Micronization of astaxanthin by the supercritical anti-

solvent process (SAS)

Joana Jorge da Costa

Dissertação para a obtenção do grau de Mestre em

Bioengenharia e Nanossistemas

Orientadores: Professor Dr. António Manuel de Figueiredo Palavra

Dra. Maria Beatriz Pinto Pereira Palma Nobre

Júri

Presidente: Professor Luís Joaquim Pina da Fonseca

Orientador: Professor Dr. António Manuel de Figueiredo Palavra

Vogal: Professor Dr. José Augusto Paixão Coelho

Dezembro 2014

II

Agradecimentos

Primeiramente, queria agradecer ao Instituto Superior técnico pelos serviços prestados e por ser uma

instituição de renome, onde tantos jovens depositam as suas esperanças.

Agradeço ao coordenador de Mestrado Professor Luís Fonseca pela sua disponibilidade e empenho

em tornar o Mestrado de Bioengenharia e Nanossistemas multidisciplinar mantendo a sua coerência.

Queria agradecer também pelas palavras de força que tem sempre para dar.

Gostaria de agradecer especialmente ao meu orientador Professor António Palavra por me ter

selecionado e acolhido no seu grupo de investigação. Trabalhar consigo tem sido uma experiência

exigente mas muito gratificante.

À Dra. Beatriz Nobre o mais sincero obrigado, por toda a dedicação, pelos ensinamentos, por tanta

ajuda e por todo o apoio e motivação. Foi um caminho atribulado mas a sua presença sempre me

transmitiu muita confiança para continuar.

Um grande obrigado à Professora Helena Marques e Dra. Ana Salgado do IMed, FFUL por me ter

deixado usufruir e ajudado com as análises no Mastersize, assim como ao Professor José Paulo

Farinha pela ajuda na utilização do Nanosight. Também tenho muito que agradecer à Dra. Rute

Domingos pela facilidade com que me deixou usar o seu equipamento laboratorial sempre que

necessitava especialmente o Coulter Multisizer, e à unidade de Bioenergia do LNEg onde me foram

possíveis as análises espectrofotométricas e de HPLC.

Agradeço todo o apoio da minha família, especialmente o da minha mãe que sempre me impulsionou

nos estudos e desvalorizou as perdas, para que as vitórias tivessem sempre mais sabor. À Romana,

por ter sido tão paciente, alegre e me ter ajudado tanto, e ao meu pai que sempre fez força para que

eu terminasse rapidamente o mestrado. Queria salientar também a grande importância e o quanto

grata estou por ter tantos amigos que me apoiaram incondicionalmente durante todo este percurso.

Muito Obrigado à Agna e à Adriana que me ouviram tantas vezes, à Mafalda e à Joana que foram

companheiras e lutaram a meu lado.

Agradeço também à FCT pelo financiamento do projecto PEst-OE/QUI/UI0100/2013.

III

Abstract

The Present Master Thesis dissertation aimed the micronization of synthetic astaxanthin by

supercritical antisolvent technique (SAS). The objectives were accomplished using CO2 as antisolvent

and THF as solvent. DOE was applied in a fractional factorial design at 4 factors, pressure (100 to 150

bar), concentration (0.5-3 mg/ml), temperature (40-60°C) and solution flow rate (0.5-1.5 ml/min) and

at 2 responses (yield of micronized product and mean particle size). Screening analysis showed higher

significance to pressure, concentration, and temperature. Two experiments were run in order to have

a better understanding of the temperature influence. It was shown that temperature influenced the

morphology of the micronized particles, and that increasing temperature it was possible to obtain

sphere-like and smaller particles. Central Composite Design was used for optimization of the process.

Factors for this analyze were pressure (100-150 bar) and concentration (1-3 mg/ml) and mean particle

size was the response. Temperature and flow rate were maintained respectively at 60° C and

1.5ml/min. Minimum mean particle size obtained was of 0.182m during the screening process at 100

bar, 60° C, 0.5ml/min and 3 mg/ml. Central composite design predicted that a similar response (0.171

m) could be obtained at flow rate of 1.5 ml/min but the mean particle size found in this conditions was

a slightly higher, 0.202 m.

Key words

Astaxanthin; Supercritical Antisolvent Micronization (SAS); Carbon Dioxide; DOE.

IV

Resumo

O trabalho apresentado nesta dissertação teve como objetivo a micronização de astaxantina pelo

processo de micronização por anti-solvente supercrítico (SAS). Os objetivos foram concretizados

utilizando CO2 como antisolvent e THF como solvente. DOE foi utilizado através de um fractional

factorial design (FFD) a 4 fatores, sendo estes a pressão (100 a 150 bar), concentração (0,5 a 3

mg/ml), temperatura (40 a 60° C) e a caudal da solução orgânica (0,5-1,5 ml/min), sendo considerados

para como parâmetros de resposta o rendimento de produto micronizado e o tamanho médio de

partícula. Na fase inicial de screening foi demonstrada maior importância para os fatores pressão,

concentração e temperatura. Foram realizadas duas experiências para que o factor temperatura

pudesse ser melhor entendido, Verificou-se que o tamanho médio de partícula diminuia com o

aumento de temperatura e que a morfologia das particulas mudava de “longas agulhas” para

“pequenas esferas” na temperatura mais alta. O processo de otimização foi estudado usando central

composite design onde a temperatura e caudal foram mantidos a respetivamente, 60°C e 1,5ml/min;

os fatores independentes foram a pressão (100 a 150 bar) e a concentração (1 a 3 mg/ml), e a

resposta a estudar, o tamanho médio de partícula de astaxantina micronizada. O menor valor obtido

experimentalmente para o tamanho médio de partícula foi de 0,182 m, obtido pelo FFD nas

condições experimentais de 100 bar 60° C, 0,5ml/min e 3mg/ml. A previsão pelo CCD das condições

experimentais (pressão e, concentração) para obter o menor tamanho médio de partícula, foram de

100 bar e 3mg/ml (a 60ºc e 1,5 mg/ml), com a estimativa de tamanho médio de partícula de 0,171 m,

A confirmação experimental do modelo de otimização pelo CCD mostrou que nas condições previstas

foi possível obter um tamanho médio de partícula de 0.202 m.

Palavras-chave

Astaxantina; Micronização por antisolvente supercritico; Dióxido de Carbono; DOE.

V

Index

I. Agradecimentos .......................................................................................... ii

II. Abstract ........................................................................................................ iii

III. Resumo ......................................................................................................... iv

IV. Index ................................................................................................................ v

V. Index of Figures .......................................................................................... vii

VI. Index of Tables............................................................................................. ix

VII. List of Abbreviations ................................................................................... x

VIII. 1. Introduction ............................................................................................... 1

1.1 Astaxanthin..................................................................... 1

1.1.1 Nutritional and Medicine Role of Astaxanthin .................................................................................... 1

1.1.2 Astaxanthin Extraction ................................................................................................................................. 2

1.2 Basic Principles on Supercritical Fluids .............. 2

1.3. Particle Design ............................................................ 4

1.4 Micronization.................................................................. 5

1.4.1 Rapid Expansion of Supercritical Solutions

(RESS) ........................................................................................................................................................................... 6

1.4.2 Particles from Gas-Saturated Solutions (PGSS) .............................................................................. 8

1.4.3 Supercritical Antisolvent Micronization Process

(SAS) .............................................................................................................................................................................. 9

1.4.4 SUPERCRITICAL ASSISTED ATOMIZATION

(SAA) ............................................................................................................................................................................ 13

1.5 Objectives of the Thesis .......................................... 14

IX. 2. Experimental Method ............................................................................ 15

2.1 Materials ........................................................................ 15

2.2 Solubility Studies ........................................................ 15

2.3 Supercritical Antisolvent Micronization Apparatus ............................................................................. 15

2.3.1 Experimental Procedure ........................................................................................................................... 17

2.3.2 Determination of the Particle Size and Particle

Size Distribution ....................................................................................................................................................... 17

2.3.3 Evaluation of the Purity of Micronized Astaxanthin ....................................................................... 18

2.4 Experimental Design ................................................ 19

VI

X. 3. Results ..................................................................................................... 21

3.1 Solubility ........................................................................ 22

3.2 DOE................................................................................. 23

3.3 Particle Analysis ......................................................... 31

3.4 Purity ............................................................................... 35

XI. 4. Discussion ............................................................................................... 38

XII. 5. Conclusion .............................................................................................. 42

XIII. References ................................................................................................... 43

VII

Index of Figures

FIGURE 1: REPRESENTATION OF THE CHEMICAL STRUCTURE OF AN ASTAXANTHIN MOLECULE. .......................... 1

FIGURE 2: PRESSURE-TEMPERATURE PHASE DIAGRAM FOR CO2 (MENDES ET AL., 2003) ...................................... 3

FIGURE 3: SCHEMATIC REPRESENTATION OF A RESS PROCESS; FONT HTTP://WWW.ARL.AOT.UNI-

ERLANGEN.DE/SC-FLUID- TECHNOLOGY/RESS.HTML ........................................................................................ 7

FIGURE 4: SCHEMATIC DIAGRAM OF A PGSS APPARATUS. SM- STATIC MIXER; ST- SPRAY TOWER. FONT:

HTTPS://AICHE.CONFEX.COM/AICHE/2006/TECHPROGRAM/P67632.HTM ..................................................... 8

FIGURE 5: SCHEMATIC REPRESENTATION OF A SAS MICRONIZATION APPARATUS. S1: CO2 SUPPLY; S2: LIQUID

SUPPLY; RB: REFRIGERATING BATH; P1, P2: PUMPS; TC: THERMOCOUPLE; M: MANOMETER; PV:

PRECIPITATION VESSEL; MV: MICROMETERING VALVE; LS: LIQUID SEPARATOR; BPV: BACK PRESSURE

VALVE; R: ROTAMETER; DM: DRY-TEST METER (DE MARCO AND REVERCHON 2011). ................................. 10

FIGURE 6: SCHEMATIC DIAGRAM OF THE NOZZLE IN THE SEDS APPARATUS. ........................................................ 12

FIGURE 7: SCHEMATIC REPRESENTATION OF THE EXPERIMENTAL APPARATUS OF SAA PROCESS. S1, INERT GAS

HEATER; S2, S3, AND S4, HEAT EXCHANGERS; P2 AND P3, PUMPS; SA, SATURATOR; PR, PRECIPITATOR; CO,

LIQUID CONDENSATOR; C, DRY TEST METER. (REVERCHON, 2002). ............................................................... 13

FIGURE 8 SCHEMATIC REPRESENTATION OF SAS APPARATUS. BP1 AND BP2, BACK PRESSURE REGULATORS; C,

CO2 COOLER; F, CALIBRATED FLOW-METER; G, CO2 CYLINDER; GM, DRYTEST METER; HE1 AND HE2, HEAT

EXCHANGERS; LS, LIQUID SOLVENT RECOVERY VESSEL; MV, MICROMETERING VALVE; P1 AND P2, HIGH-

PRESSURE PUMPS; PV, PRECIPITATION VESSEL; RD1, RD2 AND RD3, RUPTURE DISCS; S, LIQUID SOLUTION

SUPPLY; WB, THERMOSTATIC WATER BATH..................................................................................................... 16

FIGURE 9 PHOTO OF THE SAS MICRONIZATION APPARATUS AT IST ....................................................................... 17

TABLE 5 ANOVA FOR YIELD OF MICRONIZATION ANALYSIS FOR ADJUSTED AND UNADJUSTED MODELS. .......... 26

FIGURE 10: SEM IMAGES OF SAS AT (A) 40ºC, 100 BAR, 3MG/ML AND 0.15 ML/MIN AND EXPERIENCE (B)

60ºC, 100 BAR 3MG7ML AND 1.5 ML/MIN................................................. ERRO! MARCADOR NÃO DEFINIDO.

FIGURE 11: GRAPHICAL REPRESENTATION OF CREATED CCD MODEL AT 2 FACTORS (PRESSURE AND

CONCENTRATION) AND 1 RESPONSE (MEAN PARTICLE SIZE). THE LABELED POINT (0.17) IS AN

ESTIMATION OF THE MINIMUM RESPONSE THAT CAN BE OBTAINED FROM THE GIVEN MODEL. ....... ERRO!

MARCADOR NÃO DEFINIDO.

FIGURE 12: SEM IMAGE OF UNPROCESSED ASTAXANTHIN. .................................................................................... 31

FIGURE 13: SEM IMAGES AT 100 BAR, 60ºC, 1.5 ML/MIN ORGANIC SOLUTION FLOW RATE AND AT

ASTAXANTHIN CONCENTRATION IN ORGANIC SOLUTION OF A- 0.1575 MG/ML(FFD RUN 6), B-1MG/ML (CCD

RUN1), C- 2MG/ML (CCD RUN3) AND D-3 MG/ML (CCD

RUN7)……………………………………………………………………………………………32

FIGURE14: SEM IMAGES AT 3MG/ML (CCD RUN 2), 60ºC AND 1.5 ML/MIN (CCD RUN 11) AND PRESSURES OF

125 AND 150 BAR RESPECTIVELY. .............................................................................................................................. 33

FIGURE 15 – HPLC CHROMATOGRAMS OF A-ASTAXANTHIN STANDARD, B- SOLUTION FROM SEPARATION

VESSEL (100 BAR, 60ºc, 1.5 MG/MIN AND 3 MG/ML), C- MICRONIZED ASTAXANTHIN (100 BAR, 60ºc, 1.5

MG/MIN AND 3 MG/ML). .................................................................................................................................. 36

Figure 16: Mole fraction of astaxanthin in pure SC CO2 (Fuente, Oyarz´un, Quezada, & Valle, 2006) (Youn, Roh,

Weber, Wilkinson, & Chun, 2007) and SC CO2+THF (0.05 mole fraction), at 60ºC…………………………………………..37

VIII

FIGURE17: PARTICLE SIZE DISTRIBUTION OF FFD RUN8 AND CCD RUN7............................................................... 38

FIGURE 18: SEM IMAGES OF A SAMPLE OF MICRONIZED ASTAXANTHIN AT SOLUTION FLOW RATE OF

0.15ML/MIN (RUN 8 FFD) AT THE LEFT, AND FLOW RATE OF 1.5ML/MIN (RUN 7 CCD) AT THE RIGHT. ERRO!

MARCADOR NÃO DEFINIDO. FIGURE 18: GRAPHICAL REPRESENTATIO6N OF MEAN PARTICLE SIZE

EVOLUTION WITH TEMPERATURE

CHANGE………………………………………………………………………………………………………………………………………………….39

Figure 19: Graphical representatio6n of mean particle size evolution with temperature change………………….39

FIGURE 20: MEAN PARTICLE SIZE OF SAS MICRONIZED ASTAXANTHIN AS A FUNCTION OF PRESSURE, AT 60ºC, 3

MG/ML AND 1.5 ML/MIN ORGANIC SOLUTION FLOW RATE. .......................................................................... 40

FIGURE 21: MEAN PARTICLE SIZE OF SAS MICRONIZED ASTAXANTHIN AS A FUNCTION OF ORGANIC SOLUTION

CONCENTRATION, AT 60ºC, 100 BAR AND 1.5 ML/MIN ORGANIC SOLUTION FLOW RATE. .......................... 41

IX

Index of Tables

TABLE 1 FRACTIONAL FACTORIAL DESIGN (FFD) FOR THE SAS MICRONIZATION OF ASTAXANTHIN…….. .......... 25

TABLE 2: HIGHEST SOLUBILITY OF ASTAXANTHIN IN EACH ORGANIC SOLVENT. CONCENTRATIONS DETERMINED

BY SPECTROPHOTOMETRY…………………………………………………………………………………………………………. 27

TABLE 3: MATRIX FOR FFD AT 4 FACTORS ( PRESSURE, TEMPERATURE, ASTAXANTHIN CONCENTRATION OF

ORGANIC SOLUTION AND SOLUTION FLOW RATE) AND 2 RESPONSES ( YIELD OF MICRONIZATION AND

MEAN PARTICLE SIZE) CREATED BY DESIGN- EXPERT 9.0.3. STD. DEV. VALUES STANDS FOR MEAN

PARTICLE SIZE ANALYSIS………………………………………………………………………………………………………………28

TABLE 4: ANOVA RESULTS FOR MEAN PARTICLE SIZE ANALYSIS………………………………………………………………………. 29

TABLE 5 ANOVA FOR YIELD OF MICRONIZATION ANALYSIS FOR ADJUSTED AND UNADJUSTED MODELS……. ... 31

TABLE 6: CCD MATRIX OBTAINED BY DESIGN- EXPERT 9.0.3 AT 2 FACTORS ( PRESSURE AND ASTAXANTHIN

CONCENTRATION) AND ONE RESPONSE ( MEAN PARTICLE SIZE). STD. DEV. STANDS FOR THE MEAN

PARTICLE SIZE DETERMINATION……………………………………………………………………………………………………………… 32

TABLE 7: ANOVA RESULTS FOR CHOOSEN MODEL IN CCD CALCULATED BY DESIGN-EXPERT 9.0.3…………….. .... 33

TABLE 8: RESUME OF ANOVA RESULTS WITH PREDICTED AND ADJUSTED VALUES……………………… ................. 34

TABLE 9: COEFFICIENTS TABLE FOR THE MODEL ADJUSTED AFTER RECTIFICATION OF SOLUTION 1 IN DESIGN -

EXPERT 9.0.3……………………………………………………………………………………………………………………………..35

TABLE 10: SEM IMAGES AT 100 BAR, 60ºC, 1.5 ML/MIN ORGANIC SOLUTION FLOW RATE AND AT

ASTAXANTHIN CONCENTRATION IN ORGANIC SOLUTION OF 0.1575, 1, 2 AND 3 MG/ML………………………. 36

TABLE 11: SEM IMAGES AT 3MG/ML, 60ºC AND 1.5 ML/MIN AND PRESSURES OF 125 AND 150 BAR…………….. 38

TABLE 12: PARTICLE SIZE RESULTS OBTAINED BY SEM IMAGES ANALYSIS (MEAN FERET AND RANGE FERET),

NANOSIGHT, AND PARTICLE SIZE RANGE BY MASTERSIZE…………………………………………………………………39

X

List of Abbreviations

CCD Central Composite Design

CP Critical Point

DCM Dicloromethane

DMSO Dimethyl-Sulfoxyde

DOE Design of Experiments

FFD Fractional Factorial Design

MPS Mean Particle Size

Pc Critical Pressure

SAS Supercritical Anti-Solvent

SFE Supercritical Fluid Extraction

Tc Critical Temperature

THF Tetrahydrofuran

TP Triple Point

1

1. Introduction

1.1 Astaxanthin

Astaxanthin is a red carotenoid from the carotenoid family. Carotenoids are usually synthetized by

microorganisms and plants, but due food chain are transported to almost all the animal kingdom.

The most common source of Astaxanthin is the microalgae Haematoccus pluvialis, which can

synthetize large amounts of this carotenoid in order to protect itself from ultraviolet radiation and in

response to nutrient and environmental stresses.

Astaxanthin started to be applied in coloring salmonid fish in the feed industry, but is currently used in

health and well-being markets, due to its anti-oxidant properties, as well as in cosmetics (Guedes et

al., 2011).

Its metabolic pathways and physiological functions are shared by other carotenoids like β- carotene,

zeaxanthin and lutein. Chemical structure of astaxanthin is represented in Figure 1. Hydroxyl groups

on each ionone ring give special feature to this compound, such as the anti- oxidant activity, ability to

be esterified and a more polar configuration when compared to other carotenoids. As a free molecule,

astaxanthin is very sensitive to oxidation. Therefore, it naturally occurs in nature conjugated with

proteins or esterified with one or two fatty acids, which stabilizes the molecule.

Figure 1: Representation of the chemical structure of an astaxanthin molecule.

1.1.1 Nutritional and Medicine Role of Astaxanthin

Astaxanthin studies have shown that this compound is not only a super antioxidant, but it possesses

anti-inflammatory properties. This carotenoid appears to have potential benefits for acid reflux and

macular degeneration, provides vascular benefits, and less oxidative stress and inflammation.

Moreover, astaxanthin enhances and strengthens the immune system and decreases DNA damage

(Anarjan and Tan, 2013, Guerin et al., 2003, Hussein et al., 2006, Vílchez et al., 2011).

2

In human body, free radicals (e.g. hydroxyl and peroxyl radicals) and highly reactive forms of oxygen

(e.g. singlet oxygen), which are produced during normal metabolic pathways, exposure ultraviolet light

and chemicals, phagocytes activity , among others, can damage DNA and are correlated to aging

process, atherogenesis, ischemia-reperfusion accelerated macular degeneration and carcinogenesis

(Papas, 1999) . Moreover, free radicals may also be produced by the chain reaction of degradation of

poly-unsaturated fatty acids, which can accelerate the degradation of lipid membranes (Naguib, 2000

and Guerin et al., 2003). Carotenoids can also absorb the excited energy of singlet oxygen onto the

carotenoid chain, leading to the degradation of the carotenoid molecule but preventing other molecules

or tissues from being damaged (Beutner et al., 2001).

Fasset and Coombes, (2011) reported that the regular ingestion of astaxanthin may improve oxidative

response and prevent tissue damage. Also, astaxanthin proved to be a very good protective agent to

membranous phospholipids and other lipids against peroxidation (Naguib, 2000 and Guerin et al.,

2003).

1.1.2 Astaxanthin Extraction

Astaxanthin can be obtained from microalgae and yeast (Nobre et al., 2006, Passos et al., 2006)

Conventional techniques like organic solvent extraction presented drawbacks, such as degradation of

this thermolabile compound and the presence of toxic solvent traces in the extract. Supercritical fluid

extraction (SFE) techniques overcome those limitations, since this separation technique can operate

at moderate temperatures and it is possible to obtain a solvent free solute. Additionally, with SFE

technology, high selectivity can be achieve, since extracts of different compounds (oil and pigments)

can be obtained through modification on solvent density (e.g, pressure and temperature) or the

addition of a co-solvent (Nobre et al., 2012).

Supercritical fluid extraction of astaxanthin from microalga Haematococcus pluvialis allowed a total

carotenoid recovery of 92% at the pressure of 300 bar and the temperature of 60 ºC, using ethanol

(10%) as a co-solvent (Nobre et al., 2006).

1.2 Basic Principles on Supercritical Fluids

Accurate PVT measurements on CO2 carried out by several workers, reported in the IUPAC

international tables for this compound (Angus et al., 1976), allows the construction of the pressure-

temperature phase diagram shown in Figure 2.

3

Figure 2: Pressure-Temperature phase diagram for CO2 (Mendes et al., 2003)

In this diagram, the vapour-/liquid equilibrium curve starts at the triple point (TP), where the tree phases,

solid, liquid and vapour are in equilibrium. This curve ends at the critical point (CP), where the

meniscus separating the liquid and the vapour phases disappears and only a single phase occurs.

The corresponding values of temperature, Tc, and pressures, Pc, are designated by critical

temperature and pressure, respectively. Supercritical region corresponds to the zone of the diagram

for which T>Tc and P>Pc.

In the supercritical fluid region, near the CP, the density of the fluid phase is particularly sensitive to

small changes in pressure and temperature (Bruno et al., 1993). The solubility of a solute in a solvent

is strongly influenced by the heteromolecular interaction and, therefore, by the density of the solvent.

Then, it is expected that a supercritical fluid to be a bad solvent in states with low or gas-like densities

and a good solvent at high or liquid-like densities.

The solvent power of the supercritical fluids can also be influenced by its transport properties, namely

by the viscosity and mass diffusivity. Since the density of the supercritical fluid can be similar to that

of the liquid, its solvent power can also be almost the same and be adjusted by the control of pressure

and temperature. Besides, as the values of the diffusivity and the viscosity are between those of the

gases and liquids, the supercritical fluid is capable of a faster and deeper penetration into a vegetable

matrix containing the substance to be extracted than a liquid.

Since the supercritical fluids have a special capability to dissolve solutes, which was discovered in

1879 by Hannay and Hogarth (1879), a new separation technique called Supercritical Fluidic

Extraction (SFE), is now used in several areas of Science and Technology.

Supercritical CO2 has been the most used supercritical fluid, because is non-flammable, non- toxic,

inexpensive and relatively inert from a chemical point of view. On the other hand, due to its moderate

critical temperature, the extraction of thermolabile compounds can occur without thermal degradation.

4

Moreover, the addition of a small amount of a substance, which has a volatility intermediate between

that of the compound to be extracted and that of the supercritical fluid, called co-solvent or entrainer,

can increase the solvent power of the CO2. Due to its non-polar behavior, CO2 is a bad solvent for

polar compounds. However, with the addition of a polar entrainer, such as water, ethanol, and

methanol, polar compounds can also be extracted (Hong and Johanson, 1986).

1.3. Particle Design

Particle design is an area of most importance, since it can be determinant in the efficiency of

absorption, solubility and diffusion of a solid compound. Particles can be obtained using traditional

micronization techniques. However, due to their advantages, new techniques using compressed gases

or supercritical fluids have received special attention in the recent years.

Particle design can be very relevant in the manufacturing of advanced ceramic materials, explosives,

pharmaceuticals, polymers, dyes, among others (Reverchon and Della Porta, 2003).

In the pharmaceutical industry smaller particle sizes can increase the efficiency of drug uptake by cells,

which can mean the need of lower doses and the consequent reduce cost of the medicine. The size

of solid particles of an active pharmaceutical ingredient used in a pharmaceutical formulation can have

a great impact in properties like solubility, dissolution rate, dosage levels and bioavailability.

For instance, reducing particle size for micro and nano scale can improves solubility of compounds

that usually are not soluble in aqueous or other organic solutions. Also, smaller particles will increase

the bioavailability of the compound, which makes it possible to provide dosages well below the toxicity

threshold (Atkinson et al., 1962). Moreover, in ophthalmic formulations the use of smaller particle size

crystals is mandatory in order to avoid macula injury.

Endocytosis is the process that normally cells internalize particles of the medium, fragments until 10

µm can be processed when fagocitosis occurs. There are many pathways that particles can follow until

achieve its target component, which is highly dependent on the particle size. For instance, the

penetration of polysterene spheres in HeLa cells via cholesterol independent, non-clathrin and non-

caveolae-mediated pathway is only possible for particles with particle size between 25 and 42 nm

(Shekunov et al., 2006). On the other hand, the rate of internalization of PLGA particles on

gastrointestinal epithelium is significantly higher for particles with size of 100 nm compared to particles

with 500nm of size. (Shekunov et al., 2006). Moreover, it was verified that the interactions between

breast cancer cells and gold functionalized nanoparticles are more effective when using particles with

diameters of 40 and 50 nm, which improves considerably the therapeutic efficacy (Wang et al., 2011).

Particle shape and surface chemistry are other characteristics of particles that have to be taken

account regarding cells uptake (Shariati and Peters, 2003). Doshi et al. (2010) as well as Decuzzi and

Ferrari (2008) verified the importance of the contact angles and shape of the particles are important

5

parameters in internalization of particles into cells. Moreover, Chan et al. (2008) proved that surface

chemistry also influence the cells uptake. Other studies demonstrated that if contact angles are over

that 45° cells will adhere to particles, but will not internalize them, and also that rod like shape is

advantageous to absorption. Rigidity or softness of particles, may also determine its behavior among

cells. Allen et al. (1981) showed that rigid liposomes can decrease complement activation and

subsequently reduce macrophage uptake.

Other important feature is the charge of particles surface, which may determine their interactions with

cell plasma membrane (Kalani and Yunus, 2011). Cell membrane is negatively charged and thus

electrostatically it’s possible to promote cell-particle interaction if the particle has a positively charged

surface. Particle surface chemistry is correlated with the construction of target ligands at particle

surface, which can improve internalization. Ligands may be constructed like RGD peptides, aptamers

that can bind specifically to any target, among others. Recently, Davis et al. (2009) successfully

targeted siRNA with transferrin- conjugated cyclodextrin polymer-based nanoparticles.

Particle size can also play an important role in colorants, nutrients and nutraceutical for food, cosmetic

and pharmaceutical products (Costa, 2009). For instance, the final colour of the pigmented systems is

a function of the physical properties of the pigment (including the particle size distribution, morphology,

and crystal configuration) and is dependent on the way the pigment is made. Besides color strength,

other properties such, as transparency, opacity, and flow are known to be a function of the pigment

particle size (Suo et al., 2005 and Yang et al., 2012). Furthermore, the solubility of the active ingredient

can increase with the decrease of the particle size, which allows obtaining more dispersed and stable

solutions. Finally, the bioavailability of these compounds will increases for lower particle sizes and may

depend on the solid state of the compound (amorphous or crystalline) (Costa, 2009). Carotenoids

have singular importance in food, cosmetic and pharmaceutical industry as colorants, nutrients and

anti-oxidants. Particularly, B-carotene, which is the one of the most used carotenoids due to its high

pro-vitamin A activity. The application of this carotenoid requires proper formulation to avoid its

degradation and increase its solubility in aqueous solution. The development of water soluble β-

carotene formulations were achieved by encapsulation in OSA-starch after obtained a size particle

capable of encapsulation. Santos et al. (2012) verified the existence of a high and significant inversely

proportional correlation between particle size and encapsulation efficiency.

1.4 Micronization

Micronization is the general term used to describe numerous processes that aim to reduce the average

diameter of solid material particles. Usually, micronization is referred to the creation of particles with

diameters in the order of 10 µm. However, due the development of modern techniques, as well as the

demand of pharmaceutical industry, it is now also used to describe the formation of particles with nano-

sized diameters.

6

The most common micronization techniques are spray drying, mechanical comminution, solute

recrystallization, freeze drying, and interfacial polymerization. For instance, spray drying was

successfully used to obtain microparticles of chitosan for drug delivery (He et al., 1999). Nevertheless,

these techniques presented significant disadvantages, such as excessive use of solvent, thermal and

chemical solute degradation, high residual solvent concentration, and difficulty in controlling the

particle size, particles size distribution, as well as the changing the crystal structure of the precipitated

powder.

To overcome those disadvantages micronization techniques relying on supercritical fluids technology

were developed. Among the several supercritical micronization techniques particularly important are

the following: Rapid Expansion of Supercritical Solutions (RESS), Particles from Gas-Saturated

Solutions (PGSS), Supercritical Anti-Solvent process (SAS) and Supercritical Assisted Micronization

(SAA).

1.4.1 Rapid Expansion of Supercritical Solutions (RESS)

This technique is based in the change of the solvent power of the supercritical fluids with pressure,

which allows the formation of the precipitated solute when the solution is expanded to ambient

conditions of pressure and temperature (Smith 1983). This technique was patented in by Smith (1983),

being one of the objects of their invention the formation of a fine powder with a narrow size distribution

and controllable physical and chemical properties.

Figure 3 shows the procedure and the variation occurring in the different chambers until the solute and

solvent separation.

7

Figure 3: Schematic representation of a RESS process; font http://www.arl.aot.uni-erlangen.de/sc-fluid- technology/ress.html

In this figure supercritical fluid is added to the extraction vessel, where it is supersaturated with

substrate(s). Afterwards, the solution leaves this vessel and passes through a heated nozzle, where it

will be depressurized to an expansion vessel at low pressure. In this chamber occurs an extremely

rapid nucleation and very small particles of the substrate(s) are formed, while are separated from the

gaseous stream, due to the decrease of solubility caused by pressure drop.

There are a few limitations for this technique, being the most important the fact that the substrate

should be highly soluble in the supercritical fluid, which is even more difficult in the case of co-

precipitation, as well as encapsulation, since the solute and carrier have to be also highly soluble in

SC CO2. To overcome the solubility limitation it is possible the use of alternative organic solvents

[Pestov et al. (2003)], or a co-solvent for the polymer to improve the solubility in the supercritical fluid

[Mattea et al. (2009)], being this technique called RESS-N (non- solvent RESS).

Recently, Iman and Ferido (2012) presented a study about the influence of particles size on material

viscosity. Their conclusions showed high influence not only in temperature and pressure regulations,

but also in the nozzle diameter. These authors used RESS process at 200bar 55ºC and with 500 µm

nozzle and 1cm spraying distance in the presence of deionized water as co-solvent, to obtain the finest

particle characteristics. It was shown that particle size decreased with pressure and temperature and

increased with the nozzle diameter and spraying distance. Oscar and Charlotta (2011) monitored the

effect of the nozzle distance in the RESS spray coating process. The results showed that large

particles carried by liquid CO2 droplets aggregates on the surfaces, and that the nozzle distance

8

influenced the deposition. When the distance was short there was deposition since CO2 was still in

liquid phase and could transport efficiently crystal to the surface, but at longer distances small particles

are formed and carried aerodynamically and are not adsorbed (Du et al., 2011).

1.4.2 Particles from Gas-Saturated Solutions (PGSS)

The operation principle of PGSS is very similar to RESS. Although there are few organic compounds

soluble in CO2, a considerable number of polymers can be solubilized in supercritical carbon dioxide

and therefore processed by PGSS.

Figure 4 represents a schematic diagram of a PGSS apparatus.

Figure 4: Schematic diagram of a PGSS apparatus. SM- static mixer; ST- spray tower. Font: https://aiche.confex.com/aiche/2006/techprogram/P67632.HTM

PGSS starts with the pumping of supercritical fluid into the static mixer (SM) in order to be dissolved

or suspended in the substrate, which is already inside of the high pressure vessel. Saturated solution

is led to a nozzle where there is rapid expansion. During the expansion, carbon dioxide is suddenly

vaporized and intensely cooled down by Joule- Thomson effect, thus providing the driving force for the

solidification of the solute and formation of the precipitated particles in the spray tower (ST).

De Paz et al. (2012) investigated the formulation of β-carotene with poly-(ε-caprolactones) by PGSS

process. Regarding particle characterization, increasing molar ratio increased particle size. Optimal

temperature and pressure for higher β-carotene content were, respectively, 70°C and 15MPa.

Differential scanning calorimetric assays indicated that no variations in the crystalline structure of the

polymer had occurred as a consequence of the PGSS processing. On the other hand, the same

analyses did not show any peaks corresponding to crystalline β- carotene and the melting peak was

9

also not observed in physical mixtures of crystalline β- carotene and polymer. These results indicated

that β-carotene is solubilized in the polymer melt.

1.4.3 Supercritical Antisolvent Micronization Process (SAS)

Supercritical fluid Anti-solvent processe (SAS) was proposed as alternative to liquid anti-solvent ones

(Gallagher et al., 1989). The SAS process works similarly, but instead of the use of a liquid solvent, in

which the compound to be micronized is insoluble, it is used a supercritical fluid. The supercritical

anti-solvent, with its high diffusivity, produces a fast supersaturation, leading to much lower particle

sizes than those obtained by liquid anti- solvents or other processes (E. Reverchon, 1999). The

combination of the high solvent power of supercritical fluids to dissolve the organic solvent and the low

solubility of the compounds in the supercritical fluids makes SAS the most suitable technique for the

precipitation of several organic compounds, like carotenoids and active pharmaceuticals ingredients.

Moreover, it is possible to recover the supercritical anti-solvent by simple decompression, avoiding

complex treatments typical of the liquid process.

SAS has been successfully used for the micronization of pharmaceuticals, and all type of compounds

like PLGA, Chitosan microspheres, lysozyme (Reverchon et al., 2002; Reverchon et al., 20012-13,

Costa et al., 2007, Pyo and Lim, 2008, Cocero et al., 2008, Cocero et al., 2009, Mezzomo et al., 2012,

Park et al., 2013, Chang et al., 1998, Du et al., 2011, He et al., 1988)

In the SAS process (Figure 5), the supercritical fluid acts as anti-solvent, and the substrate is dissolved

into a liquid solvent (solution). The supercritical anti-solvent is continuously fed to the precipitation

vessel (PV), as well as the liquid solution, which is sprayed through a restrictor into the PV. The rapid

contact between the two media causes the precipitation of the solute. After precipitation the fluid phase

is expanded through a micrometric valve (MV), and the liquid solvent is recovered in the low-pressure

liquid solvent recovery vessel (LS). Furthermore, the antisolvent is expanded to atmospheric pressure.

10

Figure 5: Schematic representation of a SAS micronization apparatus. S1: CO2 supply; S2: liquid supply; RB: refrigerating bath; P1, P2: pumps; TC: thermocouple; M: manometer; PV: precipitation vessel; MV: micrometering valve; LS: liquid separator; BPV: back pressure valve; R: rotameter; DM: dry-test meter

(De Marco and Reverchon 2011).

During the supercritical antisolvent process the surface area will be increased, which leads to an

improvement in bioavailability. This fact is of great importance in drug delivery since narrower particle

size distribution means a better flexibility of administration. Moreover increasing the bioavailability the

required drug dosage decreases and raises the control over a sustained period (Acosta, 2009).

Recently, this technique was improved by the use of applications (Snavely et al., 2002). The vibration

of the ultrasonic nozzles (120 KHz) reduced the size of particles formed to nanoscale.

Cardoso et al. (2008) studied the SAS micronization of minocycline. The experiments were carried out

to analyse the effects of temperature, pressure and concentration of liquid solution in the main particle

size and particle size distribution of the product formed. Results showed that micronization takes place

in a homogeneous supercritical phase formed in the precipitator and particle formation results from

gas-phase nucleation as predicted. Particles with a mean particle diameter of 139nm and a particle

size distribution from 1 to 300 nm were obtained at 130 bar, 50ᴼC, solution concentration and flow-

rate of 10 mg/ml and 1ml/min, respectively, showing that this compound was successfully micronized

by this technique. Moreover, the solubility results of minocycline were modelled through to computer

fluid dynamic (CFD) studies. The obtained model allowed the identification of problems regarding the

suitableness of the precipitation vessel geometry to the binary system used (CO2 + ethanol) and the

buoyance effect in the SAS micronization apparatus performance. Those represent an improvement

of the previously CFD studies that had been performed by Werling and Debenedetti (2000), Lengsfeld

et al. (2000) and Martin and Cocero (2000).

11

Pharmaceutical compound micronization has been much explored recently. Ehsan Adeli (2014) in his

work with Azithromycin, a poorly water-soluble drug with a lower dissolution rate, concluded that SAS-

SCF technique for preparation of ternary solid dispersion systems highly increased the solubility and

dissolution rate of the compound. Evaluation of the crystallinity form of the drug showed no significant

changes. Overall, it was showed that SAS would have a great impact in processing this antibiotic,

since it lead to a huge improve of the oral administration of this drug.

Beta-Carotene, lycopene and lutein are carotenoids with important industrial interest and a wide

range of applications in food, pharmaceutical and cosmetic. The SAS process can be a suitable

technique for the micronization of these compounds since its solubility in supercritical CO2 is very low

(Mendes, Nobre, Coelho, & Palavra, 1999).

The SAS micronization of beta-carotene was successfully carried out by Cardoso et al. (2008). These

researchers obtained red crystalline particles with mean size of 100 microns and a particle size

distribution of 1-500 µm, using tetrahydrofuran as solvent, pressures between 65 and 150 bar and

temperatures in the range of 35 to 60ºC. Moreover, no degradation of the compound was observed,

which can foresee the industrial application SAS for this carotenoids. In order to increase trans-β-

carotene bioavailability and increase its dispersion in oil emulsion formulations Supercritical antisolvent

(SAS) micronization were performed. (Cardoso et al., 2008). Experiments were performed using

tetrahydrofuran (THF) as solvent and supercritical carbon dioxide (CO2) as antisolvent. Evaluation of

temperature from a range of 308.15-333.15 K, pressure from 6.5-13Mpa, and concentration of the

liquid solution (6-9 gL-1) was also analyzed. Particles obtained were in a size range from 1 to 500 μm

with mean particle diameters around 100 μm. Precipitated powder presented crystalline with superficial

pores and leaf-like appearance, crystalline with regular shapes and blade-like edges, and crystalline

without superficial pores and leaf-like appearance. The biological compound had no degradation

during the process which makes SAS micronization a suitable technique for industrial applications of

β-carotene.

SAS techniques were also studied to micronized lycopene (Cocero et al., 2006) and Lutein (Cocero et

al., 2008). Prismatic or needle-like particles were obtained in the experiments in both biological

compounds. In lycopene case mean diameter of the particles varies from about 10 to 80µm with the

different operating parameters. Supersaturation was the parameter of strongest influence on particle

size. In the case of lutein experiments the decrease in particle size was more noticeable in the pressure

interval 6.5–8MPa. The co-precipitation of lutein with poly-lactic acid (PLA) has also been studied. A

reduction in the particle size with the polymer/drug ratio has been observed, down to a minimum

diameter of 1–5 µm. In a review of Cocero group (2008) it’s highlighted the importance of parameters

such as the droplet size at the mixers’ exit and the mixer design, pressure and temperature regulation,

adequate solvent choice. Regulating those parameters favourably the precipitation of carotenoids by

means of supercritical fluid technologies is feasible, and with the SAS process in particular is possible

12

to obtain particles with mean sizes between 1 µm and 200 µm. In addition, the SAS process has the

flexibility to control the size and even the morphology of the particles by adequate modulation of

temperature and pressure.

Solution enhanced Dispersion by supercritical fluids (SEDS) is a recent development of SAS. In this

technique a nozzle with two coaxial passages allows introducing supercritical CO2 and a solution of

active substances into the precipitation vessel. Figure 6 demonstrate a schematic representation of

SEDS apparatus, with relevance to the characteristics of the nozzle.

Figure 6: Schematic diagram of the nozzle in the SEDS apparatus.

The solution to be atomized is driven along a coaxial annular passage and formed to a thin swirl film

by the liquid distributor with spiral slots, inclined at an angle of 45ᴼ relative to the central axis of the

atomizer. The SC-CO2 is driven through an inner capillary to form a jet stream with high velocity. At

the exit of the atomizer, the atomizing SC-CO2 stream impinges on the swirling film at 45ᴼ and interacts

with it to generate shear force on it providing a better mixing. Thus finer droplets are formed, large

nucleation rates and smaller needle-like particles are obtained. Experiments, using this technique for

the micronization of the carotenoid bixin, were performed by Suo et al. (2005). The obtained results

showed that higher temperatures tend to join particles together leading to net-like structures.

Additionally,, the increase in solution concentration as well as in temperature increased the contact

among microparticles resulting in agglomeration. Astaxanthin was also micronized by SEDS (Hong et

al., 2008). SEM micrographs showed changes of morphologies and sizes according to process

parameters, and new crystalline planes in the astaxanthin microparticles.

Recently, a successful experiment demonstrating the advantages of SAS micronization was performed

by Montes et al. (2013). The aim of their work was to obtain poly(L-lactic acid) (PLLA) particles small

and spherical as possible in order to be used as coating agents in drug delivery systems. For that

achievement, RESS and SAS micronization techniques were carried out. RESS results were not

favorable, since PLLA have low solubility in CO2, and even with co- solvents use, problems, such as

13

agglomeration and low production capacity appeared. SAS experiments presented favorable results

with particle sizes in the micrometer range (1–5 µm) and narrow particle size distributions. Additionally,

results showed that pressure and concentration had an inverse proportionality with the particle size

and distribution. On the other hand, temperature and flow rate were directly proportional to particle

size and particle size distribution. The glass transition temperature was reduced together with the

degree of crystallinity, increasing mobility of polymer chains and more rapid crystallization. Those

variations resulted in more plasticizes PLLA, which may possible provide a greater flexibility and

increased durability.

1.4.4 SUPERCRITICAL ASSISTED ATOMIZATION (SAA)

As referred is SAS micronization many biopharmaceutical compounds, like proteins or peptides, have

very low solubility in supercritical CO2. Proteins have been widely study for pharmaceutical use, but

the large size of drug particles made it a constraint to parenteral delivery. Reverchon (2002) proposes

a supercritical assisted atomization (SAA) to produce micro and nanoparticles with controlled size and

distribution. The experiments made with this technique showed good performance with different

solutes and solvents which turn to be reliable to a variety of applications like ceramics superconductors

and pharmaceutical as well (Adami et al., 2009, Adami et al., 2011, Reverchon et al., 2011).

Figure 7: Schematic representation of the experimental apparatus of SAA process. S1, inert gas heater; S2, S3, and S4, heat exchangers; P2 and P3, pumps; Sa, saturator; Pr, precipitator; Co, liquid

condensator; C, dry test meter. (Reverchon, 2002).

14

This technique was patented by Reverchon (2003) and its development complement RESS process,

since it allows the micronization of almost any solute using a supercritical fluid based technique.

A schematic representation of this technique is presented in Figure 7. CO2 and the liquid solution

containing the solute are pumped (P2 and P3) into the saturator (Sa), a high pressure chamber the

solid-liquid-gas streams are mixed together, obtaining a saturated solution, which then passes through

a conical pump where it is expanded as a spray into the precipitation vessel (Pr) at nearly atmospheric

conditions forming little droplets. The precipitator is also feed with nitrogen, which will lead to the

solvent evaporation. Convection current is created along the precipitator carrying all components

through a helicoidal tube, which at the end will have the precipitated particles. Gases will pass along

and will be refrigerated in a cooled condenser causing the liquid solvents condensations (CO2+solvent).

Recent studies using SAA showed a good optimization of the process in drug delivery systems (Liparoti

et al., 2013). Reverchon et al. (2002) used SAA to increase the solubility of a compound in a solvent

using a coprecipitate. The authors studied the micronization of a drug poorly soluble in water with a

polymer which functions which works as a drug carrier. Their results demonstrated effective

micronization of the drug-coprecipitate particles and an establishment of the amount of coprecipitate

was defined to control the dissolution rate of the drug. Further solubility tests reveal great water

dissolution.

Moreover, lysozyme was micronized by SAA (Du et al., 2011), and it was shown that SAA did not

influence the secondary structure an optimized grain to aerosol drug delivery was obtained. Moreover,

these results showed that the bio-activity of micronized proteins remains higher, >85% and the protocol

may be also applied to other protein compounds.

1.5 Objectives of the Thesis

Astaxanthin is a carotenoid with an increasing interest for the pharmaceutical and food industries. It

has been shown to possess important biological properties, is considered a powerful anti-oxidant and

is considered that it can reduce the risk of several acute diseases.

The main objective of this thesis is to micronize astaxanthin with supercritical CO2. The SAS was the

micronization technique chosen, since its solubility in supercritical CO2 is very low. To the best of our

knowledge there are no published studies for the micronization of astaxanthin by the SAS process.

The effect of the experimental parameters (pressure, temperature, organic solution concentration and

flow rate ratio of CO2 organic solvent) in the mean particle size, particle size distribution and yield of

the process will be studied. Moreover, statistical design will be evaluated to optimize the working

conditions in order to obtain the minimal particle size.

15

2. Experimental Method

2.1 Materials

Astaxanthin was obtained from Dr. Ehrenstorfer GmbH (98,6%). Tetrahydrofuran (p.a grade) was

purchase from Sigma-Aldrich and CO2 was provided from air liquid (99.998%).

2.2 Solubility Studies

Several organic solvents are suitable for carotenoids (Delia B. Rodriguez-Amaya, Ph.D;, 2001).

However, solubility data of astaxanthin in organic solvent are scarce. In order to select the organic

solvent for the astaxanthin SAS micronization experiments, the solubility of this carotenoid was

determined in various organic solvents, since the amount of solvent to be used in the process can be

relevant in the production cost for the industrial process. Also, for products that are purposed for human

consumption, e.g., for food, pharmaceutical, cosmetic industries, international regulation regarding the

safety of consumers and toxicity of the solvents is mandatory, and so GRAS classification was also

taking into account in the choice of the solvent for this work. (Administration, Center for Drug Evaluation

and Research (CDER), & Center for Biologics Evaluation and Research (CBER).

Solubility tests were carried in ethanol, acetone, ethyl acetate, DMSO, tetrahydrophuran and

dichloromethane.

Experimental procedure consisted in dissolving 10mg of astaxanthin in a volume of 1 ml of organic

solvent. Subsequent additions of 1ml were made until it was observed that no more solid could be

dissolve. The suspension was then stirred for 30 minutes, to ensure that saturation of the solution was

reached. Afterwards, samples of 2ml were taken from the solution, filtered (0.2 m) and analyzed by

spectrophotometry (Hitachi-2000) to determine the concentration of the dissolved fraction.

Absorption spectra were run between 380 and 700 nm and the concentration of astaxanthin in the

solvent was determined using the Beer-Lambert law, considering the maximum absorbance of the

solution and the specific optical coefficient at the wavelength of the maximum absorbance of

astaxanthin in the solvent (Delia B. Rodriguez-Amaya, Ph.D;, 2001)

2.3 Supercritical Antisolvent Micronization Apparatus

SAS experimental studies were carried out in apparatus constructed at IST, under supervision of Dra

Beatriz Nobre and Professor António Palavra at IST (Instituto Superior Técnico).

16

A schematic representation of the SAS apparatus is presented in the Figure 8.

Figure 8 schematic representation of SAS apparatus. BP1 and BP2, back pressure regulators; C, CO2 cooler; F, calibrated flow-meter; G, CO2 cylinder; GM, drytest meter; HE1 and HE2, heat exchangers;

LS, liquid solvent recovery vessel; MV, micrometering valve; P1 and P2, high-pressure pumps; PV, precipitation vessel; RD1, RD2 and RD3, rupture discs; S, liquid solution supply; WB, thermostatic

water bath.

The equipment is constituted by a high pressure precipitation vessel of stainless steel, with volume of

300ml, immersed in a thermostatic bath, in which is feed a gas stream of CO2 and the organic solution

of Astaxanthin. At the bottom of the vessel is placed a stainless steel frit, where the micronized powder

will be retained, while the mixture of the organic solvent and CO2 will pass through. Both streams pass

through a coil, which is placed inside a thermostated water bath, in order to ensure that they reach the

precipitation vessel at the desired temperature. The temperature of the water bath is maintained using

a thermostat and is verified by a thermometer. Pressure and temperature inside the precipitation

vessel are measured, respectively, by digital manometer and thermocouple. Concerning the Figure 8,

CO2 is cooled with an ice bath, C, before being compressed by a HPLC pump, P1, and the pressure

is controlled by a back pressure regulator, BP1. Afterwards, the CO2 is pre-heated in a heat exchanger,

HE1, and enters into the precipitation vessel, PV. Simultaneously, the solution, S, is pumped, P2, also

heated, HE2, and fed to the precipitation vessel through a nozzle (125m ID, 1 cm length, stainless

steel). This nozzle is located in a distinct inlet point from the CO2, but also in the top of the precipitation

vessel. The flow rate of the mixture that leaves the precipitator is controlled by a micrometering valve,

MV, located between the precipitator and the liquid solvent recover vessel, LS. Here the mixture suffers

a decompression (to a pressure < 30 bar) to induce the separation of the CO2 from the organic solvent.

The pressure in the vessel, LS, is controlled by a back pressure regulator, BP. After leaving the vessel

LS, the CO2 passes through a calibrated flow meter, F, and a dry test meter, GM to be quantified.

17

2.3.1 Experimental Procedure

The experimental procedure is the following: after reaching the target pressure, by pumping CO2, a

previous calculated amount of organic solvent is injected into this vessel to ensure that all the operation

will be carried out in steady state. When the organic solvent concentration inside the vessel reaches

the fed concentration, the micrometering valve, MV, is regulated to establish the flow rate at the exit

(bottom) of the precipitation vessel and it is given some time for the system to stabilize. In that point,

the solution is injected and the micronization takes place. At the end of the solution injection, SC-CO2

will pass through the precipitation vessel in order to remove all existing organic solvent. The washing

time with pure SC-CO2 is approximately 75 min.

In Figure 9 is presented a photo of the SAS micronization apparatus built at IST.

Figure 9 Photo of the SAS micronization apparatus at IST

The morphology of unprocessed and processed particles was assessed using Scanning Electronic

Microscopy (SEM) (CamScan MV 2300, England). Particles of the several samples were coated with

gold–palladium at room temperature before the examination. The accelerator voltage for scanning was

25.0 kV.

2.3.2 Determination of the Particle Size and Particle Size Distribution

Mean particle size and particle size distribution can be measured from SEM photomycrographs

manually and with ImageJ software. For the processing of the SEM images it was considered the ferret

diameter, a measure of a particle analyzed along a specified direction. In general, directionas the

18

measure of the particle size. Several techniques are available to characterize particle size, such as,

dynamic light scattering using Malvern Mastersizer Hydro 2000, Beckman Coulter Multisizer 4 or Nano

Particle Tracking Analysis (NTA, from Nanosight). These techniques were also used to determine the

mean particle size and size distribution of the processed astaxanthin. However, Coulter Multisizer,

only allows the measurement of particle with size between 1.17 and 30 m and the micronized powder

presented particles outside this range. In other hand, Mastersize Hydro2000 (Malvern), although

presents particle size range measurement from 0.2 to 1000 m was not suitable for the analysis of the

micronized astaxanthin, due probably to the agglomeration of the particles, which lead to particle size

distribution without smaller particles (0.1-1 m).

NTA enables to measure particle size in the range of 0.001 to 2 m. So, this technique was used to

determine the particles in this range for each sample, and the results were combined with those

obtained from the processing of images from SEM. NTA measurements were carried out suspending

a small amount of micronized powder (1-2 mg) in 30 ml of Millipore water.

2.3.3 Evaluation of the Purity of Micronized Astaxanthin

HPLC analysis was used to assess the purity and presence of degradation compounds of the obtained

micronized powder, as well as to determine the concentration of astaxanthin in the solution (organic

solvent and supercritical CO2) leaving the precipitation vessel.

For the determination of the purity of the micronized carotenoid, a known amount of the processed

astaxanthin was dissolved in acetone and the solution was analyzed by HPLC.

The HPLC system consisted in a Hewlett Packard HPLC 1100 series, with mobile phase of (methanol

and 0.2 % H2O)/acetonitrile (75:25v/v) set at 1 mL/min flow with the reversed phase column, 250×4.6

mmμ-bondapack. The UV–Vis detector was set to 475 nm. Methanol and acetonitrile were HPLC-

grade reagents used without further purification other than filtration and degassing. Astaxanthin was

quantified using a calibration curve with external standard (astaxanthin, Dr. Ehrenstorfer GmbH

(98,6%). Standard solutions of astaxanthin with concentrations in the range (0.25-8.65)mg/ml were

used for the calibration.

The purity of the micronized astaxanthin was determined by the ratio of amount of astaxanthin

determined by HPLC and the amount of micronized astaxanthin used for the analysis. Moreover, the

chromatograms of the micronized compound were compared with those of the standard in order to

determine the presence of other minor or degradation compounds.

The solution (organic solvent+supercritical CO2) leaving the precipitation vessel was also analysed in

order to assess the amount of astaxanthin exiting the precipitation vessel, as well as the presence of

impurities or degradation products. The total volume of the collected solution was measured and an

19

aliquot of this solution (2ml)l was submitted to evaporation under a gentle stream of N2. The solute

was then re-dissolved in acetone and analyzed by HPLC (the system previously described). All

experiments were carried out in duplicate.

2.4 Experimental Design

Design of experiments has been employed in many areas of investigation in order to maximize the

efficiency of scientific work and minimize waste and cost. It allows a smarter choice of experiments

that give the most information possible with the fewest experiments (Hibbert, 2012).

The usual terminology adopted refers factors as the conditions to be varied in order to obtain

determined response, variables are the responses obtained by the employment of different factors,

and levels are the values varied in each factor.

The major advantage of experimental design is considered the prediction of possible interactions

between factors. The effect that occurs when the combined change in two factors produces an effect

greater (or less) than that of the sum of effects expected from either factor alone. An interaction occurs

when the effect one factor has depends on the level of another (Montegomery, 2005).

The maxima, minima and how trend parameters are changed are given by an empirical function, a

model, usually linear or quadratic with interaction terms and is created a graphical relation of the

response to the values of one or more factors called response surface. Some important aspects to

take into account are randomization of the experiments to guarantee that any uncontrollable variables

contribute to the repeatability variance and replicates in order to have statistical significance.

Several research studies concerning supercritical antisolvent micronization experiments a wide list of

factors that can affect the result of the process is considered. Taking that into account 4 factors were

chosen: temperature, pressure, concentration of the organic solution and flow rate ratio of CO2/organic

solvent solution (which was experimentally applied by maintaining the CO2 flow rate constant and

considering different levels of the organic solution flow rate). In the SAS micronization process the

responses of most interest are mean particle size, particle size distribution, yield and purity of the

micronized powder (Cho, et al., 2014), (Brion, Jaspart, Perrone, Piel, & Evrard, 2009) (Bayat,

Pourmortazavi, Ahadi, & Iravani, 2013). In the present work the mean particle size and the yield of the

process were chose as the responses.

The first stage of experimental design is to find the factors that may significantly affect the response

of an experiment. When significant factors of the process are find the optimization of these is carried

out. In the present work the optimization of the response resides in the minimization of the particle size

and maximization of the yield of the process.

Fractional factorial design (FFD) is usually use as a screening method to determine the significant

effects, since it allows obtain the main effects model with a minimum number of experiments. In Table

20

1 are summarized the experiments to be considered in this first screening stage (FFD of SAS

micronization). Plus and minus signs represent factor levels at the maximum and minimum value and

0 is the half value level and the experiment (run) at this condition was performed with 3 repetitions,

so that the experimental deviation between can be determined.

Table 1 Fractional Factorial Design (FFD) for the SAS micronization of astaxanthin

run Pressure Temperature Flow rate concentration

1 + + + +

2 + + + -

3 + + - -

4 + - - -

5 + - + -

6 - + + +

7 - - + +

8 - - - +

9 - - - -

10 0 0 0 0

11 0 0 0 0

12 0 0 0 0

Using the responses obtained by the experimental work a factorial model is then constructed through

a list of coefficients multiplied by associated factor levels. This model is in the form of presented by

equation 1.

𝑌 = 𝛽0 + 𝛽1𝐴 + 𝛽2𝐵 + 𝛽3𝐶 + 𝛽4𝐷 + 𝛽12𝐴𝐵 + 𝛽13𝐴𝐶 + ⋯ (Eq. 1)

Where β, is the coefficient associated with factor n, and the letters, A, B, C, D, represent the factors in

the model. Combinations of factors, such as AB, represent an interaction between the individual factors

in the term.

Anova tests are then run by Design-Expert 9.0.3. The results given allow to determine the significance

of the model, lack of fit and the weight that each factor has in the model construction. The first

parameter is determined by R-squared value and the other two by p-value.

21

FFD can only lead to linear models of responses and cannot give information about maxima or any

non-linear relationships. Central composite designs are a response surface method that allow greater

numbers of levels without performing experiments at every combination of factor levels and cover the

factor space near the center, star points, with more points that in periphery. In this study was used a

face centered central composite design, where the star and factorial points lie on the faces of the

factorial design.

After determined the factors with higher importance by FFD a central composite design (CCD) can be

run. In this stage another matrix of experiments is generated, response values are introduced in the

matrix and Anova results predict once again the new model, by a similar equation. Having a significant

model and well-adjusted it is possible to run space design to find the response that meet our goal.

CCD generates a series of new experiments to obtain similar responses at different factors levels.

Those experiences can be run and new values can be introduced, so the model can be adjust or

confirmed.

3. Results

22

3.1 Solubility

Preliminary studies to select the organic solvent to use were carried out. The solubility of astaxanthin

in several organic solvents was determined. Table 2 shows the highest concentration attained by the

dissolution of astaxanthin in the each solvent, at room temperature, determined by spectrophotometric

analysis.

Table 2: Highest solubility of Astaxanthin in each organic solvent. Concentrations determined by spectrophotometry.

Organic Solvent Max. Solubility

(mg/ml)

Ethanol <0.02

Acetone 0.08

DCM <0.5

Di-Methyl-Sulfoxide 0.3

Acetone/ Methanol 70/30

<0.5

Ethyl-Acetate 0.35

n-Butanol ~1.0

Tetrahydrofuran ~4.0

SAS micronization of astaxanthin was carried out, as preliminary studies with the solvents that

presented the highest solubility and were the less toxic (GRAS class 3) The working conditions were

the set the same for all these experiments:, pressure of 150 bar, temperature of 40ºC and solution flow

rate of 0.5ml/min ( flow-rate ratio of 31 (wt.)). The first organic solvent used was acetone and the

solution concentration was 0.085mg.This experiment didn’t returned any micronized product and

possibly because the astaxanthin, that was in a very small amount, was dissolved in the mixture

supercritical CO2+acetone and went out of the extraction vessel.

SAS micronization of astaxanthin using ethyl acetate, with a solution concentration of 0.35mg/ml,

presented difficult to process, due to lack of solution stability, (formation of precipitate after dissolution)

and as a result no micronized powder was obtained.

Micronization with DMSO at 0.3 mg/ml returned very few particles, very long and visible at naked eye.

This solvent was, as well, rejected.

Finally, SAS micronization of astaxanthin was performed using THF solution with 1.0mg/ml

concentration, at the same conditions of pressure, temperature, and organic solution flow rate, and it

was possible to obtain micronized powder with a yield of 30.4%. Since THF was the only solvent that

returned a significant amount of micronized powder, and is a class 2 solvent, regulated to be used in

23

food industry, it was chosen as the organic solvent for the astaxanthin SAS micronization experimental

studies.

3.2 DOE In this study a fraction factorial and central composite design methods (FFD and CCD, respectively)

using statistic software DX9.0.3 were used for the estimation and optimization of effective parameters

on the micronization of astaxanthin by the supercritical anti-solvent process (SAS). The parameters

affecting SAS micronization are mostly pressure (P), temperature (T), organic solution concentration

(C) and flow rate ratio of supercritical CO2 and organic solvent solution.

In the present work, in order to select the variables that have the main effect on the mean particle size

of the micronized astaxanthin, fractional factorial design was used as a screening method. A FFD with

4 factors, and 2 responses was built. The range selected for each effective factor was carefully chosen:

40 to 60º C for temperature, 100 to 150 bar for pressure, 10 to 31 for CO2/organic solution flow rate

ratio and 0.15 to 3 mg/ml for solution concentration.

High level choice for solution flow rate was the highest possible in order to guarantee lower molar

fraction of THF on the mixture (THF+ supercritical CO2), so the organic solvent doesn’t work as a co-

solvent increasing the solubility of the solute in the mentioned mixture. In what concerns the

temperature, the high level of this effect was chosen in order to ensure that the carotenoid doesn’t

suffer degradation. Moreover, the concentrations of the organic solution high and low level were

established by the maximum concentration of astaxanthin in THF. Furthermore, pressure low and high

levels were chosen based on previous work of the SAS micronization apparatus (Cardoso et al., 2008 ).

Two response factors were chosen as the most important criteria to optimize the SAS micronization of

astaxanthin, and these were the mean particle size and the yield of the process (which was defined

as the ratio of the amount of micronized astaxanthin collected in the precipitation vessel and the

amount of astaxanthin in the organic solution).

The total matrix design showed 12 runs and is described in Table 3. Experiments were carried out by

the order of table 3, which was already randomized by the DX9 software.

24

Table 3: Matrix for FFD at 4 factors (Pressure, Temperature, Astaxanthin Concentration of organic solution and Solution Flow Rate) and 2 responses (Yield of Micronization and Mean Particle Size)

created by Design- Expert 9.0.3. Std. Dev. values stands for mean particle size analysis.

Run Pressure

(bar)

Tempe-rature

(°C)

Astaxanthin Concentration

(mg/ml)

Solution Flow

(ml/min)

Yield of mi-cronization

(%)

Mean Particle Size (µm)

Std. Dev.

1 125 50 1.575 1 77.2 4.165 13.083

2 100 40 0.15 0.5 62.9 3.401 5.303

3 150 40 0.15 1.5 33.0 15.337 10.103

4 125 50 1.575 1 77.2 4.764 20.146

5 125 50 1.575 1 86.0 3.361 15.921

6 100 60 0.15 1.5 49.3 1.194 44.077

7 150 60 0.15 0.5 7.3 0.193 0.105

8 100 60 3 0.5 68.7 0.182 7.221

9 125 50 1.575 1 85.71 5.547 13.475

10 150 40 3 0.5 78.4 7.980 9.374

11 100 40 3 1.5 80.5 2.800 7.636

12 150 60 3 1.5 87.6 2.415 82.654

Design evaluation was performed with a linear order and a polynomial model where singular effects of

the factors are evaluated and intercepted.

The experimental results obtained for the responses at each run of the FFD were evaluated using DX9

software and it was possible to observe that the evaluation was valid, since the obtained matrix showed

degrees of freedom (df) enough to determine efficiently the lack of fit (larger degrees of freedom

increase discrimination between adequate and inadequate models),. Lack of fit is an undesirable

characteristic for a model and its calculation involves the minimum of 3 df so a lack of fit test can be

run. The p-value obtained in the evaluation for the lack of fit was 0.136, which was higher than 0.05

proving that the obtained model was well adjusted. Also, another term to prove good adjustment was

“G efficiency”, which is inverse to the average variance, lack-of-fit and replicates tend to reduce the G

efficiency of a design. In this evaluation, and for the study of the response of the mean particle size,

“g efficiency” had a value of 71.4%, which is considered a good efficiency.

The analysis of variance (ANOVA) results was carried out to assess the main effects.

Since what was pretended was to evaluate the main effects in the SAS micronization process of

astaxanthin none transformation was done. Table 4 summarizes Anova for mean particle size analysis,

25

being considered that factors with p-value below 0.05 have significant effect. Negative values on

Stdized effect means an inverse proportionality between factor and response.

Table 4: Anova results for mean particle size analysis.

Term df Stdized Effect Sum of Squares

% Contribu-tion

F Value p-

value

Model 7 206.656 19.541 0.006

A-Pressure 1 7.408 109.767 51.607 72.655 0.001

B-Temperature 1 - 3.553 25.253 11.872 16.715 0.015

C-Flow 1 - 0.335 0.224 0.105 0.148 0.720

D-Concentration 1 - 4.510 40.686 19.129 26.930 0.007

AB 1 - 1.147 2.630 1.236 1.741 0.258

AC 1 - 0.535 0.573 0.269 0.379 0.571

AD 1 - 3.710 27.523 12.940 18.218 0.013

Residual 4 6.043

Lack of Fit 1 3.487 4.093 0.136

Pure Error 3 2.556

Cor Total 11 212.699

The Model F-value of 19.54 implies that the model is significant. From Table 4 it is also possible to

confirm that the terms A (pressure), B (temperature), D (concentration) and AD (interaction of pressure

and concentration) are significant model terms. On the other side, the "Lack of Fit p-value" is 0.136,

which implies that the Lack of Fit is not significant in relation to the pure error. Also, the R-Squared for

the model has a value of 0.9716. Moreover, the signal to noise ratio was of 15.083, which was higher

than 4 indicating an adequate signal. Therefore, the model (equation 2) obtained by DX9 FFD analysis

is appropriate to predict within the design space.

Thus the final Equation in Terms of Factors for mean particle size (MPS) analysis is the following (equation 2):

𝑀𝑃𝑆 = −28.87558 + 0.36608 × 𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒 + 0.10818 × 𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 + 2.35828 × 𝐹𝑙𝑜𝑤 + 4.91990 ×

𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 − 2.28790 × 10−3 × 𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒 × 𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 − 0.021522 × 𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒 × 𝐹𝑙𝑜𝑤 −

0.052027 × 𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒 × 𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 (Eq. 2)

26

Considering now the yield analysis, the given model had a significant curvature (centre points infor-

mation) p-value, which means that the design should be augmented via Design Tools to add runs that

can estimate quadratic terms. Problems with curvature result in different estimations for adjusted and

not adjusted models and the model may not be appropriate for prediction. Table 5 summarizes the

information for adjusted and unadjusted model.

Table 5 Anova for yield of micronization analysis for adjusted and unadjusted models.

Adjusted Model Unadjusted Model

Factor Coefficient Estimate

Sum of Squares

p-value Coefficient Estimate

Sum of Squares

p-value

Model 29.414 5 205.012 0.009 1.989 5 205.012 0.264

Intercept 223.601 231.291

A-Pressure - 1.358 295.186 0.042 - 1.358 295.186 0.424

B-Temperature - 1.082 35.989 0.319 - 1.082 35.989 0.772

C-Flow - 37.710 109.388 0.129 - 37.710 109.388 0.617

D-Concentration - 24.709 381.460 0.030 - 24.709 381.460 0.370

AB 0.004 9.946 0.575 0.004 9.946 0.878

AC 0.368 169.464 0.081 0.368 169.464 0.538

AD 0.312 986.568 0.008 0.312 986.568 0.180

Curvature 56.143 1 419.266 0.005 1 495.105

Lack of Fit 56.143 1 419.266 0.005

Standard deviation was 19.33 and R-Squared was 0.77. Even if the model obtained (Adjusted) is not

appropriate for prediction it can be used to make good diagnostics. Through p-value observation it

seems that the main effect that influences the response yield, in the micronization process, is the

concentration followed by the pressure. The given model for Yield prevision is represented by Equation

3.

𝑌𝑖𝑒𝑙𝑑 = 231.29132 − 1.35766 × 𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒 − 1.08150 × 𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 − 37.71000 × 𝐹𝑙𝑜𝑤 − 24.70877

× 𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 + 4.46000 × 10−3 × 𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒 × 𝑇𝑒𝑚𝑝𝑒𝑟𝑎𝑡𝑢𝑟𝑒 + 0.36820

× 𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒 × 𝐹𝑙𝑜𝑤 + 0.31172 × 𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒 × 𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 (Eq. 3)

Taking in account the results of FFD screening test study, to continue the optimization, two variables

were fixed at suitable conditions (temperature of 60ºC and flow rate ratio of 10) and a central

27

composite design with 2 factors (Pressure and organic solution concentration) and one response

(Mean Particle Size) was created. The obtained matrix was generated and randomized by Design-

Expert 9.0.3, and is presented in Table 6 with the respective values of the obtained response and

standard deviation of the particle size analyses.

Note that the fixed value for the temperature was chosen taking into account the morphology and

mean particle size results obtained with FFD. In fact, it was possible to observe from SEM images that

the morphology the micronized astaxanthin changed from long needles to small spheres (Figure 10)

when the temperature rose from 40 to 60ºC. Also, the mean particle size and particle size distribution

for the experiment carried out at 60ºC, 150 bar, 0.5 ml/min organic solution flow rate and 0.15 g/ml of

organic solution concentration, was significantly smaller and narrow, respectively, than the results

obtained at 40ºC. In order to confirm the selection of temperature, two experiments were carried out

at the following conditions. 40ºC, 100 bar 3mg/ml, 0.15 ml/min and 60ºC, 100 bar, 3 mg/ml, 1.5 ml/min.

SEM analysis of the micronized powder (Figure 10) showed that the experiment carried out at 60ºC

lead to small spheres with mean particle size of 1.354 µm and particle size distribution of 0.013 –

37.623 µm. In contrast the results obtained at 40ºC, lead to long needles with larges particle size and

particle size distribution. Therefore the temperature was fixed at 60ºC for the next CCD experiments.

B A B A

Figure 10: SEM images of SAS at (A) 40ºC, 100 bar, 3mg/ml and 0.15 ml/min and experience (B) 60ºC, 100 bar 3mg7ml and 1.5 ml/min

B A

28

Table 6: CCD matrix obtained by Design- Expert 9.0.3 at 2 factors (Pressure and Astaxanthin Concentration) and one response (Mean Particle Size). Std. Dev. stands for the Mean Particle Size

determination.

Run Pres-sure (bar)

Astaxanthin Concentration

(mg/ml)

Mean Parti-cle Size

(µm) Std. Dev.

1 100 1 3.192 1.173

2 125 3 1.338 18.782

3 100 2 0.619 0.985

4 125 1 78.446 92.622

5 125 2 48.623 44.895

6 150 2 58.283 92.00

7 100 3 0.202 0.122

8 125 2 49.384 49.384

9 125 2 38.623 40.405

10 150 1 218.578 248.947

11 150 3 60.037 79.781

12 125 2 35.916 40.405

13 125 2 52.976 36.814

14 125 1 94.135 101.884

For evaluation purposes it was considered a linear order and a polynomial model. G-efficiency of the

model was of 53.1%, which is not very high, but was the best one. The degree of Freedom criterion

were full filled with 6 df for lack of fit and 5 df to pure error. Maximum, average and minimum variance

mean were, respectively, of 0.404, 0.175 and 0.071.

Particle size analysis required a natural log transformation, so the model could be better adjusted.

Quadratic order for Anova calculation was selected, as suggested for Design-Expert 9.0.3 software.

Table 7 presents the results of Anova for the chosen model in CCD.

29

Table 7 Anova results for choosen model in CCD calculated by Design-Expert 9.0.3.

Source Sum of Squares

df Coefficient

Estimate Standard

Error F Value

p-value Prob > F

Model 49.5131 5 22.5873 0.0002

A-pressure 30.3096 1 2.2476 0.2703 69.1346 0.0000

B-concentration 13.1914 1 - 1.3976 0.2548 30.0890 0.0006

AB 0.5387 1 0.3670 0.3311 1.2288 0.2998

A^2 2.1718 1 - 0.8376 0.3764 4.9537 0.0567

B^2 1.3845 1 - 0.6628 0.3730 3.1580 0.1135

Residual 3.5073 8

Lack of Fit 3.4233 3 67.9122 0.0002

Pure Error 0.0840 5

Cor Total 53.0204 13

The Model F-value of 22.59 implies that the model is significant. There is only a 0.02% of possibili-

ties that a large F-value could occur due to noise. In this case the pressure and concentration effects

are significant model terms. The "Lack of Fit F-value" is of 67.91, which implies that the term is sig-

nificant. Table 8 summarizes with predicted and adjusted values Anova.

Table 8: Predicted and adjusted values.for Anova

Std. Dev. 0.66 R-Squared 0.9338

Mean 2.96 Adj R-Squared 0.8925

C.V. % 22.39 Pred R-Squared 0.4497

PRESS 29.17 Adeq Precision 16.819

The "Pred R-Squared" of 0.4497 is not so close to the "Adj R-Squared" of 0.8925 as can be normally

expect, i.e. the difference is more than 0.2. This result may indicate a large block effect, or a possible

problem with the model and/or data. Things to consider are model reduction, response transformation,

outliers, etc. All empirical models should be tested by doing confirmation runs.

"Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. The obtained

ratio of 16.819 indicates an adequate signal. The final model in terms of factors is presented in Equa-

tion 4, and can be used to estimate mean particle size progression in the design space.

𝐿𝑛(𝑀𝑃𝑆) = −24.81728 + 0.39560 × 𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒 − 0.58137 × 𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 + 0.014680 × 𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒 ×

𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛 − 1.34023 × 10−3 × 𝑃𝑟𝑒𝑠𝑠𝑢𝑟𝑒2 − 0.66281 × 𝐶𝑜𝑛𝑐𝑒𝑛𝑡𝑟𝑎𝑡𝑖𝑜𝑛2 (Eq. 4)

30

Optimization of the factors can be done numerically and graphically. Numeric optimization search in

the design space, using the model created during analysis to find factor settings that meet the defined

purposes, which in this case was to minimize mean particle size. A set of 21 solutions were given, so

the aim was fulfilled, being the first solution the one with lower prediction of mean particle size. Figure

11 represents the graphical optimization of the created model with the first solution optimization

marked.

In post analysis, is possible to introduce the desired work conditions and determine the predicted re-

sponse values with confidence intervals. Also, it is possible to compare the results predicted by the

model to the outcome of a confirmation experiment. In this case the desirable mean particle size was

the minimum that is possible to get by the model, 0.179 µm approximately. The working conditions to

obtain this response were 100 bar for pressure and 3mg/ml for astaxanthin concentration in the organic

solution, these conditions were already experimented in CCD at run number 7 and had a result of

0.202 µm for mean particle size. This value was inserted in the interval predicted, 95% PI low 0.018

and 95% PI High 1.078, meaning that the model predicted efficiently the range of particle size distri-

bution. After the real value is given to the program, it can adjust the coefficients attributed to the factors

resulting in a reliable prediction (Table 9).

Design-Expert® SoftwareFactor Coding: ActualOriginal Scaleparticle size (um)

Design points above predicted valueDesign points below predicted value218.578

0.202

X1 = A: pressureX2 = B: concentration

1 1.5 2 2.5 3100 112.5

125 137.5

150

0

50

100

150

200

250

300

pa

rti

cle

siz

e (

um

)

A: pressure (bar)

B: concentration (mg/ml)

0.170896

Figure 11: Graphical representation of created CCD model at 2 factors (Pressure and Concentration) and 1 response (mean particle size). The labeled point (0.17) is an estimation of the minimum response that can be

obtained from the given model.

31

Table 9: Coefficients table for the model adjusted after rectification of solution 1 in Design -Expert 9.0.3

Response Intercept A B AB A^2 B^2

LN(Particle Size) 3,547745 2,247578 -1,39762 0,366995 -0,83764 -0,66281

p-value < 0.0001 0.0006 0.2998 0.0567 0.1135

3.3 Particle Analysis

SAS micronized Astaxanthin powder was analysed by scanning electron microscopy (SEM). It was

possible to notice that the morphology of the particles change with the experimental conditions. Parti-

cles morphologies observed were mainly two: long or middle needles and small spheres.

The raw astaxanthin powder (standard) showed spheres with a large particle size distribution (see

Figure 12).

Figure 12: SEM image of unprocessed astaxanthin.

32

Figure 13: SEM images at 100 bar, 60ºC, 1.5 ml/min organic solution flow rate and at astaxanthin

concentration in organic solution of A- 0.1575 mg/ml(FFD run 6), B-1mg/ml (CCD run1), C- 2mg/ml

(CCD run3) and D-3 mg/ml (CCD run7).

From the SEM images showed in Figure 13 it is possible to see the effect of concentration, particu-

larly in particle morphology. When concentration of astaxanthin increased in the organic solution,

particle morphology changed from long needles like to sphere like particles. Particle size is also af-

fected by concentration. However, the significant effect is observed when comparing different work-

ing pressures. Figure 14 shows the effect of pressure at astaxanthin concentration in the organic so-

lution of 3 mg/ml, 60ºC and 1.5 ml/min of organic solution flow-rate.

A B

C D

33

Figure14: SEM images at 3mg/ml (CCD run 2), 60ºC and 1.5 ml/min (CCD run 11) and pressures of 125 and 150 bar respectively.

Comparing image D from Figure 13 and images E and F from Figure 14 it is possible to verify that

pressure has a major effect on particle size, being verified that when pressure increased micronized

astaxanthin presented larger particle size.

Particle size measurements were made by ImageJ software through analysis of SEM images, Master-

sizer and Nano Particle Analysis (NTA). These methods are complementary in terms of range of size

and other constrains. Table 10 compares different methodology to particle size determination.

E F

34

Table 10: Particle size results obtained by SEM images analysis (mean feret and range feret), nanosight, and particle size range by mastersize

Run Mean Particle

Size (µm) mean

feret (µm) range

feret(µm)

mean na-nosight

(µm)

range na-nosight(µm)

range mas-ter(µm)

FFD

1 4.165 4.165 318.000 415.000

2 3.401 3.401 62.946 77.988

3 15.337 15.337 4533.865 829.859

4 4.764 4.764 50.582 136.942

5 3.361 3.361 500,000 362.000

6 1.194 11.468 129.363 0,147 1,989 103.268

7 11.504 11.504 518.554 2185.340

8 0.182 0.305 0.352 0,193 1,000 0.255

9 5.547 5.547 377.000 305.412

10 7.980 7.980 408.000 51.000

11 2.800 2.800 184.918 207.671

12 2.415 2.415 88.000 157.000

CCD

1 3.192 1 449.800 478.321 0.166 1.252

2 1.338 1.338 280.771

3 0.619 18.807 45.634 0.144 1.998

4 78.446 78.446 627.554

5 44.895 44.895 857.934

6 58.283 58.283 608.954

7 0.202 0.202 1.020

8 49.384 49.384 830.757

9 38.623 38.623 905.051

10 218.578 218.578 1 587.891

11 60.037 60.038 728.095

12 35.916 35.916 950.175

13 52.976 52.976 797.613

14 94.135 94.135 583.743

35

All SEM images were analyzed by ImageJ software although in run number 6 of FFD and 1 and 3 of

CCD only a part of the population could be measured. The rest of the population in those runs was too

small to be efficiently measured. Run 9 of FFD and run 7 of CCD couldn’t be measured in ImageJ. For

those runs with very small particles, NTA was the suitable technique to use, since it is very accurate

in the nanoscale. For samples that presented a mixture of small and large particles (particle size range

between nanometers and microns), the mean particle size and particle size distribution was measured

with two complementary techniques. The first one uses SEM images (with software imageJ) for the

larger particles, while the second the NTA analysis, for the particles in the nanoscale range.

3.4 Purity

HPLC analysis of the micronized astaxanthin at 60ºC, 100 bar, organic solution concentration of

3mg/ml and solution flow rate of 1.5 ml/min showed that the micronized powder presented a

composition of around 100% astaxanthin (relative percentage of pigments obtained from HPLC). It

was not observed the presence of other pigments, such as impurities or degradation compounds.

Analysis of the solution collected from the separation vessel, for the SAS experiments carried out in

the previously mentioned experimental conditions, showed the presence of other pigment. Astaxathin

corresponded only to 72% (relative percentage obtained from HPLC chromatograms) of the total

pigments in the solutions. The other pigment present in the chromatogram, which presented a relative

composition of 28%, could be a degradation product or the impurities of the initial astaxanthin that

were concentrated in the solution leaving the precipitation vessel. Possibly these impurities are more

soluble in supercritical CO2 than the astaxanthin and so are dissolved in the flow leaving the high

pressure vessel.

Figure 11 shows the chromatograms of the initial astaxanthin (standard), the micronized powder and

the collected solution.

36

Figure 15 – HPLC chromatograms of A-astaxanthin standard, B- solution from separation vessel (100 bar, 60ºC, 1.5 mg/min and 3 mg/ml), C- micronized

astaxanthin (100 bar, 60ºC, 1.5 mg/min and 3 mg/ml).

37

The concentration of astaxanthin in the solution collected from the separation vessel was determined.

Since the precipitation occurred inside a high pressure vessel it can be assumed that solid-gas

equilibrium was achieved. Thus, the amount of astaxanthin collected allows to determine the solubility

of astaxanthin in the mixture (supercritical CO2+THF) at the experimental conditions of pressure,

temperature and molar fraction of THF, at which the micronization occurred. Particularly interesting

are the results obtained for the micronization experiments of CCD runs 2, 7 and 11, which were carried

out at 60ºC, 1.5 ml/min ( yTHF= 0.05 mole fraction), 3mg/ml and pressures of 100, 125 and 150 bar.

The obtained results of solubility of astaxanthin in the mixture supercritical CO2+THF (0.05 mol), are

presented in the figure 16, as mole fraction of astaxanthin in function of the pressure. Moreover, the

data of solubility astaxanthin in supercritical CO2 (Fuente, Oyarz´un, Quezada, & Valle, 2006) (Youn,

Roh, Weber, Wilkinson, & Chun, 2007) are also presented in the graphic of figure 16 in order to

compare the solubility of astaxanthin in pure supercritical CO2, presented in literature, and the results

obtained in this work for the mixture supercritical CO2+THF (0.05 mole fraction). It can be observed

from the figure 16 that the solubility of astaxanthin increases when THF is used as co-solvent (0.05

mole fraction). Moreover, the increase in solubility is more pronounced for higher pressures. In fact,

solubility of astaxanthin in the mixture SCCO2+THF (0.05 mole fraction) at 100 bar there is only an

increase of three times fold of the solubility, comparing to pure SC CO2. On the other hand, for higher

pressures, 125 bar and 150 bar, solubility of astaxanthin .in the mixture SCCO2+THF (0.05 mole

fraction) showed a 10 fold increase when compared to solubility in pure SC CO2.

Figure 16: Mole fraction of astaxanthin in pure SC CO2 (Fuente, Oyarz´un, Quezada, & Valle, 2006) (Youn, Roh, Weber, Wilkinson, & Chun, 2007) and SC CO2+THF (0.05 mole fraction), at 60ºC

38

4. Discussion

FFD Run 8 and CCD Run 7 were performed at the same conditions of pressure, 100 bar, concentration

of organic solution, 3.0 mg/ml, and temperature, 60°C. The only difference between the experimental

conditions of both runs is the organic solution flow rate, which was of 0.5ml/min in Run 8 of FFD and

1.5ml/min in Run 7 of CCD. Experimental design analysis showed insignificant contribution of the

solution flow rate factor in the astaxanthin micronization process. However, comparing the particle size

distribution (Figure 12) of both runs, it is possible to verify a small difference between them. Moreover,

SEM images of both runs showed a slight difference in the morphology of the particles (Figure 17).

Figure17: Particle size distribution of FFD Run8 (100 bar, 60°C, 3 mg/ml and 0.15 ml/min) and CCD

Run7 (100 bar, 60°C, 3 mg/ml and 1.5 ml/min).

From the Figure 18 it is possible to verify that for the lower organic solvent flow-rate particle size

presents a narrow particle size distribution.

The combination of these facts indicates that even if the solution flow rate has numerically low

relevance in the FFD model, its contribution can be considered important to obtain a narrow particle

size distribution.

0

0,001

0,002

0,003

0,004

0,005

0,006

0,007

0,008

0 100 200 300 400 500 600 700 800

%

o

f

P

a

r

t

i

c

l

e

s

Mean Particle Size (nm)

CCD Run 7

FFD Run 8

39

These results ilustrate an extra valorization of solution flow rate, which was discarded in

The images presented in Figure 18 are at the same amplification, and it can be seen that, although

the morphology was the same for both organic solution flow rates, sphere-like particles, the run at the

lower organic solvent flow rate showed smaller particles with very similar particle size. On the other

hand, when a higher organic solution flow-rate was used slightly larger particles were obtained. From

Tables 3 and 6 it is possible to verif a difference of 20 nm between the two samples

Temperature effect, althought also not considered in CCD, had an expressive effect in the

micronization process. It was found that a complete different morphology, as well as smaller particle

size could be obtained at 60°C. A possible reason for this behaviour is the fact that the rise in

temperature leads to an increase of astaxanthin solubility in THF and, since the concentration remains

the same, a less saturated solution is obtained. Astaxanthin will be more disperse in the solvent and

interaction of organic solvent/ supercritical anti-solvent occurs and astaxanthin will precipitate in

smaller particles. To a better visualization of temperature effect, Figure 18 shows the evolution of mean

particle size with this factor. As seen in this figure MPS decreases with the increase of temperature

Figure 18: SEM images of a sample of micronized astaxanthin at solution flow rate of 0.15ml/min (Run 8 FFD)

at the left, and flow rate of 1.5ml/min (Run 7 CCD) at the right.

40

Figure 19: Graphical representatio6n of mean particle size evolution with temperature change.

In what concerns the effect of pressure, in the SAS micronization of astaxanthin, CCD analysis allowed

to obtain the results shown on Table 6, as well as in the Figure 20.

Figure 20: Mean particle size of SAS micronized astaxanthin as a function of pressure, at 60ºC, 3 mg/ml and 1.5 ml/min organic solution flow rate.

The point at 100 bar shows that at this pressure there is a higher probability to find particles with

desirable properties, and increasing this factor will lead to a larger range of the particle size. Therefore,

higher organic solution concentration and lower pressure proved to be the most favorable conditions

for the SAS micronization of astaxanthin. Figure 18 represents the effect of pressure at 60ºC, 1,5

0

10

20

30

40

50

60

70

80

90

38 43 48 53 58 63

MP

S (µ

m)

Temperature (°C)

100 bar

150 bar

41

ml/min solvent flow-rate and 3 mg/ml of organic solution concentration (the same trend was observed

for the other organic solution concentrations studied – see Table 6). Mean particle size increases with

pressure and particle size distribution becomes narrower for lower pressures. The increase of particle

size with pressure has been notice by other authors for SAS of compounds like beta-carotene or

lycopene (Cocero et al., 2006 and Cocero et al., 2008).

A possible explanation can be the fact that the increase in pressure corresponds to a rise in the density

of the supercritical fluid and consequently the solubility of astaxanthin increases in CO2 since the

supersaturation decreases leading, to a decrease in the particle size.

Figure 21: Mean particle size of SAS micronized astaxanthin as a function of organic solution concentration, at 60ºC, 100 bar and 1.5 ml/min organic solution flow rate.

In Figure 21 is shown the influence of organic solution concentration in the mean particle size, at 100

bar, 60ºC and 1.5 ml/min of organic solution flow rate. It can be observed that the mean particle size

decreased with the increase of astaxanthin concentration in the organic solution. Larger particles with

a different morphology, similar to needle-like, were obtained when using the lowest concentration.

When using concentrations of 3 mg/ml, sphere-like particles and lower mean particle sizes were

obtained. A narrower particle size distribution was also achieved using 3 mg/ml. This trend possibly

occurs, because, the higher concentration allows to attain higher supersaturation, which tends to

decrease the particle size.

Yield of micronization obtained for the experiments with lower particle size was of approximately 67%

for FFD and 50% for CCD.

0,0

1,0

2,0

3,0

4,0

5,0

6,0

0 1 2 3 4

Mean P

art

icle

Siz

e (

m)

Organic Solution concentration (mg/ml)

42

5. Conclusion

Micronization of astaxanthin was successfully carried out by SAS process. FFD showed that pressure

and concentration were the significant factors in the micronization of astaxanthin. The lowest particle

size found was 0.182 µm at 100 bar, 60°C, 3mg/ml and 0.5mg/ml.. The obtained particles showed a

sphere-like morphology and narrow particle size distribution (range of 1.0 µm).

Regarding future work, encapsulation of micronized astaxanthin could be a good approach to

implementation in food and nutraceutical industry. To accomplish this, micronized astaxanthin could

be encapsulated in PEG solution, or other and then processed by SAS micronization. With this new

process it could be obtained nano-sized particles coated with polymer, which could improve its

dispersion in water formulations, as well as protect this sensitive carotenoid and so improve its use in

food and pharmaceutical formulations.

43

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