School of Biotechnology, Jiangnan University, Jiangsu, People's Republic of China.
Centre des Sciences du Goût et de l'Alimentation, INRAE, CNRS, AgroSup Dijon, Université Bourgogne Franche-Comté, Dijon, France.
Chem Senses. 2020 May 21;45(4):303-311. doi: 10.1093/chemse/bjaa020.
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).
愉快感是气味感知的一个主要维度。虽然自然散发的气味依赖于气味物质的混合物,但很少有研究调查气味混合物的愉快感知背后的规则。为了解决这个问题,一组由 72 种气味物质组成的 222 种二元混合物由 30 名参与者组成的小组对其强度和愉快感进行了评价。在大多数情况下,二元混合物的愉快感取决于其成分的愉快感和强度。然而,在由 2 种具有相似愉快感评分的成分组成的 6 种二元混合物中,观察到了显著的愉快感部分相加。一个涉及成分的愉快感和τ值(反映成分气味强度)的数学模型被应用于预测混合物的愉快感。使用该模型,可以有效地预测包括强度和愉快感对比的两种成分的混合物的愉快感(面板水平的 R2 > 0.80,均方根误差<0.67)。