Predicting odor profile of food from its chemical composition: Towards an approach based on artificial intelligence and flavorists expertise
2023
Mejean Perrot, Nathalie | Roche, Alice | Tonda, Alberto | Lutton, Evelyne | Thomas-Danguin, Thierry | Mathématiques et Informatique Appliquées (MIA Paris-Saclay) ; AgroParisTech-Université Paris-Saclay-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Institut des Systèmes Complexes - Paris Ile-de-France (ISC-PIF) ; École normale supérieure - Cachan (ENS Cachan)-Université Paris 1 Panthéon-Sorbonne (UP1)-École polytechnique (X) ; Institut Polytechnique de Paris (IP Paris)-Institut Polytechnique de Paris (IP Paris)-Institut Curie [Paris]-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS) | Centre des Sciences du Goût et de l'Alimentation [Dijon] (CSGA) ; Université de Bourgogne (UB)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Dijon ; Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro) | Université Bourgogne Franche-Comté [COMUE] (UBFC) | Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) | Université Paris-Saclay
International audience
Show more [+] Less [-]English. Odor is central to food quality. Still, a major challenge is to understand how the odorants present in a given food contribute to its specific odor profile, and how to predict this olfactory outcome from the chemical composition. In this proof-of-concept study, we seek to develop an integrative model that combines expert knowledge, fuzzy logic, and machine learning to predict the quantitative odor description of complex mixtures of odorants. The model output is the intensity of relevant odor sensory attributes calculated on the basis of the content in odor-active comounds. The core of the model is the mathematically formalized knowledge of four senior flavorists, which provided a set of optimized rules describing the sensory-relevant combinations of odor qualities the experts have in mind to elaborate the target odor sensory attributes. The model first queries analytical and sensory databases in order to standardize, homogenize, and quantitatively code the odor descriptors of the odorants. Then the standardized odor descriptors are translated into a limited number of odor qualities used by the experts thanks to an ontology. A third step consists of aggregating all the information in terms of odor qualities across all the odorants found in a given product. The final step is a set of knowledge-based fuzzy membership functions representing the flavorist expertise and ensuring the prediction of the intensity of the target odor sensory descriptors on the basis of the products' aggregated odor qualities; several methods of optimization of the fuzzy membership functions have been tested. Finally, the model was applied to predict the odor profile of 16 red wines from two grape varieties for which the content in odorants was available. The results showed that the model can predict the perceptual outcome of food odor with a certain level of accuracy, and may also provide insights into combinations of odorants not mentioned by the experts.
Show more [+] Less [-]AGROVOC Keywords
Bibliographic information
This bibliographic record has been provided by Institut national de la recherche agronomique