Literature DB >> 32188973

Pleasantness of Binary Odor Mixtures: Rules and Prediction.

Yue Ma1,2, Ke Tang1, Thierry Thomas-Danguin2, Yan Xu1.   

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).
© The Author(s) 2020. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Entities:  

Keywords:  binary mixtures; hedonic value; odorants; prediction

Mesh:

Year:  2020        PMID: 32188973     DOI: 10.1093/chemse/bjaa020

Source DB:  PubMed          Journal:  Chem Senses        ISSN: 0379-864X            Impact factor:   3.160


  1 in total

1.  A dataset on odor intensity and odor pleasantness of 222 binary mixtures of 72 key food odorants rated by a sensory panel of 30 trained assessors.

Authors:  Yue Ma; Ke Tang; Yan Xu; Thierry Thomas-Danguin
Journal:  Data Brief       Date:  2021-05-15
  1 in total

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