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Pleasantness of Binary Odor Mixtures: Rules and Prediction

Abstract : Pleasantness is a major dimension of odor percepts. While naturally encountered odors rely on mixtures of odorants, few studies have investigated the rules underlying the perceived pleasantness of odor mixtures. To address this issue, a set of 222 binary mixtures based on a set of 72 odorants were rated by a panel of 30 participants for odor intensity and pleasantness. In most cases, the pleasantness of the binary mixtures was driven by the pleasantness and intensity of its components. Nevertheless, a significant pleasantness partial addition was observed in 6 binary mixtures consisting of 2 components with similar pleasantness ratings. A mathematical model, involving the pleasantness of the components as well as τ-values reflecting components’ odor intensity, was applied to predict mixture pleasantness. Using this model, the pleasantness of mixtures including 2 components with contrasted intensity and pleasantness could be efficiently predicted at the panel level (R2 > 0.80, Root Mean Squared Error < 0.67).
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Déposant : Sabine Julien <>
Soumis le : jeudi 25 juin 2020 - 09:46:14
Dernière modification le : mardi 25 août 2020 - 20:18:01
Archivage à long terme le : : mercredi 23 septembre 2020 - 15:42:36

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Yue Ma, Ke Tang, Thierry Thomas-Danguin, Yan Xu. Pleasantness of Binary Odor Mixtures: Rules and Prediction. Chemical Senses, Oxford University Press (OUP), 2020, 45 (4), pp.303-311. ⟨10.1093/chemse/bjaa020⟩. ⟨hal-02880526⟩

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