domain: shs sdv info

text:
Abderrahmane Aït-Kaddour
email: abderrahmane.aitkaddour@vetagro-sup.fr

Sylvain Jacquot

Didier Micol

Anne Listrat

Discrimination of Beef Muscle Based on VIS-NIR Multispectral Features: Textural and Spectral Analysis

Keywords: beef, muscle, breed, multi-spectral, textural analysis

The potential of multispectral VIS-NIR imaging to discriminate beef meat muscles in relation with their type and animal origin was examined in the present study. Two hundred and forty muscles of three types (Longissimus thoracis, Biceps femoris and Semimembranosus) were obtained from the carcasses of three types of animals, two late-maturing cattle types of animals (Limousin and Blond d'Aquitaine) that grow slowly and deposit more muscles and less fat, compared to one early-maturing cattle types of animals (Angus) which tends to have muscles richer in collagen and in intramuscular fat. Two hundred and forty cube images were collected with nineteen emitting LEDs (405 to 1050 nm) using the Videometer Lab2 device.

The image cubes were processed in order to extract image mean spectra and image shape features from co-occurrence and difference of histogram matrices. The results of the PLSDA performed on image texture features and spectral data show a maximum ranging from 63.5 to 83% of good classification depending on the muscle and breed considered.

INTRODUCTION

Beef meat has always been an important component in human diet. The development in the quantity and availability of this product leads the beef industry to be more and more concerned by the authenticity and the detection of adulteration. In recent years, the retail cost of beef having increased significantly, the substitution of high value raw meat with cheaper meat has also increased [START_REF] Cozzolino | Identification of animal meat muscles by visible and near infrared reflectance spectroscopy[END_REF] . The value of beef meat is not only determined by muscle type from which it comes but also the type of animals. Types of animals or crossbreed animals with high intramuscular fat content (for example Aberdeen Angus) are known to give a meat with higher scores for sensory attributes such as texture, flavor and juiciness [START_REF] Bureš | Chemical composition, sensory characteristics, and fatty acid profile of muscle from Aberdeen Angus, Charolais, Simmental, and Hereford bulls[END_REF] and types of animals with high muscle mass (for example Blonde d'Aquitaine) to give tender meat [START_REF] Monson | Influence of cattle breed and ageing time on textural meat quality[END_REF] . In this context, it is therefore necessary to provide to beef manufacturers a tool to achieve rapid and precise discrimination of raw pieces and of types of animals from which they come.

Classical spectroscopic techniques (fluorescence, visible and infrared) are known to be generally quick and inexpensive to implement. They are used in line as they do not require direct contact with the product to make a measurement. These methods can therefore be a remarkably effective alternative to traditional analyzes, that are generally expensive and technically long to implement. The fluorescence and infrared spectroscopy have, in recent years, demonstrated their ability to be used to conduct and process control in the food industry [START_REF] Bertrand | La spectroscopie infrarouge et ses applications analytiques[END_REF][START_REF] Karoui | Development of a portable spectrofluorometer for measuring the quality of cheese[END_REF] . However, these methods provide information on a small region of a sample at a Comment citer ce document : Ait-Kaddour, A., Jacquot, S., Micol, D., Listrat, A. (Auteur de correspondance) (2017). Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis. International Journal of Food Properties, 20 (6), 1391-1403. DOI : 10.1080/10942912.2016.1210163

A c c e p t e d M a n u s c r i p t 3 time. Therefore, the product analyzed should be reasonably isotropic in order to extract features which represent the properties of the whole product. This disadvantage of conventional spectroscopy can be easily overcome by the use of commonly known hyperspectral (HIS) and multispectral (MIS) imaging [START_REF] Gowen | Hyperspectral imagingan emerging process analytical tool for food quality and safety control[END_REF] techniques. Those technics enable to record spectral and spatial features of a region of interest based on image analysis on a part (i.e. few square mm) or on the entire sample (i.e. ten square cm) providing a higher representative data of the sample studied. MIS enables to record a cube images on a small number of wavelengths that provides the possibility to record images located on different areas of the electromagnetic spectrum (UV, VIS, or/and NIR). The MIS imaging has been used in different studies, for example to authenticate and predict physicochemical parameters of blue veined cheeses [START_REF] Monson | Influence of cattle breed and ageing time on textural meat quality[END_REF][START_REF] Jacquot | Potential of Multispectral Imager to Characterize Anisotropic French PDO Cheeses: A Feasibility Study[END_REF] and for quality assessment of pomegranate fruit [START_REF] Khodabakhshian | Development of a multispectral imaging system for online quality assessment of pomegranate fruit[END_REF] . For meat products analysis, MIS was used recently to characterize sensory properties, physicochemical parameters, microbial quality and adulteration of beef [START_REF] Abouelkaram | Muscle study with multispectral image analysis[END_REF][START_REF] El Jabri | Image analysis study of the perimysial connective network, and its relationship with tenderness and composition of bovine meat[END_REF][START_REF] Kulmyrzaev | Potential of a custom-designed fluorescence imager combined with multivariate statistics for the study of chemical and mechanical characteristics of beef meat[END_REF][START_REF] Ropodi | Multispectral image analysis approach to detect adulteration of beef and pork in raw meats[END_REF] . Studies on beef showed the potential of this method to predict tenderness (0.89 ≥ R 2 ≥ 0.75), to discriminate samples according to animal age (85% of good classification in average) and muscle type (91% of good classification in average) and to predict (0.61 ≥ R 2 ≥ 0.91) some physicochemical characteristics of samples (mechanical properties, total collagen and lipids). The studies conducted up to date show some limits due principally to the number of muscles, animal diets, and muscle type considered. So, the aim of this study was to assess, by introducing a high characteristics compared to animals from Limousin breed [START_REF] Dubost | Structural and biochemical characteristics of bovine intramuscular connective tissue and beef quality[END_REF] . Those objectives were conducted both by using image mean spectra and image texture features.

MATERIALS AND METHODS

Beef production and muscle sampling

The experiment was performed on 40 young entire males from three types of animals of three breeds: Aberdeen Angus (AA) (n = 12), Limousine (LI) (n = 14) and Blonde d'Aquitaine (BA) (n = 14). The conditions of production of animals were previously described by Dubost, et al. [START_REF] Dubost | Structural and biochemical characteristics of bovine intramuscular connective tissue and beef quality[END_REF] . In order to increase the variability between samples, three muscles (Longissimus thoracis (LT), Semimembranosus (SM) and Biceps femoris (BF)) were taken from each animal at 24 h post-mortem from carcasses chilled in a cold room (+2°C) giving a total of 120 meat samples. For each muscle, samples of 10x7x3 cm were taken in parallel to the direction of the muscle fibers in height, at the same localization inside each muscle, from the 9 th rib for LT and from the centre of the muscle for SM and BF. They were progressively frozen in pure ethanol, kept at -20°C and then thawed just before performing multispectral image analysis.

Acquisition of muscle multispectral images (MSI)

The multispectral image of the 120 meat samples were recorded with a VideometerLab2 ® device (Videometer A/S, Danemark) equipped with a multi-spectral camera (Point Gray

Research, Scorpion SCOR-20SOM, 1200x1200 pixels) for laboratory analysis that is able to A c c e p t e d M a n u s c r i p t 5 measure light intensity in the VIS-NIR region from 405 to 1050 nm using 19 emitting LEDs [3, 7] . Before MSI acquisition, the camera was calibrated by 3 successive plates: a white one for reflectance correction, a dark one for background correction and a doted one for the pixel position calibration. To ensure a total diffuse illumination of the object without shading and reflection, the camera was equipped with an Ulbricht sphere, internally painted with a diffuse reflecting paint, and an opening in the top and underside of the sphere. The top hole was used for placing the camera, whereas the bottom hole was used to place the object for image acquisition.

For each muscle and before image acquisition, the samples previously kept at -20°C were defrosted at 4°C, equilibrated at 20°C in a water bath, and then cut gently into a 6 x 5 x 1.5 cm 3 , wiped out by paper towels to eliminate moisture on meat surface and placed in the dark by lowering the hollow sphere until it comes into contact with the sample support plate. Each sample was then illuminated successively by the 19 emitting LEDs giving a cube image with 1200 pixels in the X axe, 1200 pixels in the Y axe and Z the number of LED (Fig. 1). Two images were recorded per muscle, representing at least 160 cube images for one breed and a total of 240 cube images (i.e. 4560 images).

Image mean spectra

Two average image spectra (SPECT) were recorded per meat sample by using the VideometerLab2 ® software. The SPECT data were extracted from a region of interest (ROI) of 700x575 pixels selected at the center in order to prevent the side effects for each meat sample giving a data base containing 240 image mean spectra.

Comment citer ce document : Ait-Kaddour, A., Jacquot

Pretreatment of image mean spectra

In order to optimize the prediction accuracy of the models by decreasing the scattering effects, three pre-processing methods were investigated, Standard Normal Variate (SNV), Multiple Scattering Correction (MSC) and Normalization (AREA, corresponding to the area under curve = 1). Data preprocessing were performed by using the PLS-Toolbox v.7.5 (Eigenvector Research) for MATLAB 2013b (The Mathworks Inc., Natic, MA, USA).

Image texture features extraction

Second-order statistics features were investigated in the present study to evaluate the potential of image texture features to discriminate muscles. The second order statistics are defined as the likelihood of observing the absolute difference between a pair of gray values occurring at the endpoints of a dipole (or needle) of random length placed in the image at a random location and orientation (Fig. 2) [START_REF] Conners | 2(3), 204-222 . A theorical comparison of texture algothms[END_REF][START_REF] Haralick | Textural Features for Image Classification[END_REF][START_REF] Weszka | A Comparative Study of Texture Measures for Terrain Classification[END_REF] . To extract image texture features from muscle images, two methods were used. The first one consisted on the analysis of the Gray Level Histogram Difference matrix (GLHD) and the second one on the analysis of the Gray Level Cooccurrence Matrix (GLCM). The texture features were calculated in the same ROI (700x575 pixels) used to extract the SPECT data that enabled to decrease the time calculation and to prevent the sample side effects.

For the GLCM method, texture features were calculated on four symmetric GLCM using a dipole length (d) of 1 and successively an orientation angle (θ) of 0°, 45°, 90°, and 135°. By this method it was possible to extract a total of 29 textural features (Fig. 2) as previously reported [START_REF] Monson | Influence of cattle breed and ageing time on textural meat quality[END_REF][START_REF] Jacquot | Potential of Multispectral Imager to Characterize Anisotropic French PDO Cheeses: A Feasibility Study[END_REF] . As a result, a GLCM textural matrix of 240 image cubes x 29 variables were calculated [START_REF] Monson | Influence of cattle breed and ageing time on textural meat quality[END_REF][START_REF] Jacquot | Potential of Multispectral Imager to Characterize Anisotropic French PDO Cheeses: A Feasibility Study[END_REF] .

Comment citer ce document : Ait-Kaddour, A., Jacquot For the GLHD method, the same d and θ values were used enabling to extract up to five texture features (mean, variance, entropy, contrast and energy) (Fig. 3). As a result, a GLHD textural matrix of 240 image cubes x 4 variables were calculated. The different GLCM and GLHD textural features were calculated using a home program in MATLAB R2013b (The Mathworks Inc., Natic, MA, USA), based on ximage toolbox.

Partial Least Square Discriminant Analysis

Partial Least Square Discriminant Analysis (PLSDA) were performed on auto-scaled data for GLCM and GLHD features and on mean centered image mean spectra after preprocessing in MATLAB R2013b (The Mathworks Inc., Natic, MA, USA) using the PLS-Toolbox v.7.5

(Eigenvector Research). The total number of samples was divided into two sets, 70% (i.e. 84) of the samples were used for model calibration and 30% (i.e. 36) to verify the model robustness (validation), by using the Nearest Neighbor Thinning method proposed in the PLS-Toolbox v.7.5 (Eigenvector Research). This method enabled to select validation samples which best fill out all covariance space.

PLSDA is a well spread technique and its aim is to predict the membership of an individual to a qualitative group defined as a preliminary.

RESULTS

Image mean spectra analysis

As examples, normalized mean spectra of the three different muscles (BF, LT, and SM) of LI breed and spectra of the three different types of animals (AA, BA, and LI) were presented in Fig. 5 and Fig. 6 respectively. It can be identified that the general trends of the image mean spectra were similar, whatever the types of animals. All the spectra presented two bands, a small one from 405 to 570 nm with a maximum at 500 nm and a huge one from 570 to 1050 nm with a shoulder at 645 nm and a maximum at 850 nm. More precisely, in the VIS region (i.e. from 405 to 700 nm), BF and SM muscles presented equivalent reflectance, while LT muscle tended to present higher reflectance values. In the NIR region (i.e. from the 700 to 1050 nm) the opposite was observed, LT muscle tended to present the lowest reflectance intensities (Fig. 5). The three animal types presented about the same trends of the image mean spectra. More precisely, in the VIS region (i.e. from 405 to 700 nm), LI and BA animals presented equivalent reflectance, while AA tended to present higher reflectance values. In the NIR region (i.e. from the 700 to 1050 nm) the opposite was observed, AA breed tended to present the lowest reflectance intensities (Fig. 6).

Classification of muscles based on image mean spectra and image texture features

Percentage of CC, precision, and error of the best PLSDA models carried out on image mean spectra (i.e. SPECT), GLHD and GLCM features to classify samples based on muscle categories and types of animals were summarized in Tables 1 and2 respectively. First of all, we observed that for all the prediction models, the calculated percentages of CC of the were observed when using the normalization (i.e. AREA) preprocessing method coupled with 10 PLS factors (mean CC: 82%, mean precision: 93%, and mean error: 13%). The score plot of the first 2 LVs confirmed the results observed on image mean spectra (Fig. 7), illustrating good discrimination of LT muscles from BF and SM for which some overlapping was observed (Fig. 7A). In addition, the plot of loadings (Fig. 7B) provided interesting information about the association of the wavelengths with meat samples. Thus, the wavelengths in the visible region (405 to 700 nm) are correlated with LT samples, whereas the remaining wavelengths are correlated with the BF and SM samples.

Applying GLCM (72% mean CC, 84% mean precision and 19.5% mean of error after validation) and GLHD (69% mean CC, 80.5% mean precision and 16% mean of error after validation) features with PLSDA was less successful for muscle discrimination. The best PLSDA models for GLCM and GLHD were calculated with a small number of regression factors (5 ≤ LV), the same LED (940 nm) but with different orientation values (θ), 45° and 135° respectively. Considering the classification results of each muscles show that LT muscles where well reclassified compared to BF muscles and SM muscles regardless of the method used for discrimination (i.e. SPECT, GLCM and GLHD).

DISCUSSION

In recent years the growing consumer demand for information on the quality and conditions of food production has prompted suppliers to improve the information available on the food product. In order to satisfy the consumer expectations, the companies need efficient tools to

Effect of image mean spectra preprocessing on classification accuracy

Preprocessing is performed after data acquisition to modify the data before further analysis.

Most of the time, its purposes is to remove bias, to linearize the response of the variables and to remove variations which are not of interest in the analysis (interfering variance) [START_REF] Panneton | Effects of preprocessing of ultraviolet-induced fluorescence spectra in plant fingerprinting applications[END_REF] . The best preprocessing method differs between the objective of discrimination (i.e. muscles and types of animal) and the features considered (i.e. GLCM, GLHD and SPECT) suggesting that it is impossible to know beforehand which preprocessing would lead to the most accurate model as previously reported [START_REF] Fernández-Cabanás | Optimisation of the spectral pre-treatments used for Iberian pig fat NIR calibrations[END_REF] . The results also show, and recalled, that normalization is not at all a neutral operation and has to be carefully chosen.

Sample discrimination by multispectral data

PLSDA has been used in order to evaluate the discriminant ability, as a function of muscle types and types of animals, of texture features (GLCM and GLHD) and image mean spectra (i.e. SPECT) extracted from multispectral images. Preliminary, mean spectrum of each muscle was investigated and enabled a good discrimination of samples with respect to the types of animals and muscle types, with CC accuracy ranging from 77 to 82% after external validation. The results depicted that dissimilarity between characteristics of muscle types (BF, LT, and SM) is higher than the difference observed between types of animals as noted on mean spectrum. The present conclusion was consistent with a previous study conducted on biochemical and structural parameters on the same samples [START_REF] Dubost | Structural and biochemical characteristics of bovine intramuscular connective tissue and beef quality[END_REF] .

Comment citer ce document : Ait-Kaddour, A., Jacquot The discrimination between muscle types seems to arise from difference in reflectance intensities of meat water, fat and protein contents and from their interactions. Indeed, most of the spectral information located in the spectral range studied is sensitive to meat pigments and matrix characteristics (fatty acids, proteins, and moisture) [START_REF] Cozzolino | Identification of animal meat muscles by visible and near infrared reflectance spectroscopy[END_REF] . The 3 muscles investigated presented differences between 405 to 700 nm region due to muscle pigments (i.e. myoglobin, deoxymyoglobin, metmyoglobin, and oxymyoglobin) [START_REF] Liu | Prediction of color, texture, and sensory characteristics of beef steaks by visible and near infrared reflectance spectroscopy. A feasibility study[END_REF][START_REF] Prieto | Discrimination of beef dark cutters using visible and near infrared reflectance spectroscopy[END_REF] . This spectral region was used for colors prediction of meat beef (a* value) and pork (a* and b* values). It was reported that oxymyoglobin had a single absorption band around 560 nm [START_REF] Swatland | Infrared Fiber Optic Spectrophotometry of Meat[END_REF][START_REF] Swatland | Color Measurements on Pork and Veal Carcasses by Fiber Optic Spectrophotometry[END_REF] . High correlation was found, in meat pork, between the 592 and 632 nm wavelength bands for both a* and b* values, respectively [START_REF] Cozzolino | The use of visible and near-infrared reflectance spectroscopy to predict colour on both intact and homogenised pork muscle[END_REF] . The region around 632 nm was noted as being an indicator of oxymyoglobin, myoglobin and total pigments [START_REF] Davis | Analysis of Spectral Reflectance for Measuring Pork Quality[END_REF][START_REF] Franke | Quantitative determination of metmyoglobin and total pigment in an intact meat sample using reflectance spectrophotometry[END_REF] . Thus dissimilarities observed from 405 to 700 nm may result from differences in muscle color. This hypothesis has been verified by at least two authors. Torrescano, et al. [START_REF] Torrescano | Shear values of raw samples of 14 bovine muscles and their relation to muscle collagen characteristics[END_REF] have shown that SM and BF of Swiss Brown young bulls were very close for the yellow color (b* value) compared to LT muscle, while Isdell, et al. [START_REF] Isdell | Effect of packaging cycle on the colour stability of six beef muscles stored in a modified atmosphere mother pack system with oxygen scavengers[END_REF] depicted a similar red color (a* value) between BF and SM muscles compared to LT muscles. Based on these observations, it appears that SM and BF muscles presented almost the same color while LT muscle presented a different color.

Spectral difference was also observed between the 850 and 1050 nm. The 890 nm band was identified as the most significant wavelength for predicting water content in fried minced meat [START_REF] Frosch | Determination of astaxanthin concentration in Rainbow trout (Oncorhynchus mykiss) by multispectral image analysis[END_REF] , while the 850 and 870 nm bands may be assigned to O-H vibration of water molecules. The spectral bands located between the 910 and 970 nm can be assigned to CH and OH vibrations related to proteins and water [START_REF] Bertrand | La spectroscopie infrarouge et ses applications analytiques[END_REF][START_REF] Alomar | Chemical and discriminant analysis of bovine meat by near infrared reflectance spectroscopy (NIRS)[END_REF][START_REF] Daugaard | Multi-Spectral Analysis Of Frying Processes For Meat Products[END_REF][START_REF] Murray | The NIR spectra of homologous series of organic compounds[END_REF] . The band at 940 nm can be related to meat fat [START_REF] Ziadi | Visible and nearinfrared light transmission: A hybrid imaging method for non-destructive meat quality evaluation[END_REF] . The 1050 nm band is close to the bands assigned to fat and proteins [START_REF] Osborne | Near-Infrared Spectroscopy in Food Analysis[END_REF][START_REF] Šašić | Band Assignment of Near-Infrared Spectra of Milk by Use of Partial Least-Squares Regression[END_REF][START_REF] Workman | Interpretive Spectroscopy for Near Infrared[END_REF] . These spectral differences were consistent with biochemical analysis of beef meat [START_REF] Bureš | Chemical composition, sensory characteristics, and fatty acid profile of muscle from Aberdeen Angus, Charolais, Simmental, and Hereford bulls[END_REF]36] . Bureš, et al. A c c e p t e d M a n u s c r i p t 13 [START_REF] Bureš | Chemical composition, sensory characteristics, and fatty acid profile of muscle from Aberdeen Angus, Charolais, Simmental, and Hereford bulls[END_REF] noted significant difference between LT (0.46-0.498 g/100g) and SM (0.368-0.389g/100g) total fatty acids while Dubost, et al. [36] reported on equivalent samples a higher total lipids content in LT muscles (8.46g/100 g DM) compared to SM and BF muscles (6.70 and 7.65 g/100 g respectively). Those spectral differences could also depict differences in structural characteristics of beef muscles [START_REF] Dubost | Structural and biochemical characteristics of bovine intramuscular connective tissue and beef quality[END_REF] . Those authors reported that SM and BF muscles were close in their structural (e.g. perymisium area, endomysium area, branch points) characteristics while LT muscle was different.

Secondly, treatment of image texture features, extracted from GLHD and GLCM for each of the 19 LEDs, by PLSDA enabled to discriminate muscles depending on muscle type and breed with an accuracy ranging from 58 to 72% in average. For the discrimination of types of animals, the best's models, with respect to textural features, were obtained with LEDs in the visible region (505 and 525 nm for GLCM and GLHD respectively). The 505 nm band was assigned to metmyoglobin [37,38] and the 525 nm band was identified as sensitive to meat color [39] . This last band was used in tandem with the 610 nm band as an indicator of the percentage of oxymyoglobin [40] . Those observations are also in agreement with the difference of muscle metabolism of the three types of animals. Indeed, muscles of animals such as AA are more oxidative and less glycolytic than those of animals such as LI [41] . The muscles of BA are more glycolytic and consequently less red than AA muscles that are more oxidative. It is also reported that late-maturing cattle breeds (such as BA) deposit more muscle and less fat, compared to early-maturing cattle (such as AA) [36,42] but no wavelength assigned to proteins or fat (i.e. 910, 940, 970, 1050 nm) was identified as being the most discriminating one.

With respect to muscle type, the best classification considering textural features were both obtained with the 940 nm excitation LED suggesting that muscle discrimination could be assigned to difference in muscle marbling [START_REF] Ziadi | Visible and nearinfrared light transmission: A hybrid imaging method for non-destructive meat quality evaluation[END_REF] . The results of the present study are in agreement with previous investigation but enabled to identify a new discrimination LED in the NIR region (i.e. 940 nm) but with lower classification accuracy. The best LED for discrimination of muscle type was identified as being the white-light by Kulmyrzaev, et al. [START_REF] Kulmyrzaev | Potential of a custom-designed fluorescence imager combined with multivariate statistics for the study of chemical and mechanical characteristics of beef meat[END_REF] . The white-light enabled to obtain a 91% of good classification for three muscles [START_REF] Kulmyrzaev | Potential of a custom-designed fluorescence imager combined with multivariate statistics for the study of chemical and mechanical characteristics of beef meat[END_REF] . The dissimilarity in CC between the two studies could arise from the lower variability between samples investigated by Kulmyrzaev, et al. [START_REF] Kulmyrzaev | Potential of a custom-designed fluorescence imager combined with multivariate statistics for the study of chemical and mechanical characteristics of beef meat[END_REF] . Indeed, only muscles from one breed (i.e.

Limousine) was analyzed by image texture features. Difference in muscle studied (Longissimus dorsi, Gluteus medius, and Semitendinosus) and excitation channel used (i.e.

white-light channel and UV channel) could also explain the best results reported previously.

In the present study, using spectra for discrimination gave higher accuracy for muscle classification (77 to 82%) compared to textural features (58 to 72%). This difference may arise from considering texture features of only one LED of the cube images at a time before performing PLSDA. To validate this hypothesis, the GLHD and GLCM texture features of the 19 LEDs and each direction were merged in two independent matrices and analyzed by PLSDA. The results obtained with respect to types of animals (61% and 59% of CC considering the GLCM and GLHD respectively) and muscle types (69 and 61% of CC considering the GLCM and GLHD respectively) did not validate our hypothesis.

To optimize the discrimination accuracy of the PLSDA based on textural features (i.e. GLHD and GLCM) four angle values (i.e. 0°, 45°, 90° and 135°) were investigated. This study depicted that no specific orientation angle was identified as being the best one for samples discrimination based on muscle type or type of animals. types of animals) and conditions of animal production investigated (e.g. feeding) to enhance the samples variability, this study brings new elements comforting the potential of multispectral image analysis to authenticate beef muscles. Nonetheless, the models obtained are not ready to be used in practical conditions because a higher number of muscles and types of animals should be investigated. But we hope that multispectral imaging could be used in a near future as a rapid, non-invasive method for muscles authentication and prediction of quality parameters because it can drastically reduce analytical time and cost of traditional measurements.

Comment citer ce document : Ait-Kaddour, A., Jacquot, S., Micol, D., Listrat, A. (Auteur de correspondance) (2017). Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis. International Journal of Food Properties, 20 (6) Cluster prominence) [START_REF] Cozzolino | Identification of animal meat muscles by visible and near infrared reflectance spectroscopy[END_REF] .

Comment citer ce document : Ait-Kaddour, A., Jacquot

  variability in muscle sample characteristics, and the potential of higher number of exciting LED (i.e. 19 excitation LEDs ranging from the Near-UV to the Near infrared) than those previously used to discriminate beef muscles. For that, we have chosen to work on three different muscles of three types of animals, two late-maturing cattle breeds (Limousin and Blonde d'Aquitaine) and one early-maturing cattle breed (Aberdeen Angus). Animals from Aberdeen Angus and Blonde d'Aquitaine breeds were extreme from their muscle Comment citer ce document : Ait-Kaddour, A., Jacquot, S., Micol, D., Listrat, A. (Auteur de correspondance) (2017). Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis. International Journal of Food Properties, 20 (6), 1391-1403. DOI : 10.1080/10942912.2016.1210163 A c c e p t e d M a n u s c r i p t 4

  , S., Micol, D., Listrat, A. (Auteur de correspondance) (2017). Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis. International Journal of Food Properties, 20 (6), 1391-1403. DOI : 10.1080/10942912.2016.1210163 A c c e p t e d M a n u s c r i p t 7

A c c e p t e d M a n u s c r i p t 8 procedure

 8 Comparison of the predicted groups to the real group is generally used as an indicator of the quality of the discrimination and it is valued as the percentage of correct classifications (CC). In the present study, model performance was measured in terms of percentage of CC, precision and prediction error. The optimum number of PLSDA factors was estimated by analyzing the percentage of validation error and the variance captured error. Before performing PLSDA meat samples were divided in three classes related to muscles (BF, LT and SM) and types of animals (AA, BA, and LI). The Comment citer ce document : Ait-Kaddour, A., Jacquot, S., Micol, D., Listrat, A. (Auteur de correspondance) (2017). Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis. International Journal of Food Properties, 20 (6), 1391-1403. DOI : 10.1080/10942912.2016.1210163 of the data extraction from multispectral images, before performing PLSDA, was summarized in Fig. 4.

  rapidly authenticate food and detect possible adulterations. In this context the potential of a multispectral imager was evaluated in a first part to discriminate three bovine muscles (BF, LT, and SM) sampled on three types of animals extreme for their characteristics and in a Comment citer ce document : Ait-Kaddour, A., Jacquot, S., Micol, D., Listrat, A. (Auteur de correspondance) (2017). Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis. International Journal of Food Properties, 20 (6), 1391-1403. DOI : 10.1080/10942912.2016.1210163 A c c e p t e d M a n u s c r i p t 11second part to discriminate these three types of animals. These objectives were conducted both by using image mean spectra (i.e. SPECT) and image texture features, GLHD and GLCM coupled with PLSDA.

A c c e p t e d M a n u s c r i p t 12

 12 , S., Micol, D., Listrat, A. (Auteur de correspondance) (2017). Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis. International Journal of Food Properties, 20 (6), 1391-1403. DOI : 10.1080/10942912.2016.1210163

  Comment citer ce document : Ait-Kaddour, A., Jacquot, S., Micol, D., Listrat, A. (Auteur de correspondance) (2017). Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis. International Journal of Food Properties, 20 (6), 1391-1403. DOI : 10.1080/10942912.2016.1210163

  Comment citer ce document : Ait-Kaddour, A., Jacquot, S., Micol, D., Listrat, A. (Auteur de correspondance) (2017). Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis. International Journal of Food Properties, 20 (6), 1391-1403. DOI : 10.1080/10942912.2016.1210163 A c c e p t e d M a n u s c r i p t 14

A c c e p t e d M a n u s c r i p t 15 CONCLUSION

 15 Comment citer ce document : Ait-Kaddour, A., Jacquot, S., Micol, D., Listrat, A. (Auteur de correspondance) (2017). Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis. International Journal of Food Properties, 20 (6), 1391-1403. DOI : 10.1080/10942912.2016.1210163 Multispectral imaging was used as a rapid method for meat beef authentication based on types of animals and muscle types. The results demonstrated that sample discrimination is more accurate when attempting to discriminate muscle types and more challenging when attempting to discriminate muscle according to types of animals. Moreover, PLSDA performed better when considering spectra and less when considering GLCM and GLHD features. In this study, selection of optimum characteristic images is a critical step, which can greatly affect prediction accuracy. Therefore, in order to improve the classification accuracy by using texture features it would be interesting to test different dipole lengths (e.g. d = 2,3, 4…) and to perform chemometric analysis (e.g. Principal Components Analysis or Minimal Noise Fraction), directly on the cube image in order to select optimum characteristic images before extraction of texture features. Taking into account the number of samples (3 muscles and 3

A c c e p t e d M a n u s c r i p t 24 Fig. 1 .M a n u s c r i p t 25 Fig. 2 .

 241252 Fig. 1. (A) An example of a cube image of beef muscles (y = number of pixel; x = number of

M a n u s c r i p t 26 Fig. 3 .M a n u s c r i p t 27 Fig. 4 .M a n u s c r i p t 28 Fig. 5 .M a n u s c r i p t 29 Fig. 6 .M a n u s c r i p t 30 Fig. 7 .

 263274285296307 Fig. 3. Process of GLHD (Gray Level histogram difference) features extraction from the

  This study was carried out in compliance with the French recommendations and those of the Animal Care and Use Committee of the National Institute for Agricultural Research (INRA, Institut National de la Recherche Agronomique) of Clermont-Ferrand/Theix, France for the use of experimental animals including animal welfare.

  , S., Micol, D., Listrat, A. (Auteur de correspondance) (2017).

	a n u s c r i p t
	M
	A c c e p t e d
	6
	Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and
	spectral analysis. International Journal of Food Properties, 20 (6), 1391-1403. DOI :
	10.1080/10942912.2016.1210163

  generally lower compared to the calibration percentages of CC which is somehow expected as validation data were not initially included in model calibration, but used as unknown samples for prediction.In the present study, three mathematical preprocessing techniques were applied to image mean spectra (AREA, MSC, and SNV) in order to obtain the best discrimination result. This study demonstrated that nearly all-preprocessed models calculated are more parsimonious than the un-preprocessed models except for the PLSDA conducted on the types of animals

	a n u s c r i p t
	M
	The best models for GLCM and GLHD textural features were obtained with excitation wavelengths and orientations (θ) of 505 nm/0° and 525 nm/135° respectively. Considering the percentage of CC of each samples show that types of extreme animals (BA and AA) where A c c e p t e d
	well reclassified compared to intermediate animals (LI) regardless of the method (i.e. SPECT,
	GLCM and GLHD) applied for discrimination. Very encouraging results were obtained when
	10

Comment citer ce document : Ait-Kaddour, A., Jacquot, S., Micol, D., Listrat, A. (Auteur de correspondance) (2017). Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis. International Journal of Food Properties, 20 (6), 1391-1403. DOI : 10.1080/10942912.2016.1210163 A c c e p t e d M a n u s c r i p t 9 validation steps were which gave the best results with raw data. More precisely, PLSDA performed on image mean spectra to discriminate samples relative to types of animals showed that the best models were obtained by un-preprocessed spectra (raw data) with 10 PLSDA factors. After external validation, the mean of regression parameters obtained were, on average for the 3 types of animals, 77%, 86%, and 22% for CC, precision and error classification respectively. After external validation, PLSDA models considering GLCM features gave lower classification accuracy on average for the 3 types of animals, with 63%, 78% and 27% of CC, precision and error respectively. Using GLHD features with PLSDA gave the worst results, with mean regression factors of 58%, 73% and 29% for CC, precision and error respectively. PLSDA was applied for the discrimination of muscle type regardless of the data considered (i.e. SPECT, GLCM and GLHD features). With mean spectra (i.e. SPECT) the best results Comment citer ce document : Ait-Kaddour, A., Jacquot, S., Micol, D., Listrat, A. (Auteur de correspondance) (2017). Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis. International Journal of Food Properties, 20 (6), 1391-1403. DOI : 10.1080/10942912.2016.1210163

Table 1 .

 1 Comment citer ce document : Ait-Kaddour, A.,Jacquot, S., Micol, D., Listrat, A. (Auteur de correspondance) (2017). Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis. International Journal of Food Properties, 20 (6), 1391-1403. DOI : 10.1080/10942912.2016.1210163 Comment citer ce document : Ait-Kaddour, A., Jacquot, S., Micol, D., Listrat, A. (Auteur de correspondance) (2017). Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis. International Journal of Food Properties, 20 (6), 1391-1403. DOI : 10.1080/10942912.2016.1210163 PLSDA results of beef meat samples for calibration and external validation based on the animal breed (BA: Blonde d'Aquitaine; LI: Limousine; AA: Angus Aberdeen; CC:

	a n u s c r i p t
	M
	A c c e p t e d

, 1391-1403. DOI : 10.1080/10942912.2016.1210163 16 Comment citer ce document : Ait-Kaddour, A., Jacquot, S., Micol, D., Listrat, A. (Auteur de correspondance) (2017). Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis. International Journal of Food Properties, 20 (6), 1391-1403. DOI : 10.1080/10942912.2016.1210163 Comment citer ce document : Ait-Kaddour, A., Jacquot, S., Micol, D., Listrat, A. (Auteur de correspondance) (2017). Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis. International Journal of Food Properties, 20 (6), 1391-1403. DOI : 10.1080/10942912.2016.1210163 Comment citer ce document : Ait-Kaddour, A., Jacquot, S., Micol, D., Listrat, A. (Auteur de correspondance) (2017). Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis. International Journal of Food Properties, 20 (6), 1391-1403. DOI : 10.1080/10942912.2016.1210163

Table 2 .

 2 PLSDA results of beef meat samples for calibration and external validation based on the muscle type (BF: Biceps Femoris; LT: Longissimus Thoracis; SM: Semi Membranosus;

		CC: percentage of correct classification); SPECT: image mean spectra, GLHD: Gray level
		histogram difference and GLCM: Gray level co-occurrence matrix; RAW: un-preprocessed
		spectra; SNV: Standard Normal Variate spectra, MSC: Multiple Scattering Correction spectra;
		ARE: Area under curve = 1; Direction: best angle identified for predictive models; CC:
		percentage of correct classification; PLSDA factor: number of loading vectors for the best
		discrimination)
					PLSDA		Confusion matrix for calibration Confusion matrix for validation
				LED
	Method Preprocess Direction		Factor	Classes
				(nm)
					(validation)			Precision	Error		Precision	Error
							CC (%)			CC (%)
								(%)	(%)		(%)	(%)
			-	-		BF	78	88.5	16	69.0	95.5	16
	SPECT	AREA	-	-	10	LT	96	95.0	6	97.5	97.6	8.5
			-	-		SM	83	89.0	12	80.5	86.2	14
						Mean	83	91	11	82	93	13
		-
						BF	61.0	79.0	25	62.0	79.0	24
		-
	GLCM		135°	940	5	LT	97.5	94.0	4	96.0	94.0	5.0
		-
						SM	60.0	74.0	24	57.0	80.0	30
						Mean	73	82	18	72	84	20
		-				BF	51.0	73.0	28	54.0	75.0	22
	GLHD	-	45°	940	4	LT	97.0	96.0	3	96.0	94.0	5.0
		-				SM	63.0	83.0	24	58.0	73.0	20
						Mean	70	84	18	69	81	16

Comment citer ce document : Ait-Kaddour, A., Jacquot, S., Micol, D., Listrat, A. (Auteur de correspondance) (2017). Discrimination of beef muscle based on visible-near infrared multi-spectral features: Textural and spectral analysis. International Journal of Food Properties, 20 (6), 1391-1403. DOI : 10.1080/10942912.2016.1210163


Christelle Crampon
email: christelle.crampon@univ-amu.fr

Clémence Nikitine

Mohamed Zaier

Olivier Lépine

Céline Dejoye Tanzi

Maryline Abert Vian

Farid Chemat

Elisabeth Badens

Oil extraction from enriched Spirulina platensis microalgae using supercritical carbon dioxide

Keywords:

This article deals with the extraction of neutral lipids and antioxidants from enriched Spirulina platensis microalgae using supercritical carbon dioxide (CO 2 ), and more particularly with the influence of experimental conditions on extraction yields and kinetics at laboratory and pilot scales. Preliminary studies were carried out at laboratory scale before the establishment of an experimental design: extraction curves were plotted for different autoclave fill rates, and under different conditions of pressure and temperature. Using a Response Surface Methodology, the significant influence of pressure on extraction efficiency was highlighted. Surface responses showed that, in the studied experimental field, mass loss increased when pressure, temperature, and CO 2 /microalgae mass ratio increased. Extract analyses showed that oil extracts contained chlorophylls a and b, as well as ␤-carotene. Finally, larger-scale experiments were carried out with batches of 1 and 50 kg (scale-factors of 100 and 5000, respectively) and the results were consistent with those obtained at laboratory scale.

Introduction

For several years now, microalgae have been the subject of a growing interest in research and industry for health-related and energy applications. Indeed, microalgae are photosynthetic organisms that have the ability to adapt to many environments (even the most extreme). Moreover, microalgae can synthesize high-added value compounds in significant quantity when subjected to particular growing conditions. For example, some species are able to accumulate high content of oil [START_REF] Chisti | Biodiesel from microalgae[END_REF] and particularly when submitted to nitrogen defaults. The oil quality, due to the presence of polyunsaturated fatty acids such as eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA) or ␥-linolenic acid (GLA) [START_REF] Ackman | Algae as sources of edible lipids[END_REF][START_REF] Mendes | Supercritical CO2 extraction of ␥-linolenic acid (GLA) from the cyanobacterium Arthrospira (Spirulina) maxima: experiments and modeling[END_REF][START_REF] Mendes | Supercritical CO2 extraction of ␥-linolenic acid and other lipids from Arthrospira (Spirulina) maxima: comparison with organic solvent extraction[END_REF][START_REF] Mendes | Supercritical fluid extraction of active compounds from algae, chapter 6[END_REF][START_REF] Plaza | In the search of new functional food ingredients from algae[END_REF][START_REF] Tang | Study on supercritical extraction of lipids and enrichment of DHA from oil-rich microalgae[END_REF] and antioxidants (i.e. ␤-carotene) [START_REF] Herrero | Sub-and supercritical fluid extraction of functional ingredients from different natural sources: plants, food-by-products, algae and microalgae: a review[END_REF], allows their use in food and pharmaceutical industries. Many microalgae are even able to synthesize antimicrobial and antiviral molecules which increases the interest of pharmaceutical industries [START_REF] Borowitzka | Vitamins and fine chemicals from micro-algae[END_REF][START_REF] Xue | Molecular species composition of glycolipids from Spirulina platensis[END_REF][START_REF] Supamattaya | Effect of a Dunaliella extract on growth performance, health condition, immune response and disease resistance in black tiger shrimp (Penaeus monodon)[END_REF]. Thanks to its content in phycocyanin, a water-soluble active compound, and thanks to its high protein content, Spirulina platensis is one of the microalgae with the greatest potential [START_REF] Chen | Purification and immunomodulating activity of C-phycocyanin from Spirulina platensis cultured using power plant flue gas[END_REF][START_REF] Coca | Protein production in Spirulina platensis biomass using beet vinasse-supplemented culture media, Food Bioprod[END_REF]. Spirulina platensis ranks first in the current global market with about 3000 tons of dry matter produced per year and is mainly cultivated in China, France, India, Japan, Myanmar, and USA. This microalga is used as a food supplement for human and animal nutrition, or as raw materials for cosmetic products. Furthermore, some works have been focussing on its beneficial impacts on cancer prevention [START_REF] Subhashini | Molecular mechanisms in C-Phycocyanin induced apoptosis in human chronic myeloid leukemia cell line-K562[END_REF].

The extraction of compounds of interest from dry seaweeds or microalgae is usually performed using organic solvents such as n-hexane but such widely-used solvents have major drawbacks: toxicity, flammability and low selectivity.

One alternative to avoid the use of such toxic and unsecured solvents to extract bio-oil or other compounds of interest is to carry out extraction using supercritical carbon dioxide (CO 2 ) as solvent. This solvent is Generally Recognized As Safe (GRAS) -non toxic, non-flammable -and may be rather selective. This selectivity is obtained by varying pressure and temperature. The separation step to recover the target product is done using simple depressurization since CO 2 is gaseous at ambient pressure. The depressurization can be multi-staged allowing a fractioning of the extracted compounds based on their solubility variation with density. The extract yield, which depends on experimental conditions, can be similar to those obtained through extraction processes using organic solvents and can be high for quite a short extraction time [START_REF] Crampon | Supercritical carbon dioxide extraction of molecules of interest from microalgae and seaweeds[END_REF]. Moreover, the low critical temperature of CO 2 (304.21 K/31.06 • C) permits its use for thermolabile compound extractions. Lastly, its critical pressure (7.38 MPa) is quite low.

Supercritical CO 2 solubilizes non polar compounds as neutral lipids (triglycerides, free fatty acids. . .); when the compounds of interest are not soluble, the solvent power can be increased using safe and polar modifiers like ethanol or water.

This technology is well-known today and is considered as a green process. On an industrial scale, the major part of the CO 2 is recycled therefore decreasing the consumption per extracted mass. In summary, the use of supercritical CO 2 instead of organic solvents would provide a cleaner and more compact extraction process. For the last four decades, a significant number of industrial extraction plants using supercritical fluids have been built, attesting that this technology is economically viable for a large number of applications.

Some works in literature from 1995 to nowadays have dealt with the extraction of compounds of interest from microalgae using supercritical CO 2 . In 2011 Crampon et al. [START_REF] Crampon | Supercritical carbon dioxide extraction of molecules of interest from microalgae and seaweeds[END_REF] published a review reporting 80 articles about this subject: the supercritical CO 2 extraction of neutral lipids and other high-added value compounds from seaweeds and microalgae was described, as well as the operating conditions and pre-treatments associated. As also mentioned in literature, supercritical CO 2 extracts specifically the neutral lipids contained in microalgae; the extracted oil is free of phospholipids. This point is of great interest since the presence of phospholipids in lipid extracts implies an additional degumming step to remove them either for food and health, or energy applications. Another important point to highlight is the possibility to apply this technique on wet biomass containing water content up to 23 wt% [START_REF] Crampon | Influence of pretreatment on supercritical CO2 extraction from Nannochloropsis oculata[END_REF].

Many articles particularly have focused on the extraction of compounds of interest from Spirulina (Arthrospira) platensis or maxima. Supercritical CO 2 extraction allows the extraction of neutral lipids [START_REF] Hu | Supercritical carbon dioxide extraction of Spirulina platensis component and removing the stench[END_REF][START_REF] Canela | Supercritical fluid extraction of fatty acids and carotenoids from the microalgae Spirulina maxima[END_REF] and particularly ␥-linolenic acid (GLA) which has an important role in human metabolism and is used as a dietary supplement [START_REF] Mendes | Supercritical carbon dioxide extraction of compounds with pharmaceutical importance from microalgae[END_REF][START_REF] Mendes | Supercritical CO2 extraction of ␥-linolenic acid (GLA) from the cyanobacterium Arthrospira (Spirulina) maxima: experiments and modeling[END_REF][START_REF] Mendes | Supercritical CO2 extraction of ␥-linolenic acid and other lipids from Arthrospira (Spirulina) maxima: comparison with organic solvent extraction[END_REF][START_REF] Sajilata | Supercritical CO2 extraction of ␥-linolenic acid (GLA) from Spirulina platensis ARM 740 using response surface methodology[END_REF]. Indeed, thanks to the GLA, the human body is capable of producing dihomo-␥-linolenic acid which is a very important constituent of the phospholipids of the cell membrane. Some articles also mentioned the supercritical CO 2 extraction of carotenoids [START_REF] Canela | Supercritical fluid extraction of fatty acids and carotenoids from the microalgae Spirulina maxima[END_REF] and mainly ␤-carotene [START_REF] Mendiola | Screening of functional compounds in supercritical fluid extracts from Spirulina platensis[END_REF][START_REF] Wang | Antioxidant activity of Spirulina platensis extracts by supercritical carbon dioxide extraction[END_REF] as well as tocopherols [START_REF] Wang | Antioxidant activity of Spirulina platensis extracts by supercritical carbon dioxide extraction[END_REF][START_REF] Mendiola | Enrichment of vitamin E from Spirulina platensis microalga by SFE[END_REF].

Concerning GLA extraction, pure supercritical CO 2 extraction yields are of the same order of magnitude than those obtained with n-hexane. Nevertheless, the extraction of GLA is enhanced by an addition of cosolvent (ethanol) [START_REF] Mendes | Supercritical CO2 extraction of ␥-linolenic acid and other lipids from Arthrospira (Spirulina) maxima: comparison with organic solvent extraction[END_REF].

The aim of this work was to extract a bio-oil predominantly containing neutral lipids and antioxidants from enriched Spirulina platensis using pure supercritical CO 2 . In a first part, a laboratory scale study (10 g per batch) of the influence of operating parameters: pressure, temperature, CO 2 flow rate and fill rate of the autoclave on extraction yields and kinetics and on oil composition, is proposed. An experimental design focusing on the influence of pressure, temperature and CO 2 /microalgae mass ratio is then presented and discussed. Finally, larger scale experiments with 1 kg and 50 kg of dry matter per batch were carried out corresponding to scale factors of 100 and 5000, respectively. To our knowledge, such a change of scale has never been discussed in the literature on supercritical CO 2 extraction from microalgae.

Materials and methods

Chemicals and microalgae

Carbon dioxide was supplied by Air Liquide Méditerranée (France) with a purity of 99.7%.

All reagents were AR grade (Carlo Erba, France). Spirulina platensis microalgae were grown, harvested and supplied by AlgoSource SAS (Saint-Nazaire, France). Microalgae were enriched in neutral lipids by a production under nitrogen limitation. The total lipid content was comprised between 10 and 16 wt% of dry matter (supplier data) depending on the season and the cultivation method. After the harvest, the microalga suspension was centrifuged (Westfalia separator, France). The raw material thus recovered looked like a paste that needed to be dried since the moisture content remained at about 40%. This paste was then submitted to an extrusion in a 5 mm cylinder under a horizontal air flow at 308 K. Indeed, the supercritical CO 2 extraction kinetics of compounds of interest from microalgae are known to be enhanced when the initial biomass is dried under air flow in comparison with other drying methods like freeze-drying or spray-drying [START_REF] Crampon | Influence of pretreatment on supercritical CO2 extraction from Nannochloropsis oculata[END_REF]. The resulting water content of dry biomass was about 5 wt%. Microalgae were supplied in the form of flakes and then frozen to limit lipid degradation.

At laboratory scale, the received microalga biomass was stored in a freezer at 255 K under an inert atmosphere. Before laboratory scale extractions, in order to increase extraction kinetics, microalgae were crushed using a cutting mill and then they were sieved. A fraction of the particles with an average size of less than 0.160 mm was isolated. After the extraction, the extracts (mainly composed of triglycerides) were recovered in n-hexane under an inert atmosphere in order to avoid their degradation into free fatty acids.

However, the storage conditions used at laboratory scale could not be applied at pilot scale because of practical difficulties. The microalgae were kept instead at room temperature and extracts were stored in closed recipients in a freezer.

Supercritical CO 2 extraction

Experiments were performed at laboratory and at pilot scales with different set-ups. Reproducibility of results was evaluated, extraction curves were plotted, and the influence of operating conditions were studied.

Experimental set-ups

The set-up used for the extractions at laboratory scale (Separex-4219) was supplied by Separex (Champigneulles, France). This apparatus allowed work to be done with three autoclaves (5, 10 and 20 cm 3 ) corresponding to batches comprised between 2 and 13 g of dry biomass. The maximum working pressure and fluid flow rate were 45 MPa and 1 kg h -1 .

A classical extraction was conducted as follows (Fig. 1): the autoclave was filled with the dry microalgae powder and heated until the desired temperature was reached. CO 2 was cooled by a cryogenic bath at 277 K, filtered and pumped into the extractor until the working pressure was reached. The pressure was controlled by a pressure gauge. Then the expansion valve was opened. A flow of supercritical CO 2 went through the microalga powder at constant pressure, temperature and flow rate during a predefined duration corresponding to the extraction duration. After passing through the extractor, the CO 2 was expanded through the expansion valve. The CO 2 became gaseous and extracted compounds were collected in a collecting container. The CO 2 flow rate was measured by a gas flow meter placed at the end of the extraction line. In general, the whole extraction curve was plotted and used to identify if there was a mass transfer limitation by diffusion. At laboratory scale, the low mass of biomass introduced in the autoclave generates small amounts of extract difficult to recover. As a consequence, extracts were collected in pure n-hexane and stored in a CO 2 atmosphere at 255 K to avoid the hydrolysis of triglycerides.

Some other extractions were performed in autoclaves of 2L and 150L (HITEX, Vannes, France) corresponding to batches of 1 and 50 kg, respectively. The maximum pressure of this set-up was 29 MPa. At pilot scales, the amount of recovered extract was important and didn't require the dilution in n-hexane. It should be noted that at larger scales (batches of 1 kg and more), CO 2 was recycled.

Accuracy of measurements and reproducibility

The extraction performance was estimated relative to the mass losses of the extraction autoclave (Eq. ( 1)) or relative to the extraction yields (Eq. ( 2)).

Mass loss (%) =

initial mass(g)-mass after extraction(g) initial mass(g) × 100 (1)

Neutral lipid yield (%) = neutral lipid extracted(g) neutral lipid content(g) × 100

(2)

The accuracy on the gravimetric measurements for the estimation of the mass loss was in the order of 10 -3 g. The uncertainties on mass losses were of 1.7 10 -3 %. Pressure, temperature and CO 2 flow rate were read with an accuracy of 0.5 MPa, 1 K, and 7 10 -3 kg h -1 .

The reproducibility of extraction experiments was tested at three different operating conditions. Each experiment was reproduced 2 or 3 times during an extraction time of 180 min. Reproducibility was estimated from the average deviation.

Table 1 gives the results of the reproducibility experiments. It shows that whatever the operating conditions, extractions were reproducible. The reproducibility on the weight loss of the vessel was less than 0.3%.

Analytical methods

2.3.1. Analysis of raw materials 2.3.1.1. Water content of dried microalgae. The water content of the raw material was determined as follows: 5 g of microalgae were placed in an oven Memmert (Schwabach, Germany) at 378 K until the sample mass remained constant. The water content was determined by the difference between the mass of microalgae before and after drying divided by the initial mass of microalgae.

Analysis of lipids.

The qualitative composition of crude oils from microalgae was determined first by a Bligh and Dyer extraction [START_REF] Bligh | A rapid method for total lipid extraction and purification[END_REF] and then by gas chromatography coupled with mass spectrometry (GC/MS).

2.3.1.3. Fatty acid methyl ester (FAME) profiles. In order to identify and quantify the fatty acids present in the oil, lipids were methylated using the Morrison and Smith method [START_REF] Morrison | Preparation of fatty acid methyl esters and dimethylacetals from lipids with boron fluoride-methanol[END_REF]. The methylated fatty acids were purified by Thin Layer Chromatography (Adsorbil plus 1-250 mm -Hard Layer-20 × 20 cm -Alltech) and were analysed using Gas Chromatography (Autosystem XL, Perkin Elmer, France) with a polar Famewax Column (Restek, France).

Extract analysis 2.3.2.1. Phosphorus determination.

The samples were first mineralized. The amount of phosphorus was then determined by ICP (Inductively Coupled Plasma) coupled with mass spectrometry. The results were given in ppm by weight, parts per million (mg phosphorus per kg of oil).

Dry fraction.

The principle of this analysis is the same as the one used for determining the moisture content of microalgae. The result was in that case expressed relative to the solid and not to water.

Determination of lipid classes.

The separation and quantification of lipid classes were performed by gas chromatography/flame ionization detector with an Iatroscan New MK5 (Iatron Laboratories Inc., Japan) with Chromarod S III quartz rods.

Antioxidant analysis.

The identification and determination of the content of antioxidants (chlorophylls a and b and ␤-carotene) contained in the extracted oil was determined by UV-vis spectrophotometry using the absorbance value measured in a Biochrom Libra S4 spectrophotometer (Biochrom, Cambridge, UK). The wavelength used were 653 and 666 nm for chlorophyll, and 450 nm for ␤-carotene. A calibration curve using standards was plotted to determine the pigment concentration in extracts. The straight calibration curve of absorbance versus pigment concentration (mg ml -1 ) was reliant on the Beer-Lambert law.

Experimental design

An experimental design combined with Response Surface Methodology (RSM) using Nemrodw software (LPRAI, Marseille, France) was established in order to study the influence of three operating parameters upon extraction performance: pressure, temperature and CO 2 /microalgae mass ratio. The operating domain was chosen as follows: pressure varied from 28 to 46 MPa, temperature from 318 to 338 K and CO 2 /microalgae mass ratio from 80 to 200.

The three factors were chosen for entry values of the experimental design and for each, two levels at the extremity were considered. A factorial design composed of 2 3 experiments was performed. X 1 , X 2 and X 3 were the temperature, pressure, and CO 2 /microalgae mass ratio, respectively. Table 2 shows the factors and levels chosen for the experimental design. The response or the output of the experimental design, noted as Y, was the mass loss in the autoclave. The different operating conditions of the experiments carried out in this study are presented in Table 3. The factorial design was composed of 8 possible combinations between the two levels of the factors. It was deduced from an Hadamard matrix to which an experiment at the medium level was added. 2 experiments were repeated (experiments 5 and 6, and experiments 8, 9, and 10) in order to quantify the error. Second-order response surface models were used to express the variation of oil recovery (Y) as a function of the independent variables X 1 , X 2 and X 3 (Eq. ( 3)):

Manuscrit d'auteur / Author manuscript

Y (%) = b 0 + b 1 X 1 + b 2 X 2 + b 3 X 3 + b 12 X 1 X 2 + b 13 X 1 X 3 + b 23 X 2 X 3 + b 123 X 1 X 2 X 3 (3)
where Y represented the response variable, b 0 was a constant. b i , b ij and b ijk were the linear and interactive factors. It is a degree 3 synergistic model classical for this type of matrix. The quadratic coefficients were imposed to be null. The other coefficients of the model were determined by using Nemrodw software (LPRAI, Marseille, France). The agreement of model fit was evaluated using the determination coefficient R 2 .

Results and discussions

Preliminary experiments were conducted before the experimental design in order to delimit the temperature and pressure domain of study.

Qualitative analysis of lipids contained in dry microalgal biomass before supercritical CO 2 extraction

After the air flow drying the microalgal biomass was characterized. The water content was estimated at about 5 wt%.

The lipids contained in the microalgal biomass before supercritical CO 2 extraction were also analysed. The lipids were extracted by the Bligh and Dyer method [START_REF] Bligh | A rapid method for total lipid extraction and purification[END_REF] and separated by Gas Chromatography. Table 4 shows the lipid profile of oil contained in a sample of microalgal biomass before supercritical extraction. For all analysed samples, the mean lipidic content including polar and non-polar lipids varied between 10 and 16% which corresponds to the supplier data.

The original microalgal oil was rich in saturated fatty acids from C16 up to C18. We could note a non-negligible amount of ␥-linolenic acid (GLA) (C18:3).

Extraction curves at different operating conditions

Extraction curves were plotted at several operating conditions in order to i) estimate the influence of operating parameters on extraction yields and kinetics, ii) accumulate data to feed a mathematical model [START_REF] Mouahid | Supercritical CO2 extraction of neutral lipids from microalgae: experiments and modelling[END_REF]. Instead of plotting extraction curves versus time, a most commonly used parameter was chosen: the CO 2 mass flow rate-over-the mass of biomass.

Influence of fill rate

Fill rate should have an influence on mass transfer between biomass and supercritical CO 2 , and then on extraction kinetics. In order to evaluate such influence, the extractions were conducted with 9.00, 10.70 and 13.25 g in an autoclave of 20 mL corresponding to fill rates of 68, 81, and 100%, respectively. The operating conditions were fixed at a pressure of 40 MPa, temperature of 333 K, CO 2 flow rate of 0.37 kg h -1 . Fig. 2 shows the extraction curves obtained for each filling of the vessel. Filling the autoclave with 68 and 81% of biomass led to similar results. However, the extraction curves showed that when the powder is packed, the mass transfer is disadvantaged probably due to preferential paths. To obtain a high quantity of extract, it is advised to work with a vessel filled up to 80%.

Influence of pressure

In order to evaluate the influence of pressure on extraction kinetics and yields, extraction curves were plotted (Fig. 3) under several pressures: 10, 20, and 30 MPa. Each curve was obtained at a fill rate of 68%, 333 K, with a CO 2 flow rate of 0.45 kg h -1 .

Fig. 3 shows that pressure had a positive influence on yields as the solubility of compounds in supercritical CO 2 generally increases when the pressure increases. Working under too low pressures may drastically decrease the solubility of compounds in the solvent and finally decrease yields for a given extraction duration. The extraction curves seemed to be limited by the diffusion phenomenon but pressure had a positive impact on kinetics. Working under high pressure led to more rapid extractions, and then with a lower consumption of CO 2 for a given extraction duration.

Influence of temperature

The temperature effect was studied from 313 to 333 K under pressures of 10 and 20 MPa. Figs. 4 and5 show the extraction curves obtained.

The retrograde behavior is well illustrated by Figs. 4 and5. The retrograde solubility is characteristic to supercritical fluids and is explained by the complex influence of temperature on solubility because of the competitive effects of two phenomena: the vapor pressure of the solute which increases when temperature increases, and the supercritical solvent power which decreases when temperature increases while its density decreases. Depending on pressure conditions, one effect is predominant over the other. At high pressure, when the fluid is quite incompressible, solubility increases thereby following the increase in vapor pressure of the solute. At low pressure, solubility decreases when temperature increases because of the predominant effect of the decrease in density. Figs. 4 and5 thus show that under 10 MPa, mass losses obtained at 313 K were higher than those obtained at 333 K whereas under 20 MPa, the influence of temperature was the opposite. The inversion pressure is thus understood between 10 and 20 MPa. It could be concluded that above 20 MPa, an increase in temperature would have a positive effect on extraction yield.

Experimental design for response surface methodology (RSM)

In the present work, the extractions were performed at temperatures in the range of 318-338 K, pressures from 28 to 46 MPa and CO 2 flow rates between 0.32 and 0.81 kg h -1 corresponding to CO 2 /microalgae mass ratios from 80 to 200. These chosen parameters have a great impact on extraction performance: the first two parameters influence the solubility of extracted compounds in the supercritical CO 2 as well as the mass transfer by diffusion whereas the last parameter is important to deduce the optimum amount of solvent to be used for a given amount of biomass. It is therefore connected to the cost of the operation. This experimental domain was determined based on the preliminary experiments and literature. The pressure was chosen above 20 MPa in order to avoid the retrograde solubility area and enhance mass losses as illustrated in Figs. 345.

For all experiments, an extraction duration of 90 min was fixed. The microalgae mass was on average 6 g in an extractor of 10 mL corresponding to an average fill rate of 80%. Table 5 gives the mass loss obtained for each experiment. The obtained mass loss covered a large range of values of 4.29-16.11%. As expected, the highest mass loss was reached for experiment 11 at the highest studied temperature, pressure, and CO 2 /microalgae mass ratio, i.e. 338 K, 46 MPa, and 200, respectively. Experimental oil yields were used to determine the coefficients of the response surface equation (Eq. ( 3)). Estimated coefficients are given in Table 6. The most significant factor is X 2 , i.e. the pressure.

Manuscrit d'auteur / Author manuscript

The following polynomial equation (Eq. ( 4)) correctly represents the experimental data (R 2 = 0.999). The calculated mass loss and experimental data are compared in Table 7.

Y (%) = 9.247 + 1.307X 1 + 3.267X 2 + 1.232X 3 + 0.483X 1 X 2 + 0.332X 1 X 3 + 0.108X 2 X 3 + 0.102X 1 X 2 X 3 (4)
Fig. 6(a) and (b) shows the response surfaces illustrating the influence of pressure and temperature, and temperature and CO 2 /microalgae mass ratio on mass losses, respectively. In the experimental field of study, the most influent parameter on extraction mass loss was the pressure while the least influent was the CO 2 flow rate. The evolution of extracted yield with each parameter was as described in literature [START_REF] Crampon | Supercritical carbon dioxide extraction of molecules of interest from microalgae and seaweeds[END_REF]: the solubility of neutral lipids in supercritical CO 2 increases when the pressure increases. From 28 up to 46 MPa the influence of temperature on mass losses was confirmed from the previous measurements: an increase in temperature increases the solubility of neutral lipids in supercritical CO 2 .

The microalgal biomass lipidic content including polar and non-polar lipids is on average 16% (supplier data), and using supercritical CO 2 it was possible to extract up to 16% of the initial biomass. Nevertheless, it is known that polar lipids are not extracted by supercritical CO 2 [START_REF] Crampon | Supercritical carbon dioxide extraction of molecules of interest from microalgae and seaweeds[END_REF][START_REF] Crampon | Influence of pretreatment on supercritical CO2 extraction from Nannochloropsis oculata[END_REF], so these high mass losses can be explained The brown pigmentation of extracts is mainly due to the presence of chlorophylls and carotenoids. Indeed, under certain operating conditions, supercritical CO 2 also dissolved these antioxidants with the oil and their solubility increased when the pressure increased, as for neutral lipids.

The ␤-carotene and chlorophyll a and b contents were determined using UV-vis spectrophotometry for samples of oil obtained at different operating conditions. Table 8 relates the operating conditions and gives the corresponding antioxidant contents. All extractions were performed with a CO 2 flow rate of 0.4 kg h -1 and a particle size less than 0.160 mm.

As expected, antioxidant contents were higher when extraction was performed under the highest pressure and temperature. However, the extraction of antioxidants seemed to be strongly influenced by temperature. Indeed, the content of antioxidants was more than doubled when the temperature was increased from 313 K to 333 K. A variation of pressure did not have a significant effect. Indeed, working under 10 or 40 MPa led to the same composition.

Larger scale extractions

Larger scale experiments were conducted with microalgae containing a neutral lipid content of 12% with an average particle size of 0.5 mm.

A first extraction was performed in an autoclave of 2L for a batch of 1 kg of dry biomass corresponding to a fill rate on an average of 75%. Extraction was carried out under 29 MPa, 333 K and a CO 2 flow rate of 8.5 kg h -1 . One batch was carried out without any adjuvant and another one with cellulose in view to increase the mass transfer between supercritical CO 2 and biomass. Fig. 7 gives the extraction curves obtained and shows that the transfer was rapidly limited by diffusion. Moreover, the presence or not of adjuvant did not have a significant influence on the extraction performance.

Another experiment was performed in an autoclave of 150L for a batch of 50 kg (average fill rate of 50%). Extraction was performed under 29 MPa, 333 K and a CO 2 flow rate of 550 kg h -1 . Fig. 8 gives the extraction curve obtained. These two larger scale experiments correspond to scale factors of 100 and 5000, respectively. Table 9 compares the results obtained at lab-scale and at pilot scales. For both experiments (batch of 1 kg and batch of 50 kg), at a solvent/biomass mass ratio of 50, the mass loss obtained was close to 6% (Figs. 7 and8, respectively). At laboratory scale, the mass loss obtained under 30 MPa and 333 K for the same solvent-over feed ratio was 8.8% but for a particle size less than 160 m (Fig. 3). The mass losses obtained for the three different scales (laboratory, ×100, and ×5000) are thus of the same order of magnitude at the same solvent/biomass mass ratio. This feature was already observed under 50 and 85 MPa in previous works carried out on microalgae [START_REF] Crampon | Influence of pretreatment on supercritical CO2 extraction from Nannochloropsis oculata[END_REF].

Finally, the oils extracted at larger scales were analysed: phosphorus determination, dry extract and composition of oils were determined.

Phosphorus determination

The phosphorus content of oils extracted by supercritical CO 2 was less than 10 mg kg -1 (limit of detection of the analysis apparatus). This expected result is explained by the fact that supercritical CO 2 does not solubilize phospholipids.

Dry extract

The dry extract of extracted oils is about 96-99 wt%. One part of residual water (initial water content in the biomass was on an average of 5 wt%) is then extracted.

Composition of oils

Table 10 shows the lipid classes of the extract obtained with the batch of 50 kg under 29 MPa and at 333 K.

The analysed oil showed a high content in free fatty acids to the detriment of triglycerides. This feature has already been observed in previous works [START_REF] Crampon | Influence of pretreatment on supercritical CO2 extraction from Nannochloropsis oculata[END_REF]. Indeed, at laboratory scale, the low quantity of microalgal biomass is stored in a freezer under inert atmosphere, but this way of storing cannot be applied at larger scale. The batch of 50 kg of microalgal biomass was stored at ambient temperature potentially leading to the degradation of triglycerides in free fatty acids. Another explanation can be the substantial content of water in the extract, perhaps also leading to the hydrolysis of the extracted triglycerides in free fatty acids. However, this content in free fatty acids does not compromise the nutritional value of extracted oils.

Conclusions

The aim of this work was to perform supercritical CO 2 extraction from neutral lipid enriched Spirulina platensis. The influence of operating conditions: pressure, temperature, CO 2 flow rate, fill rate on extraction kinetics and/or yields, and on antioxidant contents was studied. As expected, the pressure is the most influent parameter on extraction yields and kinetics. The retrograde behavior was also well illustrated. The inversion pressure is understood between 10 and 20 MPa. Concerning the influence of operating parameters on antioxidant content, it appears that temperature is the most significant parameter. The higher the temperature, the higher the antioxidant content. Extractions at larger scale (scale factors of 100 and 5000) were conducted and led to encouraging results since the mass loss was of the same order of magnitude as at laboratory scale for an equivalent solvent-over-feed ratio. The collected data will be used to develop a mathematical model in order to predict extraction curves.

Fig. 1 .

 1 Fig. 1. Classical laboratory scale CO2 extraction apparatus.

  Version définitive du manuscrit publiée dans / Final version of the manuscript published in : The Journal of Supercritical Fluids (2017), vol. 119, p. 289-296, DOI: 10.1016/j.supflu.2016.10.006 Journal homepage : http://www.elsevier.com/locate/supflu 292 C. Crampon et al. / J. of Supercritical Fluids 119 (2017) 289-296

Fig. 2 .

 2 Fig. 2. Extraction curves obtained for several fill rates.

Fig. 3 .

 3 Fig. 3. Influence of pressure on extraction curves at 333 K.

Fig. 4 .

 4 Fig. 4. Influence of temperature under 10 MPa.

Fig. 5 .

 5 Fig. 5. Influence of temperature under 20 MPa.

  Version définitive du manuscrit publiée dans / Final version of the manuscript published in : The Journal of Supercritical Fluids (2017), vol. 119, p. 289-296, DOI: 10.1016/j.supflu.2016.10.006 Journal homepage : http://www.elsevier.com/locate/supflu 294 C. Crampon et al. / J. of Supercritical Fluids 119 (2017) 289-296

Fig. 6 .

 6 Fig. 6. Response surfaces illustrating the influence of pressure, temperature on mass loss (a) and temperature and CO2/microalgae mass ratio on mass loss (b).

Fig. 7 .Fig. 8 .

 78 Fig. 7. Extraction curves obtained with a batch of 1 kg of dry biomass without any adjuvant and with cellulose at 29 MPa, 333 K and a CO2 flow rate of 8.5 kg h -1 .

  Manuscrit d'auteur / Author manuscriptVersion définitive du manuscrit publiée dans / Final version of the manuscript published in : The Journal of Supercritical Fluids (2017), vol. 119, p. 289-296, DOI: 10.1016/j.supflu.2016.10.006 Journal homepage : http://www.elsevier.com/locate/supflu 296 C. Crampon et al. / J. of Supercritical Fluids 119 (2017) 289-296

Table 1

 1 Reproducibility of extraction experiments.

	1	11.7	11.6 ± 0.3
	2	11.3
	3	11.9

Experiment

Mass loss/% Average/% T = 328 K; P = 37 MPa; QCO2 = 0.562 kg h -1 1 8.8 8.9 ± 0.1 2 8.8 3 8.9 T = 318 K; P = 28 MPa; QCO2 = 0.791 kg h -1 1 6.1 6.2 ± 0.1 2 6.3 T = 318 K; P = 46 MPa; QCO2 = 0.867 kg h -1

Table 2

 2 Factors and levels studied for the experimental design.

	Parameter	Temperature T/K	Pressure P/MPa	CO2/microalgae mass ratio
	Factor	X1	X2	X3
	Maximum parameter value	338	46	200
	High level	(+1)	(+1)	(+1)
	Minimum parameter value	318	28	80
	Low level	(-1)	(-1)	(-1)

Table 3

 3 Operating extraction conditions carried out.

	Experiment number	X1	X2	X3
		T/K	P/MPa	CO2-over-feed ratio
	1	318	28	80
	2	338	28	80
	3	318	46	80
	4	338	46	80
	5	318	28	200
	6	318	28	200
	7	338	28	200
	8	318	46	200
	9	318	46	200
	10	318	46	200
	11	338	46	200
	12	328	37	140

Table 4

 4 Lipid profile of microalgae before supercritical CO2 extraction.

	Fatty acids	Concentration of	Relative
		fatty acids/g g -1	proportions/%
		dry microalgae)
	Saturated (SFA)
	C8	78	0.07
	C10	27	0.02
	C12	27	0.02
	C14	515	0.46
	C16	70860	63.48
	C18	1468	1.32
	Mono-unsaturated (MUFA)
	C16:1	8955	8.02
	C18:1n-9	3274	2.93
	C18:1 n-7	588	0.53
	Poly-unsaturated (PUFA)
	C18:2	15321	13.73
	C18:3	10505	9.42
	SFAs	72975	65.38
	MUFAs	12817	11.48
	PUFAs	25826	23.14

Table 5

 5 Mass losses obtained at different operating conditions.

	Experiment number	T/K	P/MPa	CO2/microalgae mass ratio	Mass loss/%
	1			318	28	80			4.29
	2			338	28	80			5.48
	3			318	46	80			9.85
	4			338	46	80			12.56
	5	6		318 318	28 28	200	200		6.08	6.25
	7			338	28	200			8.19
	8	9	10	318318318	464646	200	200	200	11.9111.3411.66
	11			338	46	200			16.11
	12			328	37	140			9.00

Table 6

 6 Coefficients of the response surface equation.

	Coefficient	Value	Significance %
	b0	9.247	0.600
	b1	1.307	4.5
	b2	3.267	1.80
	b3	1.232	4.77
	b12	0.483	12.1
	b13	0.332	17.3
	b23	0.108	45.2
	b123	0.102	46.7

Table 7

 7 Predicted oil mass losses and deviations relative to experimental data.

	Experiment number	Y calc/%	Y exp/%	Deviation
	1	4.259	4.29	0.031
	2	5.449	5.48	0.031
	3	9.819	9.85	0.031
	4	12.529	12.56	0.031
	5	6.049	6.08	0.031
	6	8.159	8.19	0.031
	7	11.629	11.66	0.031
	8	16.079	16.11	0.031
	9	9.247	9.00	-0.247

Table 8

 8 Operating conditions of extraction and corresponding antioxidant contents (mg g -1 of extracted oil).

	Temperature/K Pressure/MPa ␤-carotene Chlorophyll a Chlorophyll b
	333	40	0.22	0.40	1.50
	313	40	0.09	0.20	0.65
	313	10	0.11	0.26	0.64
	mainly by the presence of water in the extracts and, to a much lesser
	extent, by the presence of antioxidants.
	3.4. Influence of pressure and temperature on antioxidant
	content in the extracted oil

Table 9

 9 Comparison between lab-scale and pilot plant results.

	Scale	Autoclave volume/L (mass dry biomass)	P/MPa	T/K	CO2 flow rate kg h -1	Mass loss at CO2-over-feed ratio = 50
	Lab-scale	20 mL (10 g)	30	333	0.5	8.8 a
	×100	2 L (1 kg)	29	333	8.5	6.2 b
	×5000	150 L (50 kg)	29	333	550	6 b

a d < 160 m. b d = 500 m.

Table 10

 10 Lipid classes of the extract obtained with the batch of 50 kg.

	Lipid classes	Hydrocarbons	FFAs a	Triglycerides	Cholesterol	Antioxidants
	Composition/wt%	1.55	86.10	2.41	2.42	7.51

a Free Fatty Acids.