table S3.7. Values of t-test and p-values of the different carbohydrates considered markers of the fermentation

process performed.

Carbohydrate t-statistic p-value

Myo-inositol 2.596 0.013

Mannitol -3.017 0.004

Sucrose 6.031 3.3E-007

Disaccharide (26.7) 2.148 0.037

Melibiose 4.590 3.8E-005

Alcohol of tripentoside (18.7) 3.265 0.002


Trisaccharide (23.2) 1.971 0.055

Trisaccharide (26.8) 4.479 5.4E-005



Stachyose 3.531 0.001

Glucosyl-iminosugar -2.836 0.007


241

Table S3.8. Carbohydrates from fermented beans with significant differences among the number of days passes

of fermentation.


Fructose 0.0902

Mannitol 0.0012

sucrose 0.0804

Disaccharide 26.7 0.0035

Disaccharide 19.4 0.0441

Melibiose 8.96E-06

Raffinose 0.0022

trisaccharide 32.1 0.0174

stachyose 0.0117


242

Figure S3.1. PLS-DA of unfermented/fermented beans.

(Left) Scores plot of PLS-DA based on the entire dataset. Fermented beans are depicted by red circles, while

unfermented beans are shown as green squares. (Right) Corresponding weight plot. Colors denote VIP scores of

each variable, which indicate their discrimination power.


243

Figure S3.2. PCA score (left) and loading plot (right) of unfermented beans. Beans are color coded according to

their origin.

Figure S3.3. PCA score (left) and loading plot (right) of fermented beans. Beans are color coded according to

their origin.

Figure S3.4. PCA of fermented beans obtained by different procedure. Triangle: fermented samples performed

by controlled fermentation. Square: fermented samples carry out by pre-drying the beans.


245

Supplementary information of Chapter 5 “Analysis of minor low molecular weight

carbohydrates in cocoa beans by chromatographic techniques coupled to mass spectrometry”


246

Table S1. Characteristics of the samples under study (country, fermentation status, season of collection).

Country Fermentation status Season collection

Ivory Coast Unfermented 2014






Ivory Coast Fermented 2015



Indonesia Unfermented 2014




Indonesia Fermented 2015



Malaysia Unfermented 2014



Malaysia Fermented 2014



Brazil Unfermented 2015



Brazil Fermented 2016

Brazil Fermented 2015

Ecuador Unfermented 2014





Ecuador Fermented 2015




247

Table S2. Summary of the scyllo-inositol content from the different samples analyzed

scyllo-Inositol (µg g-1 DM)

Average SD RSD

Ivory Coast

Unfermented

219.01 2.47 1.13

69.54 6.95 10.00

traces 0.00 0.00

40.94 0.83 2.04

traces 0.00 0.00

traces 0.00 0.00

Fermented

97.32 6.69 6.87

176.51 1.38 0.78

15.48 0.87 5.59

Indonesia

Unfermented

97.63 0.85 0.87

17.70 0.42 2.37

48.67 2.37 4.87

145.34 4.28 2.94

Fermented

171.71 5.17 3.01

141.76 13.48 9.51

108.22 5.05 4.66

Malaysia

Unfermented

193.04 18.51 9.59

245.62 23.43 9.54

131.69 0.32 0.24

Fermented

469.44 19.93 4.24

303.63 24.10 7.94

370.47 20.81 5.62

Brazil

Unfermented

42.51 1.23 2.90

41.05 3.28 7.99

13.54 0.96 7.07

Fermented 90.48 5.52 6.10

63.65 3.77 5.93

Ecuador Unfermented

504.92 21.31 4.22

57.07 3.40 5.95

244.11 22.66 9.28

266.26 24.91 9.35

103.61 4.45 4.30

Ecuador Fermented

231.04 13.86 6.00

292.14 1.05 0.36

491.87 15.67 3.18


248

Table S3. Summary of the galactinol content from the different samples analyzed

Galactinol (µg g-1 DM)

Average SD RSD

Ivory

Coast

Unfermented

361.52 14.15 3.91

525.08 47.95 9.13

1083.57 91.94 8.48

537.58 38.25 7.12

1970.40 160.52 8.15

1608.51 61.03 3.79

Fermented

0.00 0.00 0.00

0.00 0.00 0.00

0.00 0.00 0.00

Indonesia

Unfermented

11.85 0.48 4.01

8.59 0.69 7.99

16.00 2.32 14.53

10.88 0.85 7.78

Fermented

0.00 0.00 0.00

0.00 0.00 0.00

0.00 0.00 0.00

Malaysia

Unfermented

789.05 29.22 3.70

196.70 17.99 9.14

163.65 8.38 5.12

Fermented

0.00 0.00 0.00

0.00 0.00 0.00

0.00 0.00 0.00

Brazil

Unfermented

27.98 2.06 7.36

141.81 7.39 5.21

traces 0.00 0.00

Fermented 0.00 0.00 0.00

0.00 0.00 0.00

Ecuador

Unfermented

150.57 11.61 7.71

430.15 15.92 3.70

506.56 9.46 1.87

128.74 8.81 6.84

1770.11 31.92 1.80

Fermented

0.00 0.00 0.00

0.00 0.00 0.00

0.00 0.00 0.00


249

Table S4. Summary of the 1-kestose content from the different samples analyzed

1-kestose (µg g-1 DM)

Average SD RSD

Ivory Coast

Unfermented

88.49 1.37 1.55

127.91 5.80 4.53

103.11 9.07 8.80

114.82 6.88 5.99

73.17 5.72 7.82

99.56 3.98 4.00

Fermented

70.76 6.04 8.53

75.75 7.00 9.24

16.05 1.55 9.64

Indonesia

Unfermented

133.49 10.88 8.15

68.49 0.24 0.35

59.60 5.36 9.00

36.13 3.59 9.94

Fermented

115.47 4.55 3.94

30.61 1.66 5.41

traces 0.00 0.00

Malaysia

Unfermented

123.60 5.06 4.09

93.01 7.93 8.52

48.22 4.45 9.23

Fermented

32.06 2.89 9.03

traces 0.00 0.00

73.07 8.18 11.19

Brazil

Unfermented

112.35 2.92 2.60

76.06 5.29 6.96

90.27 1.24 1.37

Fermented 29.86 0.79 2.63

traces 0.00 0.00

Ecuador Unfermented

79.60 1.01 1.27

56.97 1.63 2.87

87.42 4.78 5.47

92.56 1.84 1.99

108.36 9.81 9.06

Ecuador Fermented

traces 0.00 0.00

32.95 2.96 8.99

traces 0.00 0.00


250

Figure 1S. Results of one-way ANOVA analysis followed by a Fisher test as a post hoc comparison of the means

of scyllo-inositol content of unfermented beans from different origins.


251

Figure 2S. Results of t-test and graphic representation of the quantities of scyllo-inositol between

unfermented/fermented for each country.


252

Figure 3S. Results of one-way ANOVA analysis followed by a Fisher test as a post hoc comparison of the

means of scyllo-inositol content of fermented beans from different origins.


253

Figure 4S. Results of t-test and graphic representation of the quantities of 1-kestose between

unfermented/fermented for each country.


255

Supplementary information of Chapter 6 “Monitoring the changes of low molecular weight

carbohydrates in cocoa beans during spontaneous fermentation: a chemometric and kinetic

approach”


256

Table S1. Chromatographic parameters

Compound Calibration curve Pearson coefficient Range of linearity (μg /mL)

Fructose y = 0.0213x + 0.0114 0.9931 0.75 - 50.0

Glucose y = 0.0163x + 0.0135 0.9968 0.75 - 35.0

Mannitol y = 0.00341x + 0.042 0.9944 0.75 - 35.0

myo-inositol y = 0.0176x + 0.0082 0.9959 0.75 - 35.0

Sucrose y = 0.0256x + 0.034 0.9958 0.75 - 35.0

Melibiose y = 0.0162X + 0.0089 0.9950 0.75 - 35.0

Raffinose y = 0.0128x + 0.068 0.9960 0.75 - 50.0

Stachyose y = 0.0071x + 0.0065 0.9962 0.75 - 50.0


257

Table S2. Summary of the lipid content, % DM and pH from the different samples analyzed

Country Fermentation point (h) Lipid content %DM pH

Brazil 0 33.6 65.7 6.55

Brazil 24 32.7 64.8 6.56

Brazil 48 35.0 64.2 6.18

Brazil 72 31.6 59.9 5.03

Brazil 96 31.1 58.2 4.77

Brazil 120 29.1 56.6 4.63

Brazil 144 33.2 58.5 4.7

Cameroon 0 37.4 68.4 6.75

Cameroon 24 41.6 69.0 6.7

Cameroon 48 39.4 66.2 6.54

Cameroon 72 34.8 60.2 4.92

Cameroon 96 35.4 57.7 4.7

Cameroon 120 37.2 58.9 4.78

Cameroon 144 37.4 60.2 5.16

Ivory Coast 0 40.8 67.3 6.51

Ivory Coast 24 39.5 64.9 4.72

Ivory Coast 48 37.6 61.7 4.81

Ivory Coast 72 37.2 59.0 4.83

Ivory Coast 96 34.4 56.9 4.96

Ivory Coast 120 33.7 58.6 4.73

Ivory Coast 144 36.3 58.4 5.04

Ivory Coast 168 35.2 57.7 5.15


258

Country Fermentation point (h) Lipid content %DM pH

Malaysia 0 33.5 68.3 6.61

Malaysia 24 33.0 63.6 6.68

Malaysia 48 31.9 61.4 6.02

Malaysia 72 33.3 59.5 5.27

Malaysia 96 31.7 57.6 4.62

Malaysia 120 36.1 56.5 4.38

Malaysia 144 33.6 56.9 4.33

Ecuador 0 35.5 67.1 6.67

Ecuador 24 38.4 63.6 6.12

Ecuador 48 37.1 62.3 5.5

Ecuador 72 33.6 58.8 5.1

Ecuador 96 33.9 57.9 4.75

Ecuador 120 34.5 58.9 4.46


259

Supplementary information of Chapter 7 “Characterization of commercial green tea leaves by

the analysis of low molecular weight carbohydrates and other quality indicators”.


260

Figure 1S. HILIC-ESI-MS2 spectra in positive ion mode of compound tentative identified as 2-O-(β-L-

arabinopyranosyl)-myo-inositol.


261

Table 1S. Recovery values determined for each carbohydrate.

LMWC Recovery Exp 1 Recovery Exp 2 Average

Fructose 96.9 106.5 101.7

Glucose 92.1 93.2 92.7

Mannitol 89.1 109.7 99.4

Sucrose 101.8 85.4 93.6

myo-inositol 96.5 110.6 103.5

Maltose 97.1 98.6 97.8

Galactinol 103.2 104.2 103.7

Raffinose 101.8 97.3 99.6

Stachyose 107.1 97.1 102.1


262

Note for the table 2S, 3S, 4S, 5S. *Sample code: J: Japan, SK: South Korea, P: Portugal, C: China, I: Iran, N:

Nepal, SI: South India, DJ: Darjeeling, A: Assam, SL: Sri Lanka. **tr = traces .

Table 2S. Individual LMWC content of each sample within the different groups of countries.

mg carbohydrate /g tea

Sample Fructose Glucose Mannitol Sucrose Maltose Galactinol

2-O-(β-L-

arabinopyranosyl)-

myo-inositol

myo-

inositol raffinose stachyose

J1 6.94 4.23 tr** 27.58 0.17 3.70 6.99 4.07 3.66 1.96

J2 6.17 7.97 tr 25.86 tr 4.83 4.31 3.70 4.39 1.65

J3 2.07 0.68 0.21 23.94 0.74 0.70 28.63 8.77 1.93 0.00

J4 5.83 5.92 0.04 64.46 0.65 6.30 4.52 2.09 5.26 2.79

J5 6.00 3.21 tr 42.63 0.20 1.82 9.36 2.01 2.61 1.03

J6 9.71 7.38 0.06 23.98 0.64 1.72 11.10 3.06 2.75 1.18

J7 9.36 7.36 tr 56.93 1.10 11.49 9.17 3.67 5.31 2.88

J8 4.12 2.98 0.09 16.51 0.18 0.60 10.48 1.88 1.70 0.89

SK1 4.32 2.74 tr 11.19 tr tr 15.01 2.18 0.95 tr

SK2 7.50 6.38 tr 26.76 1.09 1.25 13.00 2.15 2.40 0.95

SK3 1.05 0.40 tr 18.78 0.15 tr 13.32 1.45 1.14 0.00

SK4 0.81 0.51 tr 19.16 0.28 tr 13.45 2.55 0.99 0.00

SK5 0.93 0.40 tr 17.90 0.19 tr 12.57 2.25 1.08 0.00

SK6 1.63 0.45 tr 20.20 0.60 tr 7.17 2.46 1.64 tr

SK7 0.30 0.11 tr 19.09 0.22 tr 13.38 2.57 1.15 0.00

P1 11.37 7.51 0.20 34.21 1.32 0.64 8.37 11.65 2.67 0.98

P2 9.77 7.72 0.24 108.10 1.52 2.96 12.69 11.32 5.03 1.77

P3 14.85 13.54 0.35 60.58 2.54 3.14 10.24 15.87 4.88 1.65

C1 6.24 5.07 tr 69.73 0.31 0.60 70.92 3.47 2.64 0.91

C2 6.92 4.38 tr 25.20 0.10 3.17 7.73 3.73 4.10 1.62

C3 5.14 3.27 tr 52.33 0.44 1.88 10.09 4.77 3.39 1.27

C4 2.67 0.72 tr 21.38 tr 0.32 12.31 7.29 1.92 tr

C5 1.00 0.62 tr 18.18 0.25 0.40 11.72 5.66 2.27 tr

C6 0.63 0.33 tr 20.01 tr tr 15.94 2.05 1.40 tr

C7 2.72 2.21 tr 20.68 0.17 tr 13.07 3.64 1.72 tr

C8 8.98 3.78 0.08 39.00 0.36 1.16 10.42 5.18 5.30 1.73

I1 4.95 1.94 tr 20.91 0.13 2.45 5.17 2.87 3.99 1.49

I2 4.03 3.77 tr 17.95 0.25 3.61 3.77 2.20 3.43 1.25

I3 5.72 8.04 tr 16.62 0.49 4.26 8.41 3.71 4.02 1.34

I4 4.56 3.72 tr 98.31 0.78 0.91 8.12 3.97 2.09 0.87

I5 7.87 9.24 tr 29.46 0.61 2.79 8.66 5.67 4.35 1.70

I6 3.12 2.85 0.52 21.30 0.58 0.93 13.89 5.04 1.64 0.83

N1 2.71 3.15 0.85 23.23 1.12 1.32 6.27 5.25 1.49 1.13

N2 2.98 1.33 1.07 15.55 0.21 tr 12.46 4.47 0.89 tr

N3 2.98 2.28 0.20 20.31 1.19 1.32 14.09 5.67 1.55 0.87

N4 2.07 1.17 0.31 19.10 0.66 tr 14.01 3.69 1.08 tr

N5 3.97 1.34 tr 16.70 0.07 tr 16.38 3.51 1.32 tr

SI1 15.42 12.67 0.67 54.13 1.54 7.70 13.91 6.53 4.03 2.69

SI2 7.75 5.68 tr 40.06 0.75 4.10 8.58 3.16 3.42 2.16

SI3 2.08 0.73 tr 26.38 0.55 tr 9.30 3.35 1.26 0.87

SI4 2.55 4.87 tr 17.59 0.43 1.16 11.82 2.81 2.65 1.13

SI5 2.22 0.88 tr 21.40 0.45 0.56 5.83 4.32 1.69 1.14

SI6 6.27 6.15 tr 27.04 0.62 4.85 10.31 4.66 4.04 2.11

DJ1 2.73 3.82 tr 25.32 0.35 2.17 14.45 4.31 3.56 1.17

DJ2 8.93 3.75 4.28 31.81 1.04 4.53 18.28 10.39 4.87 2.29

DJ3 3.35 2.43 0.28 13.14 tr 0.37 10.85 2.53 1.74 0.85

A1 2.19 1.87 0.07 17.43 0.05 0.98 12.26 2.98 2.13 1.10

A2 2.82 2.88 tr 20.04 0.24 2.39 10.44 4.21 3.23 1.31

A3 3.79 2.91 0.12 20.11 0.02 1.48 10.19 4.06 2.53 1.22

A4 3.24 2.79 0.06 21.29 0.19 0.97 10.94 3.07 2.31 1.14

SL1 1.32 1.51 tr 18.34 0.04 2.09 8.35 2.31 2.45 1.09

SL2 1.82 1.70 0.33 17.50 tr 0.47 7.57 3.22 1.46 tr

SL3 1.34 1.74 0.10 16.54 tr 1.37 6.81 2.39 1.70 0.91

SL4 1.24 0.62 tr 18.46 0.01 2.28 7.88 3.68 2.37 1.00

SL5 2.63 4.98 0.98 24.89 0.43 0.39 11.46 4.42 1.56 tr

SL6 0.97 1.46 tr 18.02 0.14 2.46 9.44 1.68 2.58 1.08


263

Table 3S. Individual soluble solids of each sample within the different groups of countries.

Sample Soluble Solids (%)

J1 30.33

J2 25.92

J3 30.42

J4 35.28

J5 41.92

J6 40.48

J7 34.05

J8 26.13

SK1 30.20

SK2 37.70

SK3 37.72

SK4 37.98

SK5 42.78

SK6 39.20

SK7 36.92

P1 37.23

P2 35.68

P3 37.72

C1 37.03

C2 34.60

C3 40.98

C4 43.00

C5 50.88

C6 36.23

C7 39.12

C8 29.85

I1 25.57

I2 25.32

I3 39.15

I4 41.48

I5 32.82

I6 38.63

N1 44.15

N2 57.14

N3 50.81

N4 46.28

N5 53.80

SI1 41.87

SI2 40.57

SI3 42.60

SI4 32.48

SI5 34.38

SI6 30.88

DJ1 32.00

DJ2 48.13

DJ3 30.25

A1 35.10

A2 40.57

A3 47.68

A4 40.47

SL1 40.43

SL2 41.20

SL3 39.40

SL4 44.03

SL5 37.57

SL6 47.55


264

Table 4S. Individual color parameters of each sample within the different groups of countries.

Sample L* a* b* C* Saturation Hue angle

J1 14.61 -3.08 1.71 3.52 0.24 179.49

J2 12.89 -2.66 0.57 2.72 0.21 179.79

J3 15.94 -2.33 -0.82 2.47 0.15 180.34

J4 15.16 -3.20 1.30 3.45 0.23 179.61

J5 17.70 -2.56 0.47 2.60 0.15 179.82

J6 16.64 -1.85 5.48 5.78 0.35 178.75

J7 17.20 -3.07 1.46 3.40 0.20 179.56

J8 16.33 -2.60 -0.87 2.74 0.17 180.32

SK1 15.67 -0.38 9.54 9.55 0.61 178.47

SK2 15.48 -2.88 1.67 3.33 0.21 179.48

SK3 17.61 -1.33 3.20 3.47 0.20 178.82

SK4 13.93 -1.49 0.19 1.50 0.11 179.87

SK5 16.25 -2.27 0.40 2.31 0.14 179.83

SK6 17.79 -0.96 2.71 2.88 0.16 178.77

SK7 13.95 -1.88 -1.25 2.26 0.16 180.59

P1 11.84 -1.58 7.49 7.66 0.65 178.64

P2 12.71 -2.24 6.39 6.78 0.53 178.77

P3 15.02 -2.41 7.22 7.61 0.51 178.75

C1 12.01 -2.10 0.32 2.12 0.18 179.85

C2 13.09 -2.32 7.38 7.74 0.59 178.73

C3 13.79 -1.64 6.11 6.33 0.46 178.69

C4 15.92 -2.39 0.67 2.48 0.16 179.73

C5 15.67 -1.34 7.73 7.85 0.50 178.60

C6 14.35 -1.58 -1.60 2.25 0.16 180.79

C7 16.56 -1.24 6.39 6.51 0.39 178.62

C8 13.00 -2.23 6.15 6.54 0.50 178.78

I1 10.49 -1.56 6.03 6.22 0.59 178.68

I2 11.69 -2.63 2.37 3.54 0.30 179.27

I3 13.79 -2.26 1.49 2.71 0.20 179.42

I4 18.82 -2.11 9.79 10.02 0.53 178.64

I5 13.83 -2.37 3.80 4.47 0.32 178.99

I6 16.99 -0.36 12.30 12.30 0.72 178.46

N1 12.19 -1.46 4.12 4.37 0.36 178.77

N2 16.35 -2.58 3.81 4.60 0.28 179.02

N3 23.26 -2.56 8.90 9.26 0.40 178.71

N4 15.40 -2.45 -0.24 2.46 0.16 180.10

N5 16.16 -1.09 8.08 8.15 0.50 178.56

SI1 13.16 -2.75 1.81 3.29 0.25 179.42

SI2 15.55 -3.07 6.77 7.43 0.48 178.85

SI3 14.98 -2.20 0.37 2.23 0.15 179.83

SI4 12.67 -1.69 -0.47 1.75 0.14 180.27

SI5 13.38 -1.77 -1.91 2.61 0.19 180.82

SI6 15.29 -2.62 0.81 2.74 0.18 179.70

DJ1 13.16 -1.25 1.52 1.97 0.15 179.12

DJ2 19.62 -2.38 11.60 11.84 0.60 178.63

DJ3 13.87 -1.59 3.74 4.06 0.29 178.83

A1 12.68 -1.41 2.45 2.83 0.22 178.95

A2 14.18 -1.42 4.64 4.86 0.34 178.73

A3 18.64 -1.93 11.48 11.64 0.62 178.60

A4 13.47 -1.33 5.77 5.92 0.44 178.66

SL1 14.77 -2.17 2.03 2.97 0.20 179.25

SL2 13.47 -1.67 6.84 7.04 0.52 178.67

SL3 15.20 -2.03 3.72 4.23 0.28 178.93

SL4 17.71 -1.21 14.27 14.32 0.81 178.51

SL5 14.11 -2.11 2.74 3.46 0.25 179.09

SL6 15.60 -2.19 7.89 8.19 0.52 178.70


265

Table 5S. Individual Trolox-E and GA-E values of each sample within the different groups of countries.

mg /g tea

Sample GA-E TROLOX-E

J1 0.09 0.19

J2 0.07 0.15

J3 0.09 0.19

J4 0.06 0.28

J5 0.07 0.29

J6 0.16 0.31

J7 0.14 0.27

J8 0.06 0.25

SK1 0.02 0.06

SK2 0.09 0.34

SK3 0.13 0.26

SK4 0.10 0.20

SK5 0.11 0.22

SK6 0.15 0.30

SK7 0.12 0.23

P1 0.04 0.12

P2 0.07 0.27

P3 0.03 0.10

C1 0.04 0.19

C2 0.10 0.19

C3 0.14 0.56

C4 0.05 0.23

C5 0.17 0.33

C6 0.05 0.14

C7 0.09 0.38

C8 0.02 0.10

I1 0.03 0.13

I2 0.05 0.11

I3 0.11 0.22

I4 0.09 0.19

I5 0.08 0.15

I6 0.15 0.30

N1 0.08 0.21

N2 0.21 0.39

N3 0.05 0.19

N4 0.18 0.30

N5 0.08 0.25

SI1 0.04 0.18

SI2 0.04 0.18

SI3 0.05 0.21

SI4 0.04 0.17

SI5 0.18 0.70

SI6 0.20 0.40

DJ1 0.12 0.24

DJ2 0.06 0.16

DJ3 0.11 0.21

A1 0.16 0.30

A2 0.16 0.31

A3 0.14 0.28

A4 0.20 0.79

SL1 0.17 0.33

SL2 0.07 0.23

SL3 0.07 0.32

SL4 0.05 0.20

SL5 0.05 0.21

SL6 0.05 0.21


266

Table 6S. Values of PC loading from the figure 3, PCA from LMWC content, soluble solids, color paramenters,

antioxidant activity values of the samples under study.

Name Loadings 1 Loadings 2

Fructose -0.38 0.05

Glucose -0.36 0.11

Mannitol -0.12 -0.15

Sucrose -0.27 0.05

Maltose -0.30 0.01

Galactinol -0.31 0.14

2-O--L-arabinopyranosyl-myo-inositol 0.03 0.04

myo-inositol -0.26 -0.10

Raffinose -0.35 0.10

Stachyose -0.34 0.11

Soluble solids 0.06 -0.27

C* -0.12 -0.44

Saturation -0.15 -0.41

Hue angle 0.12 0.39

GA-E 0.15 tr

Trolox-E 0.13 0.01

L* 0.03 -0.22

a* 0.20 -0.26

b* -0.12 -0.45

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