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178 results on '"Villière A"'

1. Approche physico-chimique et sensorielle de l’oxydation des lipides en émulsions

Author

Villière Angélique and Genot Claude

Subjects
auto-oxidation, sunflower oil, unsaturated fatty acids, proteins, interface, metallic ions, Oils, fats, and waxes, TP670-699
Abstract

In many foods, lipids are present as droplets dispersed in an aqueous matrix. This dispersed state favours the oxidation of unsaturated lipids and leads to the degradation of sensory and nutritional qualities of the foods. This work was aimed to (1) investigate the influence on lipid oxidation of physico-chemical characteristics of the lipid-water interface and the aqueous phase in oil-in-water emulsions, designed to mimic dispersion state of lipids in foods, and (2) evaluate the sensory impact of the produced volatile compounds. Analysis of oxidation kinetics of emulsions varying by the nature of the emulsifier, the pH of the aqueous phase or the presence of a hydrosoluble metal chelator, demonstrates the key roles of metal location and reactivity of the emulsifying protein on oxidation initiation and on relative proportions of produced volatile compounds. Most of the volatiles exhibit an odour activity. Their formation lead to an early detection of an odour, which was characterised by cut grass, mushroom, oilseeds, rancid, crushed wood bug, deep fried and painty descriptors. The odour evolved with time as shown by changes in the relative contributions of the intensities of these descriptors. The contribution to the odour of volatile compounds perceived alone or as mixtures was investigated by olfactometric analysis. This work contradicts models based on the charge of the interface and suggests new interpretations for oxidative stability of lipids in complex dispersed media. It also gives practical information about formulation of foods containing unsaturated lipids.

Published
2006
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2. Comparison of Child and Adult Mastication of a Sticky Processed Cream Cheese and Simulation with a Masticator

Author

Coline Caille, Cécile Rannou, Angélique Villière, Clément Catanéo, Araceli Lagadec-Marquez, Julia Bechaux, and Carole Prost

Subjects
processed cream cheese, oral processing, child and adult mastication, masticator, image analysis, texture analysis, Chemical technology, TP1-1185
Abstract

An advantage of masticators is the calibration and possible standardization of intra- and inter-individual mastication variability. However, mastication of soft, sticky and melting products, such as processed cream cheeses, is challenging to reproduce with a masticator. The objectives of this work were, for the cheese studied: (1) to compare child and adult mastication and (2) to find in vitro parameters which best reproduce their in vivo chewing. Five parameters influencing mastication (mouth volume, quantity consumed, saliva volume, mastication time and number of tongue–palate compressions) were measured in 30 children (5–12 years old) and 30 adults (18–65 years old) and compared between the two populations. They were then transposed to a masticator (Oniris device patent). The initial cheese, a homogeneous white paste, was surface-colored to investigate its in-mouth destructuring. In vivo boli were collected at three chewing stages (33, 66 and 99% of mastication time) and in vitro boli were obtained by varying the number of tongue–palate compressions and the rotation speed. In vivo and in vitro boli were compared by both image and texture analysis. Child masticatory parameters were proportionally smaller than those of adults. The in vivo child boli were less homogeneous and harder than adult ones. Comparison of in vivo and in vitro bolus color and texture enabled the successful determination of two in vitro settings that closely represented the mastication of the two populations studied.

Published
2024
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3. Analysis of Volatile Compounds in Processed Cream Cheese Models for the Prediction of 'Fresh Cream' Aroma Perception

Author

Coline Caille, Mariem Boukraâ, Cécile Rannou, Angélique Villière, Clément Catanéo, Laurent Lethuaut, Araceli Lagadec-Marquez, Julia Bechaux, and Carole Prost

Subjects
processed cream cheese models, ĸ-carrageenan, agar-agar, volatile compound release, sensory analysis, rate all that apply, Organic chemistry, QD241-441
Abstract

Controlling flavor perception by analyzing volatile and taste compounds is a key challenge for food industries, as flavor is the result of a complex mix of components. Machine-learning methodologies are already used to predict odor perception, but they are used to a lesser extent to predict aroma perception. The objectives of this work were, for the processed cream cheese models studied, to (1) analyze the impact of the composition and process on the sensory perception and VOC release and (2) predict “fresh cream” aroma perception from the VOC characteristics. Sixteen processed cream cheese models were produced according to a three-factor experimental design: the texturing agent type (κ-carrageenan, agar-agar) and level and the heating time. A R-A-T-A test on 59 consumers was carried out to describe the sensory perception of the cheese models. VOC release from the cheese model boli during swallowing was investigated with an in vitro masticator (Oniris device patent), followed by HS-SPME-GC-(ToF)MS analysis. Regression trees and random forests were used to predict “fresh cream” aroma perception, i.e., one of the main drivers of liking of processed cheeses, from the VOC release during swallowing. Agar-agar cheese models were perceived as having a “milk” odor and favored the release of a greater number of VOCs; κ-carrageenan samples were perceived as having a “granular” and “brittle” texture and a “salty” and “sour” taste and displayed a VOC retention capacity. Heating induced firmer cheese models and promoted Maillard VOCs responsible for “cooked” and “chemical” aroma perceptions. Octa-3,5-dien-2-one and octane-2,3-dione were the two main VOCs that contributed positively to the “fresh cream” aroma perception. Thus, regression trees and random forests are powerful statistical tools to provide a first insight into predicting the aroma of cheese models based on VOC characteristics.

Published
2023
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5. Testing detectability, attractivity, hedonic specificity, extractability, and robustness of colostrum odor—Toward an olfactory bioassay for human neonates

Author

Klaey‐Tassone, Magali, primary, Soussignan, Robert, additional, Durand, Karine, additional, Roy, Sarah Le, additional, Damon, Fabrice, additional, Villière, Angélique, additional, Fillonneau, Catherine, additional, Prost, Carole, additional, Patris, Bruno, additional, Sagot, Paul, additional, and Schaal, Benoist, additional

Published
2024
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7. Comparative Investigation of Conventional and Innovative Headspace Extraction Methods to Explore the Volatile Content of Human Milk

Author

Sarah Le Roy, Catherine Fillonneau, Benoist Schaal, Carole Prost, Angélique Villière, and MILKODOR Consortium

Subjects
headspace extraction, solid phase micro-extraction, dynamic headspace analysis, human milk, volatilome, olfaction, Organic chemistry, QD241-441
Abstract

The odor of human milk induces search-like movements and oral activation in newborns, which increases their chances of taking advantage of milk intake and benefits. However, the underlying volatile fraction of human milk remains understudied. This study aimed to devise a simple method to extract a wide range of volatile compounds from small-volume human milk samples. Headspace solid phase micro-extraction (HS-SPME) with a Car/PDMS fiber and dynamic headspace extraction (D-HS) with a Tenax or a trilayer sorbent were tested because of their selective affinity for volatiles. Then, innovative variations of these methods were developed to combine their respective advantages in a one-step extraction: Static headspace with multiple SPME fibers (S-HS-MultiSPME), Dynamic headspace with multiple SPME fibers (D-HS-MultiSPME) and dynamic headspace with multiple SPME fibers and Tenax (D-HS-MultiSPME/Tenax). The extracts were analyzed by gas chromatography coupled with mass spectrometric and flame ionization detection. The relative performances of these methods were compared based on qualitative and semi-quantitative analyses of the chromatograms. The D-HS technique showed good sensitivity for most compounds, whereas HS-SPME favored the extraction of acids. The D-HS-MultiSPME/Tenax identified more than 60 compounds from human milk (some for the first time) and evidence of individual singularities. This method that can be applied to volatilome analysis of any biological fluid should further our understanding of human milk odor.

Published
2022
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11. Comparison of Child and Adult Mastication of a Sticky Processed Cream Cheese and Simulation with a Masticator.

Author

Caille, Coline, Rannou, Cécile, Villière, Angélique, Catanéo, Clément, Lagadec-Marquez, Araceli, Bechaux, Julia, and Prost, Carole

Subjects
CREAM cheese, TEXTURE analysis (Image processing), MASTICATION
Abstract

An advantage of masticators is the calibration and possible standardization of intra- and inter-individual mastication variability. However, mastication of soft, sticky and melting products, such as processed cream cheeses, is challenging to reproduce with a masticator. The objectives of this work were, for the cheese studied: (1) to compare child and adult mastication and (2) to find in vitro parameters which best reproduce their in vivo chewing. Five parameters influencing mastication (mouth volume, quantity consumed, saliva volume, mastication time and number of tongue–palate compressions) were measured in 30 children (5–12 years old) and 30 adults (18–65 years old) and compared between the two populations. They were then transposed to a masticator (Oniris device patent). The initial cheese, a homogeneous white paste, was surface-colored to investigate its in-mouth destructuring. In vivo boli were collected at three chewing stages (33, 66 and 99% of mastication time) and in vitro boli were obtained by varying the number of tongue–palate compressions and the rotation speed. In vivo and in vitro boli were compared by both image and texture analysis. Child masticatory parameters were proportionally smaller than those of adults. The in vivo child boli were less homogeneous and harder than adult ones. Comparison of in vivo and in vitro bolus color and texture enabled the successful determination of two in vitro settings that closely represented the mastication of the two populations studied. [ABSTRACT FROM AUTHOR]

Published
2024
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12. Comprehensive sensory and chemical data on the flavor of 16 red wines from two varieties: Sensory descriptive analysis, HS-SPME-GC-MS volatile compounds quantitative analysis, and odor-active compounds identification by HS-SPME-GC-MS-O

Author

Angélique Villière, Ronan Symoneaux, Alice Roche, Aïda Eslami, Nathalie Perrot, Yves Le Fur, Carole Prost, Philippe Courcoux, Evelyne Vigneau, Thierry Thomas-Danguin, and Laurence Guérin

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Science (General), Q1-390
Abstract

This paper describes data collected on 2 sets of 8 French red wines from two grape varieties: Pinot Noir (PN) and Cabernet Franc (CF). It provides, for the 16 wines, (i) sensory descriptive data obtained with a trained panel, (ii) volatile organic compounds (VOC) quantification data obtained by Headspace Solid Phase Micro-Extraction – Gas Chromatography – Mass Spectrometry (HS-SPME-GC-MS) and (iii) odor-active compounds identification by Headspace Solid Phase Micro-Extraction – Gas Chromatography – Mass Spectrometry – Olfactometry (HS-SPME-GC-MS-O). The raw data are hosted on an open-access research data repository [1]. Keywords: Wine, Descriptive sensory analysis, VOC, Olfactometry, GC-MS-O

Published
2019
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14. Rapid Heat Control for Thermoplastic Injection Tooling Using Lattice Structures as Heat Exchanger

Author

Arthur Lepoivre, Denis Edelin, Nicolas Baudin, Jérôme Soto, Maxime Villière, Benoît Redais, Didier Delaunay, and Vincent Sobotka

Subjects
Mechanics of Materials, Mechanical Engineering, General Materials Science
Abstract

Since conventional cooling systems with channels are not adequate to achieve a high aspect quality with a short cycle time, a better concept has to be used to control the fast variation of temperature in the mold, close to the injected part. Recently, with advanced manufacturing technologies like 3D-printing, rapid heat cycle molding are developing, using for example lattice structures as heat exchanger inside the mold. Our work proposes an experimental study to analyze the influence of four lattice structures that were specifically designed for this industrial application. An instrumented bench was developed at the laboratory scale, to test the thermal efficiency of the lattice. The material and geometry of the lattice structures were selected based on their thermomechanical properties and their efficiency as a heat-exchanger. The instrumentation of the bench consists in measuring the flow rate and the pressures in the fluid, and also the temperatures at various locations. This allows us to determine the performances of the lattice structure. The results show that the denser the lattice structure, the better, whether considering the mechanical resistance or the thermohydraulic performances. The key element to understand this phenomenon is the average velocity of the fluid flowing inside the lattice structure, accelerating when the porosity decreases and thus bringing a more intense heat exchange.

Published
2022
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22. SketchOscent: towards a knowledge-based model and interactive visualisation of the odour space

Author

Villière, Angélique, Fillonneau, Catherine, Prost, Carole, and Guillet, Fabrice

Subjects
human knowledge-based model, odour space representation, interactive visualisation, semantic representation, olfaction
Abstract

The representation of an odour space is a challenging issue for scientists and professionals working in odour-related activities. Many food and non-food sectors have created their own classification system and representations of the odorant space to manage objective communication about odours. More generally, many representations of the odorant space have been proposed. These initiatives turn the odorant space into easy-to-digest representations classifying hierarchically a restricted set of odorants in specific categories that provide a common frame of reference to professionals or sensory analysis judges. Nevertheless, the odour set is often restricted and these representations do not reflect the continuum of current odours which are not frozen in single section but on the contrary closely overlap. Thus, the related visualisations face with difficulties to represent such odour relationships. This work based on knowledge graphs from semantic web aims to propose a readable, interactive and publicly available representation of the aroma space, dedicated to olfactory and sensory practices. This representation relies on a large set of odours and takes into account overlapping categories. The knowledge-based model thus produced includes about 380 odours which are distributed in 20 main categories, which are decomposed into 117 subcategories. Seventy-seven odours and 26 categories obviously belong to multiple categories depending either on the multidimensional perception they are suggesting or on a conceptual positioning. The odour space is fully encoded into an rdf/owl Knowledge Graph, a semantic web standard language, which can be easily edited, stored, shared and requested. A hierarchical visual representation is derived to get a friendly and readable representation. The flexibility introduced by this representation enables to easily find out the relevant term(s) needed to describe an odorant perception. This approach, applied to food application, can be expanded to non-food sectors such as odour environment for example. SketchOscent is freely and publicly available at https://oniris-polytech.univ-nantes.io/sketchoscent.

Published
2022
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23. A new trend in aromatic sample preparation: Vacuum Assisted Sorbent Extraction (VASE) applied to pet food aroma analysis

Author

Le Guillas, Gautier, Marzin, Cécile, Hanaoka, Karine, Guilloux, Marion, Pétel, Cécile, Callejon, Laurence, Villière, Angélique, Prost, Carole, and Lethuaut, Laurent

Subjects
aroma compounds, sample preparation, vacuum extraction, extruded pet food, sorbent pen
Abstract

Aroma compounds extraction remains challenging to ensure a proper detection of volatile compounds when determination of the overall composition of the aroma is needed. Recently emerged, the Vacuum Assisted Sorbent Extraction (VASE) strives for a wider range of detection of the food aroma composition by combining high sensitivity traps and the benefits of a vacuumed environment. The present work aimed to compare abilities of three different available adsorbent phases combined to VASE technic on the extraction of volatile compounds from a meat flavoured extruded dry pet food. Two extraction times under vacuum (4h and 24h) were also compared for each adsorbent phase to highlight the importance of an appropriate extraction time. A higher extraction time enabled a significantly better volatile compounds detection (higher number of compounds and higher mean peak areas; p-value < 0.001) as well as an improvement of repeatability, reducing by more than half the coefficient of variation. A relevant impact of the adsorbent phase has also stood out (p-value < 0.001) and specificities were spotted. Whatever adsorbent type, a common pattern composed of 33 compounds, on a total of 100 identified, was extracted, probably due to the Tenax® TA contained by the three adsorbent phases. The Carboxen 1000 supplemented Tenax TA adsorbent trap favoured extraction of additional nitrogen and carbonyl compounds whereas the CarbopackTM X supplemented one ended up to be the less efficient except for low boiling point (

Published
2022
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26. SketchOscent : Towards a knowledge-based model and interactive visualization of the odour space

Author

Villière, Angélique, Fillonneau, Catherine, Prost, Carole, Guillet, Fabrice, Matrices Aliments Procédés Propriétés Structure - Sensoriel (GEPEA-MAPS2), Laboratoire de génie des procédés - environnement - agroalimentaire (GEPEA), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN)-Université de Nantes (UN)-Institut Universitaire de Technologie - Nantes (IUT Nantes), Université de Nantes (UN)-Institut Universitaire de Technologie Saint-Nazaire (IUT Saint-Nazaire), Université de Nantes (UN)-Institut Universitaire de Technologie - La Roche-sur-Yon (IUT La Roche-sur-Yon), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL), Laboratoire des Sciences du Numérique de Nantes (LS2N), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), and Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SDV.IDA]Life Sciences [q-bio]/Food engineering, [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM]
Abstract

International audience; The representation of an odour space is a challenging issue for scientists and professionals working in odour-related activities. Such an odour representation has to tackle the issues of a large amount of odors, dealing with subjectivity and uncertainty, a meaningful categorization for a knowledge map, and a readable and interactive visualization. Many representations of the odorant space have been proposed like : Field of odors[1], ScenTree [2], SensoryMap [3], Flavor network[4]. These initiatives turn the odorant space into easy-to-digest representations but the odour set is often restricted, and does not reflect the overlapping of categories. Thus, the related visualizations, face with difficulties to represent such odour relationships. This work based on knowledge graphs from semantic web aims to propose a readable, interactive and publicly available representationof the aroma space, relying on a large set of odors and on overlapping categories, dedicated to olfactory and sensory practices.

Published
2021
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27. Comparative Investigation of Conventional and Innovative Headspace Extraction Methods to Explore the Volatile Content of Human Milk.

Author

Le Roy, Sarah, Fillonneau, Catherine, Schaal, Benoist, Prost, Carole, and Villière, Angélique

Subjects
BREAST milk, SORBENT testing, GAS chromatography, FOOD aroma, ARCHAEOLOGICAL human remains
Abstract

The odor of human milk induces search-like movements and oral activation in newborns, which increases their chances of taking advantage of milk intake and benefits. However, the underlying volatile fraction of human milk remains understudied. This study aimed to devise a simple method to extract a wide range of volatile compounds from small-volume human milk samples. Headspace solid phase micro-extraction (HS-SPME) with a Car/PDMS fiber and dynamic headspace extraction (D-HS) with a Tenax or a trilayer sorbent were tested because of their selective affinity for volatiles. Then, innovative variations of these methods were developed to combine their respective advantages in a one-step extraction: Static headspace with multiple SPME fibers (S-HS-MultiSPME), Dynamic headspace with multiple SPME fibers (D-HS-MultiSPME) and dynamic headspace with multiple SPME fibers and Tenax (D-HS-MultiSPME/Tenax). The extracts were analyzed by gas chromatography coupled with mass spectrometric and flame ionization detection. The relative performances of these methods were compared based on qualitative and semi-quantitative analyses of the chromatograms. The D-HS technique showed good sensitivity for most compounds, whereas HS-SPME favored the extraction of acids. The D-HS-MultiSPME/Tenax identified more than 60 compounds from human milk (some for the first time) and evidence of individual singularities. This method that can be applied to volatilome analysis of any biological fluid should further our understanding of human milk odor. [ABSTRACT FROM AUTHOR]

Published
2022
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28. Comprehensive sensory and chemical data on the flavor of 16 red wines from two varieties: sensory descriptive analysis, HS-SPME-GC-MS volatile compounds quantitative analysis, and odor-active compounds identification by HS-SPME-GC-MS-O

Author

Villière, Angélique, Symoneaux, Ronan, Roche, David, Eslami, Aïda, Perrot, Nathalie, Le Fur, Yves, PROST, Carole, Courcoux, Philippe, Vigneau, Evelyne, Thomas-Danguin, Thierry, Guerin, Laurence, Matrices Aliments Procédés Propriétés Structure - Sensoriel (GEPEA-MAPS2), Laboratoire de génie des procédés - environnement - agroalimentaire (GEPEA), Institut Universitaire de Technologie - Nantes (IUT Nantes), Université de Nantes (UN)-Université de Nantes (UN)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-Institut Universitaire de Technologie Saint-Nazaire (IUT Saint-Nazaire), Université de Nantes (UN)-Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Universitaire de Technologie - La Roche-sur-Yon (IUT La Roche-sur-Yon), Université de Nantes (UN)-Institut Universitaire de Technologie - Nantes (IUT Nantes), Université de Nantes (UN), Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS), Laboratoire GRAPPE, Ecole Supérieure d'Agriculture (Groupe ESA), Centre des Sciences du Goût et de l'Alimentation [Dijon] (CSGA), Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Génie et Microbiologie des Procédés Alimentaires (GMPA), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Statistique, Sensométrie et Chimiométrie (StatSC), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS), Unite VINs Innovat Itineraires TERroirs & Acteurs, Institut Français de la Vigne et du Vin (IFV), These data were collected as part of the INNOVAROMA research program on wine, which was conducted with financial support from the regional councils of Pays de la Loire, Centre, Bretagne, and Bourgogne (DINOS-AGRALE-12-2011, DINOS-AGRALE-12-2012, PARI-DINOS-2013)., Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS), ONIRIS, USC 1381 StatSC Statistique, Sensométrie et Chimiométrie, and Institut National de la Recherche Agronomique (INRA)

Subjects
olfactometry, VOC, lcsh:Computer applications to medicine. Medical informatics, [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biomolecules [q-bio.BM], GC-MS-O, Chemistry, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, [SDV.IDA]Life Sciences [q-bio]/Food engineering, lcsh:R858-859.7, descriptive sensory analysis, wine, [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], lcsh:Science (General), [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, ComputingMilieux_MISCELLANEOUS, lcsh:Q1-390
Abstract

This paper describes data collected on 2 sets of 8 French red wines from two grape varieties: Pinot Noir (PN) and Cabernet Franc (CF). It provides, for the 16 wines, (i) sensory descriptive data obtained with a trained panel, (ii) volatile organic compounds (VOC) quantification data obtained by Headspace Solid Phase Micro-Extraction – Gas Chromatography – Mass Spectrometry (HS-SPME-GC-MS) and (iii) odor-active compounds identification by Headspace Solid Phase Micro-Extraction – Gas Chromatography – Mass Spectrometry – Olfactometry (HS-SPME-GC-MS-O). The raw data are hosted on an open-access research data repository [1]. Keywords: Wine, Descriptive sensory analysis, VOC, Olfactometry, GC-MS-O

Published
2019
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30. A methodology for characterization of volatile and aromatic profile of dry sourdough and their related bread

Author

Pétel, C, Onno, B., Villière, Angélique, Fillonneau, C., Rouillé, J, Prost, C., Ecole Nationale Vétérinaire Agroalimentaire et de l'Alimentation Nantes Atlantique (ONIRIS), Matrices Aliments Procédés Propriétés Structure - Sensoriel (GEPEA-MAPS2), Laboratoire de génie des procédés - environnement - agroalimentaire (GEPEA), Institut Universitaire de Technologie - Nantes (IUT Nantes), Université de Nantes (UN)-Université de Nantes (UN)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-Institut Universitaire de Technologie Saint-Nazaire (IUT Saint-Nazaire), Université de Nantes (UN)-Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Universitaire de Technologie - La Roche-sur-Yon (IUT La Roche-sur-Yon), Université de Nantes (UN)-Institut Universitaire de Technologie - Nantes (IUT Nantes), Université de Nantes (UN), Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS), Villière, Angélique, Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN)-Université de Nantes (UN)-Institut Universitaire de Technologie - Nantes (IUT Nantes), Université de Nantes (UN)-Institut Universitaire de Technologie Saint-Nazaire (IUT Saint-Nazaire), Université de Nantes (UN)-Institut Universitaire de Technologie - La Roche-sur-Yon (IUT La Roche-sur-Yon), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), and Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL)

Subjects
[SDV.AEN] Life Sciences [q-bio]/Food and Nutrition, [CHIM.ANAL] Chemical Sciences/Analytical chemistry, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, digestive, oral, and skin physiology, [SDV.IDA]Life Sciences [q-bio]/Food engineering, food and beverages, [SDV.IDA] Life Sciences [q-bio]/Food engineering, [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], [SDV.BBM.BC] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition
Abstract

International audience; Combine different type of analyses permit to obtain a stronger understanding of product aromatical characteristics. Differences in volatiles composition imply sensorial differences. We can notice that the differences in the malty and fermentative odors mentioned in the profile are also found in the GC-O results. But the contrary is also true so there is not a linear correlation between the product odor profile and the volatiles odors. That could be explain by the interaction phenomenon between volatiles and by the matrix effect. This methodology combined analytical and sensorial results in order to understand the odor formation during the sourdough and bakery process. Apply different analytical and sensorial techniques in order to understand the volatiles composition and the consequences on the aromatic profile of dry-sourdough (DSD) and the corresponding dry-sourdough-bread (DSD-bread). Objectives There is high differences in DSD and DSD-bread volatiles chemical class repartition. The DSD-bread is characterized by a major part of alcohols issue of the yeast fermentation, and the DSD is characterized by a major part of acids which are evaporated during bakery. The DSD volatiles composition is more equilibrated for an higher total area but less compounds than the DSD-bread. Sensorial characterization by odor-profile The profile sessions point out that-as for the GC-O session-there is some similarities between DSD and DSD-bread crumb odors and some differences. Indeed they present the same global intensity, "sweet" and "salt/fat" score but the DSD odor is more "acidic/fermentative" and more "malty" than the DSD-bread odor which is more complex. The GC-O sessions highlight more odorous zone for DSD than DSD-Bread. There is some similarity between DSD and DSD-bread, as the score of "animal, mould/hearthy, flowery and fruity", but also some differences as the DSD seems to be more "fermentative", more "sweet" and more "malty" than the DSD-bread which presents a more diversified profile. Panel formation 12 judges trained to the identification of odors and the use of 4 points intensity scale (0: none , 1: low , 2 : medium, 3: strong). Analysis of DSD and the corresponding DSD-bread. For the study of DSD-bread odor, the crumb and the crust were separately evaluated. Profile training and sessions 3 sessions of odor-description generation. 5 training session for the DSD and the DSD-bread. 3 profile sessions for DSD and DSD-bread. 4,5 g DSD or mixed DSD-bread vial 125 mL Stirring bar CAR/PDMS fiber (75 µm, 1cm)

Published
2017

31. How to predict the odor profile of wine from its chemical composition? In silico modelling using experts' knowledge, fuzzy logic and optimization

Author

Roche, Alice, Perrot, Nathalie, Chabin, Thomas, Villière, Angélique, Symoneaux, Ronan, Thomas-Danguin, Thierry, Centre des Sciences du Goût et de l'Alimentation [Dijon] (CSGA), Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Centre National de la Recherche Scientifique (CNRS), Université Bourgogne Franche-Comté [COMUE] (UBFC), Génie et Microbiologie des Procédés Alimentaires (GMPA), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, GEPEA-MAPS2, Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS), Ecole Supérieure d'Agriculture (Groupe ESA), Centre National de la Recherche Scientifique (CNRS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB), Matrices Aliments Procédés Propriétés Structure - Sensoriel (GEPEA-MAPS2), Laboratoire de génie des procédés - environnement - agroalimentaire (GEPEA), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN)-Université de Nantes (UN)-Institut Universitaire de Technologie - Nantes (IUT Nantes), Université de Nantes (UN)-Institut Universitaire de Technologie Saint-Nazaire (IUT Saint-Nazaire), Université de Nantes (UN)-Institut Universitaire de Technologie - La Roche-sur-Yon (IUT La Roche-sur-Yon), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL), Ecole Superieure d'Agriculture (ESA), Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS), ProdInra, Migration, and Villière, Angélique

Subjects
[CHIM.ANAL] Chemical Sciences/Analytical chemistry, sensory evaluation, [CHIM.ORGA]Chemical Sciences/Organic chemistry, [SDV.IDA] Life Sciences [q-bio]/Food engineering, [CHIM.ORGA] Chemical Sciences/Organic chemistry, fruity, woody, [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biomolecules [q-bio.BM], smoky, [SDV.AEN] Life Sciences [q-bio]/Food and Nutrition, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, [SDV.IDA]Life Sciences [q-bio]/Food engineering, [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], [SDV.BBM.BC] Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2017

32. Olfactometric process: new insights in automated acquisition and data treatment

Author

Villière, Angélique, Guillet, Fabrice, Prost, Carole, Matrices Aliments Procédés Propriétés Structure - Sensoriel (GEPEA-MAPS2), Laboratoire de génie des procédés - environnement - agroalimentaire (GEPEA), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN)-Université de Nantes (UN)-Institut Universitaire de Technologie - Nantes (IUT Nantes), Université de Nantes (UN)-Institut Universitaire de Technologie Saint-Nazaire (IUT Saint-Nazaire), Université de Nantes (UN)-Institut Universitaire de Technologie - La Roche-sur-Yon (IUT La Roche-sur-Yon), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL), Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS), Data User Knowledge (DUKe), Laboratoire des Sciences du Numérique de Nantes (LS2N), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)

Subjects
[INFO.INFO-DB]Computer Science [cs]/Databases [cs.DB], [CHIM.ANAL]Chemical Sciences/Analytical chemistry, [SDV.IDA]Life Sciences [q-bio]/Food engineering, [INFO.INFO-DS]Computer Science [cs]/Data Structures and Algorithms [cs.DS], [INFO]Computer Science [cs], [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition
Abstract

International audience; Olfactometry is a valuable methodology commonly used to investigate odorant active compounds in food aroma profiles. Considering the number of studies using this technique, little is done to improve olfactometric data acquisition, although it is essential for quality outcomes. Efforts were mainly done to automate recording of moment and duration of perceptions but their intensity and description are still often communicate orally, which disrupt judge’s breathing rhythm during analysis. Solutions that integrate intensity and description parameters recording, result in a multiple steps acquisition procedure, scarcely compatible with the transience of the perceptions evaluated during olfactometry experiment. This work aims to present an olfactometry dedicated software developed to simplify the user task, overcoming constraints and bias of existing systems, and associating data treatment capabilities. More specifically the WheelOscent software, coded with Java technologies, implements innovative components:- a data acquisition interface based on adaptable aroma wheels, which permits judges to characterize all parameters related to odors perceived, in a single and intuitive move (patented sensory system),- a data store, which stores collected data into adapted representation describing aroma wheels, experiments, products, judges, and aromagrams, - a data analysis interface, providing a direct and interactive visualization of data resulting from several processing such as data aggregation over judges. The WheelOstat module also provides a straight statistical comparison of aroma profiles.Assessment of the software was performed on food products with complex aroma such as wines or coffee. Providing a good usability for judges, it enables a precise aromatic characterization. Moreover, the statistical comparison module allows to point out singular features of products. Finally, judges take advantage of this wheel aroma presentation, already used for sensory characterization. This consistent presentation for olfactometric and sensory analysis facilitate approaches that attempt to determine contribution of compounds to an overall aroma and apprehend existing interactions.

Published
2018

33. Random forests: A machine learning methodology to highlight the volatile organic compounds involved in olfactory perception

Author

Ronan Symoneaux, Evelyne Vigneau, Philippe Courcoux, Laurence Guérin, Angélique Villière, Unité de Sensométrie et Chimiométrie, Institut National de la Recherche Agronomique (INRA)-École nationale d'ingénieurs des techniques des industries agricoles et alimentaires (ENITIAA), Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS), Laboratoire GRAPPE, Ecole Supérieure d'Agriculture (Groupe ESA), Matrices Aliments Procédés Propriétés Structure - Sensoriel (GEPEA-MAPS2), Laboratoire de génie des procédés - environnement - agroalimentaire (GEPEA), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN)-Université de Nantes (UN)-Institut Universitaire de Technologie - Nantes (IUT Nantes), Université de Nantes (UN)-Institut Universitaire de Technologie Saint-Nazaire (IUT Saint-Nazaire), Université de Nantes (UN)-Institut Universitaire de Technologie - La Roche-sur-Yon (IUT La Roche-sur-Yon), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL), Pays de la Loire region, Institut Universitaire de Technologie - Nantes (IUT Nantes), Université de Nantes (UN)-Université de Nantes (UN)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-Institut Universitaire de Technologie Saint-Nazaire (IUT Saint-Nazaire), Université de Nantes (UN)-Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Universitaire de Technologie - La Roche-sur-Yon (IUT La Roche-sur-Yon), Université de Nantes (UN)-Institut Universitaire de Technologie - Nantes (IUT Nantes), and Université de Nantes (UN)

Subjects
Computer science, [SDV]Life Sciences [q-bio], Wine, Machine learning, computer.software_genre, [SHS]Humanities and Social Sciences, Permutation, [SPI]Engineering Sciences [physics], 0404 agricultural biotechnology, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, Partial least squares regression, CART, [CHIM]Chemical Sciences, Volatile organic compounds, Nutrition and Dietetics, [SHS.STAT]Humanities and Social Sciences/Methods and statistics, Binary decision diagram, business.industry, Regression analysis, 04 agricultural and veterinary sciences, 040401 food science, Regression, Random forest, [STAT]Statistics [stat], Variable (computer science), Odor, Artificial intelligence, Olfactory perception, business, computer, [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, Food Science
Abstract

International audience; The purpose of this paper is to discuss the application of the Random Forest methodology to sensory analysis. A methodological point of view is mainly adopted to describe as simply as possible the construction of binary decision trees and, more precisely, Classification and Regression Trees (CART), as well as the generation of an ensemble of trees or, in other words, a Random Forest. The interest of the permutation accuracy criterion, as a measure of variable importance, is specifically emphasized as a way of identifying the most predictive variables and selecting a subset of these variables for parsimonious and efficient predictive models. A two-step procedure is proposed for choosing this subset of variables. The principle of the method is illustrated in a case study in which the aim was to better understand and predict the olfactory characteristics of red wines made of the Cabernet Franc grape variety, from their Volatile Organic Compound (VOC) content. For two main olfactory attributes, the bell pepper odor and the leather odor, it was possible to list the most important compounds and to highlight a very small number of compounds useful for estimating each of the olfactory attributes considered. For the latter, it was also observed that Random Forest models had a better predictive ability than Partial Least Squares (PLS) Regression models.

Published
2018
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34. Le projet de soin personnalisé comme anticipation dans la démarche palliative de la personne âgée démente en EHPAD

Author

Bancal-Villière, Mylène, Sorbonne Université - Faculté de Médecine (SU FM), Sorbonne Université (SU), Véronique Blanchet, and Yolaine Raffray

Subjects
Démence sénile, Établissements d'hébergement pour personnes âgées dépendantes, Prise en charge personnalisée du patient, Soins en phase terminale, [SDV.MHEP]Life Sciences [q-bio]/Human health and pathology
Abstract

Psychologue clinicienne en EHPAD, je propose la situation d'une résidente atteinte de démence évoluée et en fin de vie. Elle est accompagnée par son fils très présent, seul proche de son entourage.Lors d'une altération de son état de santé, l'absence de réévaluation de son projet personnalisé de soin a conduit à un manque d'anticipation pour clarifier les objectifs. Cela a produit une insécurité tant du côté du proche de la résidente que de l'équipe soignante. Avec pour conséquences : un transfert aux urgences dommageable pour la résidente, une inadaptation de l'équipe à la situation palliative et une souffrance pour tous, soignants, résidente et proche.Ce travail de recherche a confirmé la pertinence d'une réflexion pluridisciplinaire éthique par l'intermédiaire du projet personnalisé de soin incluant au plus près le respect des volontés du résident.

Published
2018

35. Thermal resistance evaluation in orthotropic single-lap joint composite materials

Author

DELAUNAY, Didier, Villière, Maxime, Laboratoire de Thermique et d’Energie de Nantes (LTeN), Ecole Polytechnique de l'Université de Nantes (Polytech Nantes), Université de Nantes (UN)-Université de Nantes (UN)-Centre National de la Recherche Scientifique (CNRS), and Ecole Polytechnique de l'Université de Nantes (EPUN)

Subjects
[PHYS.MECA.THER]Physics [physics]/Mechanics [physics]/Thermics [physics.class-ph], ComputingMilieux_MISCELLANEOUS
Abstract

National audience

Published
2018

36. Probe key-odorants in wine throught online GC/O recombination with olfactoscan

Author

Beno, Noëlle, Loison, Aurélie, Villière, Angélique, Le Fur, Yves, Thomas-Danguin, Thierry, Centre des Sciences du Goût et de l'Alimentation [Dijon] (CSGA), Centre National de la Recherche Scientifique (CNRS)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement-Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB), Université Bourgogne Franche-Comté [COMUE] (UBFC), Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS), and Projet INNOVAROMA

Subjects
[SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2018

39. Headspace solid phase microextration vs. dynamic headspace extraction to explore breast milk volatile fraction

Author

Le Roy, Sarah, Villière, Angélique, Fillonneau, Catherine, Prost, Carole, Matrices Aliments Procédés Propriétés Structure - Sensoriel (GEPEA-MAPS2), Laboratoire de génie des procédés - environnement - agroalimentaire (GEPEA), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN)-Université de Nantes (UN)-Institut Universitaire de Technologie - Nantes (IUT Nantes), Université de Nantes (UN)-Institut Universitaire de Technologie Saint-Nazaire (IUT Saint-Nazaire), Université de Nantes (UN)-Institut Universitaire de Technologie - La Roche-sur-Yon (IUT La Roche-sur-Yon), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL), Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS), ANR-15-CE21-0009,MILKODOR,Les composés odorants-clés du lait déclencheurs des réponses d'appétence alimentaire chez l'enfant nouveau-né(2015), Institut Universitaire de Technologie - Nantes (IUT Nantes), Université de Nantes (UN)-Université de Nantes (UN)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-Institut Universitaire de Technologie Saint-Nazaire (IUT Saint-Nazaire), Université de Nantes (UN)-Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Universitaire de Technologie - La Roche-sur-Yon (IUT La Roche-sur-Yon), Université de Nantes (UN)-Institut Universitaire de Technologie - Nantes (IUT Nantes), and Université de Nantes (UN)

Subjects
[CHIM.ANAL]Chemical Sciences/Analytical chemistry, [SDV]Life Sciences [q-bio], [SDV.IDA]Life Sciences [q-bio]/Food engineering, [CHIM]Chemical Sciences, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, ComputingMilieux_MISCELLANEOUS, [SHS]Humanities and Social Sciences
Abstract

International audience

Published
2018
Full Text
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40. Influence of cider-making process parameters on the odourant volatile composition of hard ciders

Author

Gaëlle Arvisenet, Thierry Serot, Rémi Bauduin, Angélique Villière, and Jean-Michel Le Quéré

Subjects
Pressing, Chemistry, business.industry, Pulp (paper), 010401 analytical chemistry, food and beverages, Biomass, 04 agricultural and veterinary sciences, engineering.material, 040401 food science, 01 natural sciences, 0104 chemical sciences, law.invention, 0404 agricultural biotechnology, Pilot plant, law, engineering, Brewing, Flame ionization detector, Fermentation, Gas chromatography, Food science, business, Food Science
Abstract

This study was performed to explore the relationships between some parameters of the French cider-making process and the odourant compounds of cider. Sixteen ciders were prepared on a pilot plant scale using experimental design and varying according to apple blends, pressing conditions, pre-fermentation clarification implementation and conditions, and biomass reduction during fermentation. Odourant compounds were extracted from final ciders by headspace solid-phase microextraction with a CAR/PDMS fibre, a method previously shown to provide extracts representative of the studied cider. Extracts were analysed by gas chromatography coupled with mass spectrometry and flame ionization detection. All of the parameters tested had at least a slight effect on the odourant composition and particularly on the esters, which bring fruity notes that are appreciated by consumers. Clarification and biomass reduction had a greater impact than apple blend and pressing conditions. This could be explained by the influence of the nitrogen content on fermentation rate and efficiency, which affects the production of secondary metabolites. Under the conditions tested, a juice obtained from a bitter blend of apples by a slow pressing of the pulp at low temperature, after 1 h of cuvage, clarified by keeving and fermented without biomass reduction, produced a cider with the highest quantity of esters. These results could help cider-makers enhance product quality according to consumer expectations. Copyright © 2015 The Institute of Brewing & Distilling

Published
2015
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41. HS-SPME vs DHS to explore breast milk volatile fraction

Author

Le Roy, Sarah, Villière, Angélique, Fillonneau, Catherine, Prost, Carole, Matrices Aliments Procédés Propriétés Structure - Sensoriel (GEPEA-MAPS2), Laboratoire de génie des procédés - environnement - agroalimentaire (GEPEA), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN)-Université de Nantes (UN)-Institut Universitaire de Technologie - Nantes (IUT Nantes), Université de Nantes (UN)-Institut Universitaire de Technologie Saint-Nazaire (IUT Saint-Nazaire), Université de Nantes (UN)-Institut Universitaire de Technologie - La Roche-sur-Yon (IUT La Roche-sur-Yon), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL), and Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)

Subjects
[CHIM.ANAL]Chemical Sciences/Analytical chemistry, High Energy Physics::Lattice, High Energy Physics::Phenomenology, [SDV.IDA]Life Sciences [q-bio]/Food engineering, High Energy Physics::Experiment, [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2017

42. Introduction of an innovative olfactometry-dedicated software using intuitive aroma wheel interface

Author

Villière, Angélique, Guillet, Fabrice, Le Roy, Sarah, Fillonneau, Catherine, Prost, Carole, Matrices Aliments Procédés Propriétés Structure - Sensoriel (GEPEA-MAPS2), Laboratoire de génie des procédés - environnement - agroalimentaire (GEPEA), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN)-Université de Nantes (UN)-Institut Universitaire de Technologie - Nantes (IUT Nantes), Université de Nantes (UN)-Institut Universitaire de Technologie Saint-Nazaire (IUT Saint-Nazaire), Université de Nantes (UN)-Institut Universitaire de Technologie - La Roche-sur-Yon (IUT La Roche-sur-Yon), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL), Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS), Laboratoire des Sciences du Numérique de Nantes (LS2N), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), and Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[CHIM.ANAL]Chemical Sciences/Analytical chemistry, [SDV.IDA]Life Sciences [q-bio]/Food engineering, [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2017

43. WheelOscent: presentation of an innovative olfactometry-dedicated software using intuitive aroma wheel interface

Author

Villière, Angélique, Guillet, Fabrice, Le Roy, Sarah, Fillonneau, Catherine, Prost, Carole, Matrices Aliments Procédés Propriétés Structure - Sensoriel (GEPEA-MAPS2), Laboratoire de génie des procédés - environnement - agroalimentaire (GEPEA), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN)-Université de Nantes (UN)-Institut Universitaire de Technologie - Nantes (IUT Nantes), Université de Nantes (UN)-Institut Universitaire de Technologie Saint-Nazaire (IUT Saint-Nazaire), Université de Nantes (UN)-Institut Universitaire de Technologie - La Roche-sur-Yon (IUT La Roche-sur-Yon), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL), Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS), Laboratoire des Sciences du Numérique de Nantes (LS2N), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), Flavour Science, actes du 15e Weurmann flavour research sympo-sium. 2018, Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)

Subjects
[SPI]Engineering Sciences [physics], High Energy Physics::Lattice, High Energy Physics::Phenomenology, High Energy Physics::Experiment, [SPI.GPROC]Engineering Sciences [physics]/Chemical and Process Engineering, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2017
Full Text
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44. WheelOscent : Presentation of an olfactometry-dedicated software using intuitive aroma wheel interface

Author

Villière, Angélique, Guillet, Fabrice, Le Roy, Sarah, Fillonneau, Catherine, Prost, Carole, Matrices Aliments Procédés Propriétés Structure - Sensoriel (GEPEA-MAPS2), Laboratoire de génie des procédés - environnement - agroalimentaire (GEPEA), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN)-Université de Nantes (UN)-Institut Universitaire de Technologie - Nantes (IUT Nantes), Université de Nantes (UN)-Institut Universitaire de Technologie Saint-Nazaire (IUT Saint-Nazaire), Université de Nantes (UN)-Institut Universitaire de Technologie - La Roche-sur-Yon (IUT La Roche-sur-Yon), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL), Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS), Laboratoire des Sciences du Numérique de Nantes (LS2N), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)

Subjects
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2017
Full Text
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45. In silico modelling to predict the odor profile of food from its molecular composition using experts' knowledge, fuzzy logic and optimization: Application on wines

Author

Thierry Thomas-Danguin, A. Roche, Angélique Villière, Nathalie Perrot, Thomas Chabin, and Ronan Symoneaux

Subjects
Molecular composition, biology, Computer science, business.industry, media_common.quotation_subject, Sensory system, Sensory profile, biology.organism_classification, Machine learning, computer.software_genre, Fuzzy logic, Odor, Perception, Data mining, Artificial intelligence, business, computer, Aroma, media_common
Abstract

Aroma analysis follows a well-established procedure which provides a list of odorants that contribute to a given food aroma. However, such a procedure does not allow establishing the actual sensory profile of the food because the perceptual influence of mixed odorants is poorly considered. To improve the aroma analysis efficiency, we explored an innovative strategy which combines classical aroma analysis results with expert knowledge on aroma formulation through a modelling approach relying on fuzzy logic and optimization. The model queries analytical and sensory databases in order to predict the odor profile of wines, namely the intensity of a series of odor descriptors. By comparing the output of the model and the actual sensory data we estimated that the model can predict sensory scores in a promising way.

Published
2017
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46. Experimental Study on the Identification of the Saturation of a Porous Media through Thermal Analysis

Author

Didier Delaunay, Sébastien Gueroult, Nicolas Boyard, Joël Bréard, Maxime Villière, and Vincent Sobotka

Subjects
Materials science, Thermal conductivity, Transfer molding, Heat flux, Mechanics of Materials, Mechanical Engineering, Heat transfer, Thermosetting polymer, General Materials Science, Composite material, Porous medium, Saturation (chemistry), Homogenization (chemistry)
Abstract

Resin Transfer Molding (RTM) is among the most commonly used fabrication processes for producing high quality and complex composite structural parts. RTM process consists of placing a dry fibrous preform into a mold cavity. A liquid resin is subsequently injected into that cavity. The consolidation of the part is then obtained by crosslinking in case of a thermosetting resin or by crystallization in case of thermoplastic one. Voids can be created in the porous medium during the flow of the resin. Presence of residual voids in the composite part at the end of the filling drastically affect mechanical performances. Even if several authors have contributed to a better understanding and modeling of the mechanisms of formation and transport of voids during injection, few experimental approaches allowed a direct measurement of the saturation curve. The aim of this study is then to identify the saturation of a fibrous preform by a liquid through thermal analysis. To address this issue, an experimental bench that allows the injection of a fluid into a textile preform has been used. This apparatus combines the measurement of temperatures and wall heat flux densities at several locations. A simplified modeling of the filling front has been performed with FEM using Comsol Multiphysics™. The saturation curve is modeled using several geometric parameters. Saturation is taken into account through the evolution of thermophysical properties. Effective thermophysical properties of the dry and completely-saturated porous medium in transverse and longitudinal directions have been measured by several methods, and their results have been then cross-checked and compared with good accuracy. The evolution between these two states has been modeled. A particular attention has been paid for the modeling of the transverse thermal conductivity. This parameter has been modeled using a periodic homogenization method as a function of the micro- and macro-saturation. The saturation curve parameters are determined by minimizing the cost function defined as the square difference between the measured and computed heat flux. The obtained saturation curve is finally compared with the one measured by a conductometric sensor.

Published
2014
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47. Simultaneous Microwave/Ultrasound-Assisted Hydrolysis of Starch-Based Industrial Waste into Reducing Sugars

Author

Ulla Lassi, Marie-France Marais, Jean-Marc Lévêque, Anthony Paquet, Sonia Molina-Boisseau, Tao Hu, Giancarlo Cravotto, Laura Rinaldi, Audrey Hernoux-Villière, Laboratoire de Chimie Moléculaire et Environnement (LCME), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry]), Harbin Institute of Technology (HIT), Dipartimento di Patologia e Sanità Animale, Università degli studi di Napoli Federico II, Università degli studi di Torino (UNITO), Centre de Recherches sur les Macromolécules Végétales (CERMAV), and Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)

Subjects
Materials science, Starch, General Chemical Engineering, 02 engineering and technology, 01 natural sciences, Industrial waste, Catalysis, chemistry.chemical_compound, Hydrolysis, Mass transfer, [CHIM]Chemical Sciences, Environmental Chemistry, Organic chemistry, ta116, Potato starch, ComputingMilieux_MISCELLANEOUS, 010405 organic chemistry, Renewable Energy, Sustainability and the Environment, Sulfuric acid, General Chemistry, 021001 nanoscience & nanotechnology, 0104 chemical sciences, chemistry, Chemical engineering, [SDE]Environmental Sciences, 0210 nano-technology, Microwave
Abstract

Catalytic conversion of starch-based industrial waste into sugars was investigated using a simultaneous microwave/ultrasound-assisted procedure. The results were compared with single microwave and ultrasound irradiations to determine the eventual synergetic effects generated by the combined technologies. In heterogeneous systems, microwave irradiation enforces heat transfer, while ultrasound irradiation imposes the intensive mass transfer at the interfaces. Two hours of combined irradiation of potato starch (employed as a reference) at 60 °C in sulfuric acid revealed the existence of a glucose selectivity. In a result that was hitherto never achieved, 46% of this industrial waste was converted into reducing sugars in identical conditions.

Published
2013
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48. InnOscent chromatographic system: an innovative device to revisit aroma analysis and recombination perspectives

Author

Villière, Angélique, Le Roy, Sarah, Fillonneau, Catherine, Prost, Carole, Matrices Aliments Procédés Propriétés Structure - Sensoriel (GEPEA-MAPS2), Laboratoire de génie des procédés - environnement - agroalimentaire (GEPEA), Institut Universitaire de Technologie - Nantes (IUT Nantes), Université de Nantes (UN)-Université de Nantes (UN)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-Institut Universitaire de Technologie Saint-Nazaire (IUT Saint-Nazaire), Université de Nantes (UN)-Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Universitaire de Technologie - La Roche-sur-Yon (IUT La Roche-sur-Yon), Université de Nantes (UN)-Institut Universitaire de Technologie - Nantes (IUT Nantes), Université de Nantes (UN), Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN)-Université de Nantes (UN)-Institut Universitaire de Technologie - Nantes (IUT Nantes), Université de Nantes (UN)-Institut Universitaire de Technologie Saint-Nazaire (IUT Saint-Nazaire), Université de Nantes (UN)-Institut Universitaire de Technologie - La Roche-sur-Yon (IUT La Roche-sur-Yon), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL), Barbara Siegmund, and Erich Leitner

Subjects
[CHIM.ANAL]Chemical Sciences/Analytical chemistry, High Energy Physics::Lattice, [SDV]Life Sciences [q-bio], High Energy Physics::Phenomenology, [SDV.IDA]Life Sciences [q-bio]/Food engineering, [CHIM]Chemical Sciences, High Energy Physics::Experiment, [SDV.BBM]Life Sciences [q-bio]/Biochemistry, Molecular Biology, [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2017
Full Text
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49. Introduction of a new olfactometry-dedicated software: Application to wine aroma analysis

Author

Villière, Angélique, Guillet, Fabrice, Le Roy, Sarah, Fillonneau, Catherine, Prost, Carole, Matrices Aliments Procédés Propriétés Structure - Sensoriel (GEPEA-MAPS2), Laboratoire de génie des procédés - environnement - agroalimentaire (GEPEA), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Ecole Polytechnique de l'Université de Nantes (EPUN), Université de Nantes (UN)-Université de Nantes (UN)-Institut Universitaire de Technologie - Nantes (IUT Nantes), Université de Nantes (UN)-Institut Universitaire de Technologie Saint-Nazaire (IUT Saint-Nazaire), Université de Nantes (UN)-Institut Universitaire de Technologie - La Roche-sur-Yon (IUT La Roche-sur-Yon), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS)-Université Bretagne Loire (UBL), Ecole Nationale Vétérinaire, Agroalimentaire et de l'alimentation Nantes-Atlantique (ONIRIS), Data User Knowledge (DUKe), Laboratoire des Sciences du Numérique de Nantes (LS2N), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [CHIM.ANAL]Chemical Sciences/Analytical chemistry, [SDV.IDA]Life Sciences [q-bio]/Food engineering, [INFO]Computer Science [cs], [SDV.BBM.BC]Life Sciences [q-bio]/Biochemistry, Molecular Biology/Biochemistry [q-bio.BM], [SDV.AEN]Life Sciences [q-bio]/Food and Nutrition, ComputingMilieux_MISCELLANEOUS, [PHYS.PHYS.PHYS-DATA-AN]Physics [physics]/Physics [physics]/Data Analysis, Statistics and Probability [physics.data-an]
Abstract

International audience

Published
2017
Full Text
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