IN SILICO MODELLING TO PREDICT THE ODOR PROFILE OF FOOD FROM ITS MOLECULAR COMPOSITION USING EXPERTS' KNOWLEDGE, FUZZY LOGIC AND OPTIMIZATION: APPLICATION ON WINES
| Affiliation auteurs | !!!! Error affiliation !!!! |
| Titre | IN SILICO MODELLING TO PREDICT THE ODOR PROFILE OF FOOD FROM ITS MOLECULAR COMPOSITION USING EXPERTS' KNOWLEDGE, FUZZY LOGIC AND OPTIMIZATION: APPLICATION ON WINES |
| Type de publication | Conference Paper |
| Year of Publication | 2017 |
| Auteurs | Roche A., Perrot N., Chabin T., Villiere A., Symoneaux R., Thomas-Danguin T. |
| Conference Name | 2017 ICOCS/IEEE INTERNATIONAL SYMPOSIUM ON OLFACTION AND ELECTRONIC NOSE (ISOEN 2017) |
| Publisher | IEEE; Int Soc Olfact & Chem Sensing; IEEE Sensor Council; IEEE Montreal Sect; Agropur; Aurora Sci; Breathtech Biomed Inc; Jlm Innovat; Senson Int |
| Conference Location | 345 E 47TH ST, NEW YORK, NY 10017 USA |
| ISBN Number | 978-1-5090-2392-9 |
| Mots-clés | Expert knowledge, Fuzzy logic, Modelling, Odor mixture, Wine aroma |
| Résumé | 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. |