Random forests: A machine learning methodology to highlight the volatile organic compounds involved in olfactory perception
| Autor: | Ronan Symoneaux, Evelyne Vigneau, Philippe Courcoux, Laurence Guérin, Angélique Villière |
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| Přispěvatelé: | 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), Université de Nantes (UN) |
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
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 |
| Zdroj: | Food Quality and Preference Food Quality and Preference, Elsevier, 2018, 68, pp.135-145. ⟨10.1016/j.foodqual.2018.02.008⟩ |
| ISSN: | 0950-3293 |
| DOI: | 10.1016/j.foodqual.2018.02.008⟩ |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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