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Multivariate statistical analysis and odour-taste network to reveal odour-taste associations

Abstract : Odor taste association has been successfully applied to enhance taste perception in foods with low sugar or low salt content. Nevertheless, selecting odor descriptors with a given associated taste remains a challenge. In the aim to look for odors able to enhance some specific taste, we tested different multivariate analyses to find links between taste descriptors and odor descriptors, starting from a set of data previously obtained using gas chromatography/olfactometry-associated taste: 68 odorant zones described with 41 odor descriptors and 4 taste associated descriptors (sweetness, saltiness, bitterness, sourness). A partial least square analysis allowed identifying odors associated with a specific taste. For instance, odors described as either fruity, sweet, strawberry, candy, floral or orange are associated to sweetness, while odors described as either toasted, potato, sulfur or mushroom are associated to saltiness. A network representation allowed visualizing the links between odor and taste descriptors. As an example a positive association was found between butter odor and both saltiness and sweetness. Our approach provided a visualization tool of the links between odor and taste description and could be used to select odor-active molecules with a potential taste enhancement effect, based on their odor descriptors.
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https://hal.inrae.fr/hal-02617715
Déposant : Migration Prodinra <>
Soumis le : lundi 25 mai 2020 - 13:03:54
Dernière modification le : mardi 9 février 2021 - 03:29:35

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Elisabeth Guichard, Carmen Barba, Thierry Thomas-Danguin, Anne Tromelin. Multivariate statistical analysis and odour-taste network to reveal odour-taste associations. Journal of Agricultural and Food Chemistry, American Chemical Society, 2020, 68 (38), pp.10318-10328. ⟨10.1021/acs.jafc.9b05462⟩. ⟨hal-02617715⟩

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