Food Chemistry 217 (2017) 766–772

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Food Chemistry

journal homepage: www.elsevier .com/locate / foodchem

Analytical Methods

Combination of 1H NMR and chemometrics to discriminate manukahoney from other floral honey types from Oceania

http://dx.doi.org/10.1016/j.foodchem.2016.09.0270308-8146/� 2016 Elsevier Ltd. All rights reserved.

⇑ Corresponding author.E-mail address: FreddyThomas@Eurofins.com (F. Thomas).

Marc Spiteri a, Karyne M. Rogers b, Eric Jamin a, Freddy Thomas a,⇑, Sophie Guyader a, Michèle Lees a,Douglas N. Rutledge c

a Eurofins Analytics France, Rue Pierre Adolphe Bobierre, B.P. 42301, F-44323 NANTES Cedex 3, FrancebNational Isotope Center, GNS Science, 30 Gracefield Road, Seaview, P O Box 31 312, Lower Hutt 5040, New ZealandcAgroParisTech, UMR1145 Ingénierie Procédés Aliments, 16, rue Claude Bernard, 75005 Paris, France

a r t i c l e i n f o

Article history:Received 4 December 2015Received in revised form 30 May 2016Accepted 5 September 2016Available online 07 September 2016

Keywords:HoneyManukaNuclear magnetic resonanceAuthenticityLeptosperinMethylglyoxal

a b s t r a c t

Manuka honey is a product produced essentially in New Zealand, and has been widely recognised for itsantibacterial properties and specific taste. In this study, 264 honeys from New Zealand and Australia wereanalysed using proton NMR spectroscopy coupled with chemometrics. Known manuka markers,methylglyoxal and dihydroxyacetone, have been characterised and quantified, together with a newNMRmarker, identified as being leptosperin. Manuka honey profiling using 1H NMR is shown to be a pos-sible alternative to chromatography with the added advantage that it can measure methylglyoxal (MGO),dihydroxyacetone (DHA) and leptosperin simultaneously. By combining the information from these threemarkers, we established a model to estimate the proportion of manuka in a given honey. Markers of otherbotanical origins were also identified, which makes 1H NMR a convenient and efficient tool, complemen-tary to pollen analysis, to control the botanical origin of Oceania honeys.

� 2016 Elsevier Ltd. All rights reserved.

1. Introduction

Honey is a natural product produced by bees after nectar orhoneydew collection, maturation and storage inside the beehive.Traditionally it is used as a sweetener for foodstuffs and its healthbenefits have been widely recognised since antiquity including itsskin healing properties or as a digestive aid. Nowadays, science istending to discover or re-discover these properties. Over the pastfew years, one honey in particular has made an internationalimpact due to its exceptional antiseptic properties: manuka honey.Manuka (Leptospermum scoparium) is a scrub-type tree that growsonly in New Zealand and some parts of Australia. Its nectar con-tains a specific molecule, dihydroxyacetone (DHA), that convertsinto methylglyoxal (MGO) during maturation and aging of thehoney (Adams, Manley-Harris, & Molan, 2009). It is this lattermolecule that is primarily responsible for the strong antimicrobialactivity of the honey. Of course, with rising interest comes risingdemand from consumers, leading to escalating prices, but also toincreasing fraud risk. Honey is already known as a matrix that iseasily subjected to fraud or mislabelling, but in the specific caseof manuka honey, this potential is reaching critical levels. In a

recent article from the Daily Mail, it is said that there are threetimes more jars of manuka honey sold around the world than theactual production of New Zealand (Morgan, 2015). According tothe Ministry for Primary Industries (MPI) around 19,700 tonnesof honey was produced in New Zealand in 2015, with honeyexports estimated at 9046 tonnes ($223 million NZD) whichincluded both manuka and other specialty floral types(Apiculture, 2015). However it is estimated that more than10,000 tonnes of New Zealand manuka honey is sold globally eachyear (Creasey, 2014). Recently stricter labelling criteria have comeinto place to regulate the labelling of manuka honey in New Zeal-and and ensure honey can be linked directly back to a producer fortighter quality assurance and authenticity control (Manuka, 2016).

Quality control testing of manuka honey is basically the same asfor any other honey, but it does present some particularities:manuka honey with higher MGO contents (above 250 mg/kg) hasa tendency to show false positive results for sugar adulterationwhen tested using the AOAC 998.12 C4 sugar detection test(AOAC Official Method 998.12, 2013): an explanation for thisphenomenon has been proposed by Rogers, Grainger, andManley-Harris (2014). They discovered the bioactive ingredientsin manuka honey could fractionate the honey protein postulatedthis could result in a larger isotopic difference measured betweenthe whole honey and its corresponding protein, artificially inflating

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the C4 sugar calculation. Another problem is pollen analysis, whichis the main technique to determine the botanical and geographicalorigin of honeys. Manuka pollen is morphologically similar toanother plant growing and blossoming around the same time inthe same areas: kanuka (Kunzea ericoides). Apart from their simi-larities in pollen and common name, the two nectars do not havethe same chemical composition, as kanuka nectar does not containany DHA (Adams et al., 2009), making kanuka honey MGO-free.

Several studies by other researchers have presented moleculesthat could potentially discriminate manuka honey from others(Adams et al., 2008; Beitlich, Koelling-Speer, Oelschlaegel, &Speer, 2014; Gresley et al., 2012; Kato et al., 2014; Stephenset al., 2010), but the proposed methods are usually cumbersome,requiring several approaches to be combined. Very recently apatent has been written to present another compound, leptosperin,as a specific key marker for Manuka honey [EP 2 774 932 A1] (Kato,2013). A previous article by our team on the general application ofNMR profiling for honey authenticity testing (Spiteri et al., 2015)showed its ability to simultaneously detect sugar syrup additionand miscellaneous quality discrepancies such as overheating orfermentation. We present here a comprehensive work on a largesuite of honeys coming from Oceania, using 1H NMR analysis,showing that this technology can be used as a convenient methodto characterise mono-floral honeys from this part of the world,qualify and quantify the mono-floral manuka honey versus otherfloral types.

2. Material and methods

2.1. Material

264 authentic honey samples were collected from New Zealand(254) and Australia Gresley et al., 2012. The labelling of the sam-ples was based on pollen analysis, physical and organoleptic test-ing and bee keeper information. Samples were labelled as comingfrom various botanical origins, among them of course manuka(Leptospermum scoparium) and kanuka (Kunzea ericoides), but alsokamahi (Weinmannia racemosa), pohutukawa (Metrosiderosexcelsa), clover (Trifolium repens), rewarewa (Knightia excelsa),and several honeys labelled as multifloral. Standards of 1,3-dihydroxyacetone (DHA, purity >97%) and methylglyoxal (MGO,40% solution in H2O) were purchased from Sigma-Aldrich. Lep-tosperin was measured and calibrated from a suite of referencehoneys analysed using an established Liquid Chromatographymethod (Kato et al., 2012, 2014).

2.2. NMR method

For NMR preparation, the procedure was adapted fromOhmenhaeuser, Monakhova, Kuballa, and Lachenmeier (2013).Honey must be liquid and homogeneous before preparation. Allhoney samples were stirred for around 30 s before sampling toensure a homogeneous mix. If the honey was not liquid it wasplaced in an oven at 50 �C for 2 h, until all crystals were dissolved;then the honey was cooled by placing samples in a water bath at20 �C. The moisture content was obtained for each honey accordingto the method ‘‘Determination of moisture, refractometric method”(International Honey Commission, 2009). Exactly 200 mg ofmoisture-free honey (corresponding to about 240 mg of a honeysample with about 20% water) was weighed and combined with300 lL of NMR buffer. The NMR buffer solution was prepared using10.21 g of KH2PO4 (Sigma-Aldrich, purity >99.5%) and 9.8 mg ofsodium azide (NaN3, Sigma-Aldrich, purity >99%) dissolved in50 mL of pure water. The pH was adjusted to 4.5 with H3PO4 85%(Sigma-Aldrich) or NaOH 5 M (prepared by dissolving 200 g of

NaOH pellets Riedel-de Haën in 1 L of pure water). 700 lL of TypeI ultrapure water and 100 lL of the NMR lock solution was thenadded. The NMR lock solution was prepared by combining 50 mgof trimethylsilyl propionate (TSP, Sigma-Aldrich, purity >98%) dis-solved in 50 mL of D2O (Sigma-Aldrich, purity >99.8%).

All NMR measurements were performed on a Bruker Avance400 Ultrashield spectrometer (Bruker BioSpin, Rheinstetten,Germany) equipped with a 5 mm BBI probe with Z-gradient coils,using a SampleXPress autosampler (Bruker BioSpin, Rheinstetten,Germany). 1H NMR spectra were acquired at 301.8 K without sam-ple spinning. 64 scans of 65 k points with 4 prior dummy scanswere acquired with a spectral width of 20 ppm, a receiver gain of32, and an acquisition time of 4.096 s. Recycling delay and mixingtime were 8 s and 0.01 s respectively. Water suppression wasachieved using the NOESY-presaturation pulse sequence (Bruker1D noesygppr1d pulse sequence) with irradiation at the water fre-quency (1881.2 Hz) during the recycle and mixing time delays. Thedata were acquired automatically under the control of ICON-NMR(Bruker BioSpin, Rheinstetten, Germany), requiring about 20 minper sample (including 5 min for temperature equilibration oncethe tube is inside the magnet, before NMR acquisition). Tuningand matching of each tube was done automatically by the NMRinstrument (atma) followed by fine shimming (topshim). AllNMR spectra were phased (with a line-broadening of 0.3 Hz) usingthe Bruker routines ‘‘efp” and ‘‘apks”, baseline-corrected, and offsetcalibrated by the TSP signal at 0.0 ppm. Quantification was donethe same way as (Spiteri et al., 2015). To summarise, the peaks ofmolecules of interest at 2.30 ppm, 4.41 ppm and 7.43 ppm weremanually integrated using Topspin software (Bruker BioSpin,Rheinstetten, Germany) after baseline correction, which aftermultiplication with a response factor specific to the molecule, ledto a concentration value. These factors were determined usingstandards or alternative quantification method (see below).

2.3. Data pretreatment

The entire NMR spectrum was converted to .txt format andtransferred to a computer equipped with Matlab version R2009a(The MathWorks, Natick, Massachusetts, USA) with Statistics Tool-box. The non-informative zones of the spectra (beyond 10 ppm andbefore -1 ppm) were removed. Given the dynamic range of theNMR spectrum (from 0 to 107) and to facilitate the data treatment,the spectra were segmented into regions with peaks of similarintensities: from 0.7 to 3 ppm, 3–4.1 ppm, 4.1–4.6 ppm, 4.9 to5.5 ppm, and 5.5–10 ppm. The zone between 4.1 and 5.5 ppmwas cut in two in order to get rid of the residual water peak. In eachsegment, spectra were aligned using the icoshift routine (Savorani,Tomasi, & Engelsen, 2010), and the baseline was usually correctedwith the backcor function (Mazet, Carteret, Brie, Idier, & Humbert,2005).

2.4. LC-TOF method

For LC-TOF analyses, 26 of the 264 samples were prepared usinga modified QuEChERS (Quick Easy Cheap Efficient Rugged and Safe)method (Lehotay, 2007) based on Cotton et al. (2014) using anhy-drous MgSO4 (Sigma-Aldrich, purity P99.5%) and NaCH3COO(Sigma-Aldrich, purity P99.0%) as salts. Final extracts in 90/10water/methanol (Sigma-Aldrich, ultra chromasolv) were analysedby UPLC-TOF-MS on a Xevo G2-S TOF (Waters, Manchester, UK)equipped with an Acquity I Class UPLC (Waters, Manchester, UK).Analyses were carried out using positive electrospray ionization.Mass resolution was 30.000 FWHM at leucine enkephalin mass(556.2771 Da) in positive ionization mode. Instrumental settingswere: source capillary 1 kV, sampling cone 20, source offset 80,source temperature 120 �C, desolvation temperature 550 �C, cone

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gas flow 50 L/h, desolvation gas flow 1000 L/h, dynamic rangeextended, acquisition range 50–1000 Da, scan time 0.2 s, centroidmode, collision energy 4 eV. The mass value was continuously cor-rected using a solution of leucine enkephalin at 1 ng/mL (Sigma-Aldrich, purity >95%). Chromatographic separation was achievedwith a 1.7 lm, 2.1 mm � 100 mm, BEH C18 column (Waters,Manchester, UK) using a linear gradient from 2% to 100% methanolwith 5 mM NH4CH3COOH (Sigma-Aldrich, purity P99.0%) and0.02% formic acid (Fisher Chemical, Optima LC/MS grade) for14 min (the other phase was water with same additives). The elu-tion flow was set to 0.45 mL/min and the column temperature wasmaintained at 45 �C. Data files were acquired with MassLynx soft-ware version 4.1 (Waters, Manchester, UK).

2.5. Statistics

Multivariate analysis for NMR was carried out using MatlabR2009a with Statistics Toolbox. For signal extraction, IndependentComponents Analysis (ICA) using the Joint Approximate Diagonal-isation of Eigenmatrices (JADE) algorithm (Rutledge & Jouan-Rimbaud Bouveresse, 2013) was applied to the dataset. ICA decom-poses the spectral data matrix into two matrices: one containing‘‘source” signals extracted from the spectra and the other withthe relative proportions of each ‘‘source” signal (or IndependentComponent – IC) in each spectrum. This facilitates the identifica-tion of peaks that belong to the same molecule, thereby facilitatingtheir comparison with a database for identification.

3. Results and discussion

3.1. Markers and statistics

The first goal of this study was to discriminate manuka honeyfrom all other honeys, especially kanuka honey. ICA_by_Blocks(Bouveresse, Moya-Gonzalez, Ammari, & Rutledge, 2012) was usedto determine the number of ICs in each segment and the ICs andassociated proportions were then examined to determine whichones could discriminate those samples. Two of these ICs, shownin Fig. 1, corresponded to methylglyoxal (signals at 2.30 ppm and1.38 ppm) and dihydroxyacetone (signal at 4.41 ppm).

Fig. 1. Signals of methylglyoxal (a) and dihydroxyacetone (b) extracted by ICA: above aHoneys that were labelled as manuka are colored in blue, others are colored in red. (For ithe web version of this article.)

The ICA process easily managed to extract the signals of MGOand DHA. DHA and MGO molecules are the primary markers ofmanuka honey. Fig. 1 shows that most of the blue dots (honeyslabelled as manuka) are indeed those with the highest proportion,as they contain more MGO and DHA. Although similarities can beseen between the two graphs, correspondence is not perfect asDHA is the precursor of MGO, a low concentration of one moleculedoes not imply a low concentration for the other. A high DHA con-tent with a low MGO content means that a honey is fresh (conver-sion has not happened yet), while a low DHA content with highMGO content indicates an aged honey (almost all DHA has con-verted to MGO). Still, a high DHA content indicates that moreMGO is (or will be) present. We also observe that some honeyslabelled with botanical origins other than manuka may containsome DHA and MGO: those are mostly multifloral honeys thatmay contain some portion of manuka honey.

NMR allows the simultaneous quantification of both of thesemolecules. This could serve as a first control for botanical origin,and the MGO content also confirms label claims and gives con-sumers a direct correlation of the antiseptic capacity. However,these molecules can individually be purchased from chemicalsuppliers and could be fraudulently added to more common andcheaper honey to produce fake manuka honey. Therefore, othersignals were investigated in order to provide a confirmation ofassessment. Phenyllactic acid was found to be common to bothmanuka and kanuka, as already mentioned by Stephens et al.(2010). To improve signal selection, correlation coefficients werecalculated between the proportions associated with the DHA signaland the intensity of each variable in the spectra. The mean spec-trum was calculated and each point multiplied by the correspond-ing correlation coefficient. The resulting weighted mean spectrumwas then plotted using the correlation coefficient to color eachpoint in the spectrum. Another significant signal peak in thearomatic region was thus shown to have a strong correlation withMGO or DHA contents (Fig. 2a). The ICA results were then exam-ined to confirm that this signal was extracted (Fig. 2b).

This marker, represented by a singlet at 7.42 ppm (no othersignal linked to this marker could be found, as they were probablyhidden by other stronger signals) was compared to purchasedchemical standards previously referenced in the bibliography andwhich may have matched the signal, such as 3,4,5 trimethoxyben-zoic acid, and methylsyringate. However, none of these contained

re the extracted signals, and below are the proportion of the signal in each sample.nterpretation of the references to color in this figure legend, the reader is referred to

Fig. 2. a) Colouring of the 7–7.8 ppm region based on correlation with DHA proportions: the peak at 7.42 ppm shows a very dark-red color, indicating a strong correlationwith DHA and b) ICA-extracted signal of a manuka marker: above is the signal corresponding to the molecule, and below are the proportions of that signal in each sample.Honeys that were labelled as manuka are colored in blue, others are colored in red. (For interpretation of the references to color in this figure legend, the reader is referred tothe web version of this article.)

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the peak. Then, it was hypothesised that the peak could be themanuka specific marker, leptosperin, recently isolated and identi-fied by Kato et al. (2012). They had also analysed the moleculeby NMR and found a singlet at 7.34 ppm, but the solvent used

was methanol instead of water. We surmised the difference inchemical shift with our spectra could be due to the solvent. Indeed,we analysed some manuka honey in methanol and found the samepeak at 7.34 ppm. Concentrations of this molecule were calculated

Fig. 3. Correlation between DHA and MGO quantification by HPLC and NMR.

Fig. 4. Discrimination between 100% Manuka honey and other floral type honeys by combining the quantification of the 3 characteristical markers using PLS-DA approach.

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by assuming the signal was due to this molecule. We analysedsome manuka honeys containing different leptosperin contentpreviously quantified by UPLC, and a response factor could thenbe calculated from the linear regression between the intensity ofthis signal and the UPLC concentration. This response factor wasbe then used to calculate the leptosperin content in all manukahoneys analysed by 1H NMR, and the observed concentrationrange went from 5 to 860 mg/kg (8–1480 nmol/g). By comparingthese levels to manuka honey samples with similar UMF values,results were in accordance to those of Kato and his team (Katoet al., 2014). We also confirmed the identification of this compoundby LC-TOF analysis: using positive ionisation we found a marker atthe exact weight m/z = 554.2085 Da corresponding to the adduit[M+NH4]+ of leptosperin, and using negative ionisation at theexact weight m/z = 535.1663 Da corresponding to [M�H]�.

DHA and MGO were quantified through NMR, and results werecompared to values obtained using an HPLC method (Fig. 3). A

satisfactory agreement was obtained in most of the cases, whichconfirms the ability of NMR to accurately quantify these key-components.

3.2. Authentication of manuka honey

Quantification of these three parameters was undertaken on 84authentic monofloral Manuka honey (classified as such by pollenanalysis) and 183 other monofloral type honeys from Oceania(clover, kamahi, kanuka, lotus, rewarewa, pohutukawa, tawari,vipers bugloss, thyme, blue borage, ling, rata). As expected, these2 groups present different averages and ranges (DHA: Manuka:average 1154 mg/kg, 95% range [50; 2515]; non-Manuka: average347 mg/kg, 95% range [0; 1332]; MGO: Manuka: average 672 mg/kg, 95% range [18; 1555]; non-Manuka: average 233, mg/kg 95%range [0; 940]; Leptosperin: Manuka: average 784 mg/kg, 95%range [62; 1556]; non-Manuka: average 394 mg/kg, 95% range

Fig. 5. NMR marker signal for kamahi honey: the spectra of all kamahi labelled honeys are overlayed, the specific marker is indicated by the rectangle.

M. Spiteri et al. / Food Chemistry 217 (2017) 766–772 771

[0; 980]). Nevertheless, there was some overlap due to the fact thatmost of these samples are in fact multifloral honeys, as in naturebees will rarely collect nectar from only one floral source. Basedon these three parameters, a PLS-DA model was built to discrimi-nate manuka honey from the others origins. Before model con-struction, the 100 samples of honey with kanuka, kamahi andmixtures with manuka type were removed. Moreover, 45 sampleswere considered as outliers due to a doubt about their floral originas manuka or other honey types. Consequently, 122 honey sampleswere used to build the PLS-DA model. Two data sets were definedby randomly selecting 85% (104 samples) as the training set to beused for the construction of the model. The test set was createdwith the remaining 15% (18 samples) and used for model valida-tion. Since there are only three initial variables in the data set forthe authentication of manuka type, PLS-DA models were createdand tested with one, two and three latent variables. Both the cali-bration and prediction correct classification rates were maximalfor the model with three latent variables, resulting in a satisfactorydiscrimination (89% of correct classification for the validation set)between the 100% Manuka honeys and the other samples as shownin Fig. 4.

Aside from the manuka honey discrimination markers foundusing NMR profiling, some others signals extracted using ICA ledto interesting observations of other floral types.

Kamahi (Weinmannia racemosa) is another native plant growingin New-Zealand which flowers at a similar time as manuka. NMRallowed us to observe and detect a marker (not yet identified) forthis floral type (Fig. 5), and therefore to detect blends of this sourcewith other honeys, especially pure manuka.

One other remarkable observation using NMR profiling was theclear discrimination between manuka honey sourced from Aus-tralia and New Zealand, because of co-occuring signals in the for-mer that were identified as markers of eucalyptus honeys.Although New Zealand cannot import honey for blending, thiswould be a useful application to prevent New Zealand honey beingmixed with Australian honey and fraudulently labelled as Made inNew Zealand.

4. Conclusion

This study used proton NMR profiling as a convenient way tocharacterise New Zealand- and Australia-specific mono-floral hon-eys (especially manuka honey), without the need for other moretime consuming analytical techniques such as pollen analysis.NMR was used as a rapid, low cost screening method to identifyand quantify three known markers for manuka honey: DHA,MGO (Donarski, Roberts, & Charlton, 2010) and, for the first timeby NMR, leptosperin. In spite of a large variability in the concentra-tions observed, the simultaneous use of these three compounds ina statistical model allows a confirmation of the presence of ‘‘Lep-tospermum scoparium” honey, but also to differentiate ‘‘pure man-uka” from blends. Moreover, we demonstrate that other typicalbotanical origins from New Zealand can also be identified by thismethod. Unlike conventional analytical approaches, these parame-ters and more information about potential adulteration (Spiteriet al., 2015) can be obtained through a single, quick, low cost anal-ysis. Therefore 1H NMR profiling appears to be a very convenientanalytical tool for a comprehensive and efficient screening test tocontrol the integrity of New Zealand honey.

Acknowledgement

We are grateful to Elodie Dubin, Eurofins Analytics France, forthe confirmation of the compounds by LC-TOF.

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