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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

Abstract : This paper describes data collected on a set of 222 binary mixtures, based on a set of 72 odorants chiefly found in food, rated by 30 selected and trained assessors for odor intensity and pleasantness. The data included odor intensity (IAB) and pleasantness (PAB) of the mixtures, the intensity (IA, IB) and the pleasantness (PA, PB) of the two components. Moreover, the intensity (IAmix, IBmix) of the two components' odor perceived within the mixture are included. The quality of the dataset was evaluated by checking subjects' performance and by testing repeatability using the 24 duplicated trials for each attribute. This set of experimental data would be especially valuable to investigate theories of odor mixture perception in human and to test new models to predict odor perception of odor mixtures.
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https://hal.inrae.fr/hal-03257098
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Submitted on : Thursday, June 10, 2021 - 4:42:51 PM
Last modification on : Thursday, June 24, 2021 - 3:39:38 AM

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Yue Ma, Ke Tang, Yan Xu, Thierry Thomas-Danguin. 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. Data in Brief, Elsevier, 2021, 36, pp.107143. ⟨10.1016/j.dib.2021.107143⟩. ⟨hal-03257098⟩

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