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Towards the improvement of food flavor analysis through the modelling of olfactometry data and expert knowledge integration

Abstract : Among the sensory dimensions involved in food flavor, the odor component is critical because it often determines the identity and the typicality of the food. Chemical flavor analysis provides a list of the odorants contained in a food product but is not sufficient to predict the odor resulting from their mixture. Indeed, odor perception relies on the processing by the olfactory system of many odorants embedded in complex mixtures and several perceptual interactions can occur. Thus, the prediction of the perceptual outcome of a complex odor mixture remains challenging and two main approaches emerge from the literature review. On the one hand, predictive approaches based on the molecular structure of odorants have been proposed but have been limited to single odorants only. On the other hand, methodologies relying on recombination strategies after the chemical analyses of flavor have been successfully applied to identify those odorants that are key to the food odor. However, the choices of odorants to be recombined are mostly based on empirical approaches. Thus, two questions arise: How can we predict the odor quality of a mixture on the basis of the molecular structure of its odorants? How can we improve food flavor analysis in order to predict the odor of a food containing several tens of odorants? These two questions are at the basis of the thesis and of this manuscript which is divided in two main axes.The first axis describes the development of a model based on the concept of angle distances computed from the molecular structure of odorants in order to predict the odor similarity between mixtures. The results highlight the importance of taking into account the odor intensity dimension to reach a good prediction level. Moreover, several perspectives are proposed to extend the model prediction beyond the similarity dimension and to predict more qualitative dimensions of odors.The second axis presents an innovative strategy which allows integrating experts’ knowledge in the flavor analysis procedure. Three different types of heterogeneous data are embedded in a mathematical model: chemical data, sensory data and knowledge from expert flavorists. Experts’ knowledge is integrated owing to the development of an ontology, which is further used to define fuzzy rules optimized by evolutionary algorithms. The final output of the model is the prediction of red wines’ odor profile on the basis of their odorants’ composition. Overall, the thesis work brings original results allowing a better understanding of food odor construction and gives insights on the underlying relationships within the odor perceptual space for complex mixtures.
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  • HAL Id : tel-02789854, version 1
  • PRODINRA : 458601

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Alice Roche. Towards the improvement of food flavor analysis through the modelling of olfactometry data and expert knowledge integration. Food and Nutrition. Université Bourgogne Franche-Comté, 2018. English. ⟨NNT : 2018UBFCK059⟩. ⟨tel-02789854⟩

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