Micronization of astaxanthin by the supercritical anti-

solvent process (SAS)

Joana Jorge da Costa Dep. Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Portugal

*Corresponding author: joanajorgecosta@gmail.com

Abstract This aim of this work was the micronization of synthetic astaxanthin at 98.6% purity 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 bar), 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. 2

experiments were run to have a better understanding of the temperature influence, it showed that

it influences morphologies of micronized particles, and that at increasing temperature, sphere like

and smaller particles were obtained. Central Composite Design was studied for optimization

process. Factors for this analyse was pressure (100-150 bar) and concentration (1-3 mg/ml) and

mean particle size for the response. Temperature and flow rate were maintained respectively at

60° C and1.5ml/min. Minimum mean particle size obtained was of 0.182 nm during the screening

process at 100 bar, 60° C, 0.5ml/min and 3 mg/ml. Central composite design predicted that a

similar response could be obtained at flow rate of 1.5 ml/min but the mean particle size found in

this conditions was a little higher, 0.202 nm.

Introduction Astaxanthin is a red carotenoid from the

carotenoid family. 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

stress. 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 in cosmetics (Guedes et al.,

2011). 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). 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).

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.

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 designed using traditional micronization

techniques or supercritical and compressed

gas based micronization techniques.

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.

The most common micronization techniques

are spray drying, mechanical comminution,

solute recrystallization, freeze drying, and

interfacial polymerization. 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, changing the crystal structure of the

precipitated powder.

To overcome those disadvantages

micronization techniques relying on

supercritical fluids technology were

developed. In the SAS process (Figure1),

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.

Figure 1: 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).

The rapid contact between the two media

causes the precipitation of the solute, which

is mediated by solubility interactions

between the supercritical fluid and the liquid

solvent. 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. 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).

Materials and Methods

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%). 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,

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)

SAS experimental studies were conducted

in apparatus constructed at IST, under

orientation of Dra Beatriz Nobre and

Professor António Palavra at IST (Instituto

Superior Técnico).

The experimental procedure was 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.

The morphology of unprocessed and

processed particles was assessed using

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.

ImageJ software was used to analyze SEM

photomicrographs, considering the ferret

diameter as the measure of the particle size.

Malvern Mastersizer Hydro 2000, Beckman

Coulter Multisizer 4 or Nano Particle

Tracking Analysis (NTA, from Nanosight)

were also used to determine the mean

particle size and size distribution of the

processed astaxanthin.

HPLC analysis was used to evaluate 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.

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).

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. 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

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.

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.

Results and Discussion

THF was the only solvent that returned a

significant amount of micronized powder,

and is a class 2 solvent, regulated to be used

in food industry, it was chosen as the organic

solvent for the astaxanthin SAS

micronization experimental studies.

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, 1.0

to 1.5 for

CO2/organic solution flow rate ratio and 0.15

to 3 mg/ml for solution concentration. 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 1. Experiments

were carried out by the order of table 1.

The analysis of variance (ANOVA) results

was carried out to assess the main effects.

Table 2 summarizes Anova for mean particle

size analysis, 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.

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).

Run Pressure

(bar)

Temperat

ure (°C)

Astaxanthin

Concentration

(mg/ml)

Solution

Flow

(ml/min)

Yield of

micronization

(%)

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

Term df Stdized Effect Sum of Squares

% Contribution 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

Table 2: Anova results for mean particle size analysis.

Table 1: 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

Considering now the yield analysis, the

given model had a significant curvature

(centre points in-formation) 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.

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.

Taking in account the results of FFD

screening test study, to continue the

optimization, two variables were fixed at

suitable amounts (temperature of 60ºC and

flow rate ratio of 10) and a central 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 3 with the respective

values of the obtained response and

standard deviation of the particle size

analyses. Note that value fixed 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 2) 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

Run Pressure

(bar)

Astaxanthin Concentration

(mg/ml)

Mean Particle

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

Figure 2: 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

Table 3: 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.

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 with the

results obtained at 40ºC which lead to long

needles with larges particle size and particle

size distribution. Therefore the temperature

was fixed at 60ºC.

In CCD 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 4 presents the results of ANOVA for

chosen model in CCD.

The Model F-value of 22.59 implies the

model is significant. There is only a 0.02%

chance that an F-value this large could occur

due to noise. In this case the effects A

(pressure) and B (concentration) are

significant model terms. The "Lack of Fit F-

value" is of 67.91, which implies that the

Lack of Fit is significant. Table 5 resumes

Anova results with predicted and adjusted

values. The "Pred R-Squared" of 0.4497 is

not as close to the "Adj R-Squared" of

0.8925 as one might normally expect; i.e. the

difference is more than 0.2. This 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.

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

"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

Equation 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)

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 2 represents the

graphical optimization of the created model

with the first solution optimization marked.

Table 4 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

Table 5: Resume of Anova results with predicted and adjusted values.

Particles morphologies observed in SEM

photomicrographs, were mainly two: long or

middle needles and small spheres. Figure 3

shows particle SEM images of raw

astaxanthin and processed by SAS. From

the SEM images showed in figure 3 images

A, B and C, it is possible to see the effect of

concentration, particularly 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 affected due

concentration. However, the significant

effect is observed when comparing different

working pressures. Images D and E shows

the effect of pressure at astaxanthin

concentration in the organ-ic solution of 3

Comparing image C with images D and E it

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

given model.

R A B

C D E

Figure 3: SEM images of: Raw Astaxanthin (R); A- Micronization CCD run 1 (1.0 mg/ml); B- CCD Run3 (2.0mg/ml); C- CCD Run7 (3.0mg/ml); D- CCD Run2 (125bar); E- CCD Run11 (150bar).

. Its possible to verify that pressure as a major effect on particle size, being verified that when pressure increased micronized astaxanthin presented larger particle sizemg/ml, 60ºC and 1.5 ml/min of organic solution flow-rate.Particle size measurements were made by Image J software through analysis of SEM images, Mastersize and Nano Particle Analysis (NTA). These methods are complementary in terms of range of size and other constrains. 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 minor or degradation Analysis of the solution collected in the separation vessel, for the SAS experiments carried out in the previously mentioned experimental conditions,products. 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

compostion 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 this impurities are more soluble in

supercritical CO2 than the astaxanthin and

so are dissolved in the flow leaving the high

pressure vessel.

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 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 3).

From the Figure 4 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. These results ilustrate an extra

valorization of solution flow rate, which was

discarded in experimental design. The

images presented in Figure 17 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

Figure 4: 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.

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

Figure5: Particle size distribution of FFD Run8 and CCD Run7.

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 6

shows the evolution of mean particle size

with this factor. As seen in this figure MPS

decreases with the increase of temperature.

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 19. 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 6: Graphical representatio6n of mean particle

size evolution with temperature change.

Figure 7 represents the effect of pressure at

60ºC, 1,5 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.

In Figure 8 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

0

0,002

0,004

0,006

0,008

0 200 400 600 800

%

o

f

P

a

r

t

i

c

l

e

sMean Particle Size (nm)

CCD Run 7

FFD Run 8

0

20

40

60

80

100

38 48 58 68

MP

S (µ

m)

Temperature (°C)

100 bar150 bar

0

50

100

150

50 100 150 200

Mean P

art

icle

Siz

e

(mm

)

Pressure (bar)

Figure 7: 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 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.

Conclusion

Yield of micronization obtained for the

experiments with lower particle size was

of approximately 67% for FFD and 50%

for CCD. Conclusion

Micronization of astaxanthin was

successfully done by SAS process. The

lowest particle size found was 0.182 µm at

100 bar, 60°C, 3mg/ml and 0.5mg/ml. in this

experiment (Run8 FFD) was found particle

size with sphere morphology and narrow

particle size distribution ( range of 1.0 µm).

References

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Hannay, J., & Hogarth, J. (1879). Proc. Roy. Soc. London .

Hong, H. L., Suo, Q., Han, L., & Li, C. (2009). Study on Precipitation of Astaxanthin in Supercritical Fluid. Powder Technology.

HUSSEIN, G., GOTO, H., ODA, S., SANKAWA, U., MATSUMOTO, K., & WATANABE, H. (2006). Antihypertensive Potential and Mechanism of Action of Astaxanthin:IIIAntioxidant and Histopathological Effects in Spontaneously. Biol. Pharm. Bull.

Marco, I. D., Cardea, S., & Reverchon, E. (2013). Polymer Micronization using Batch Supercritical Antisolvent process. CHEMICAL ENGINEERING TRANSACTIONS.

Marco, I. D., & Reverchon, E. (2011). Influence of pressure, temperature and concentration on the mechanisms of particle precipitation in supercritical antisolvent micronization. J. of Supercritical Fluids.

Mattea, F., Martín, Á., & Cocero, M. (2009). Carotenoid processing with supercritical fluids. Journal of Food Engineering.

Mendes, R. L., Nobre, B., Cardoso, M., Ana P. Pereira, A., & Palavra, A. (2003). Supercritical carbon dioxide extraction of compounds with pharmaceutical importance from microalgae. Inorganica Chimica Acta.

Miguel, F., Martín, A., Gamse, T., & Cocero, M. (2006). Supercritical anti solvent precipitation of lycopene Effect of the operating parameters. J. of Supercritical Fluids.

Miguel, F., Martín, A., Mattea, F., & Cocero, M. (2008). Precipitation of lutein and co-precipitation of lutein and poly-lactic acid with the supercritical anti-solvent process. Chemical Engineering and Processing.

Montes, A., Gordillo, M., Pereyra, C., & de la Ossa, E. (2013). Supercritical CO2 precipitation of poly(l-lactic acid) in a wide range of miscibility. J. of Supercritical Fluids.

D

0,0

1,0

2,0

3,0

4,0

5,0

6,0

0 2 4Mean P

art

icle

Siz

e

(mm

)

Organic Solution concentration (mg/ml)

Figure 8: Mean particle size of SAS micronized astaxanthin as a function of organic solution

concentration