ArticleData in brief2021
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.
Article in Data in brief, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Machine learning for smell: ordinal odor strength prediction of molecular perfumery components.RSC advances · 2026Article
- Optimizing Odorants for Olfactory Training Based on Olfactory Receptor-Ligand Pair Analysis.The Laryngoscope · 2026Article
- Article
- Pyrfume: A window to the world's olfactory data.Scientific data · 2024Article
- An India soyabean dataset for identification and classification of diseases using computer-vision algorithms.Data in brief · 2024Article
- Formation and Analysis of Volatile and Odor Compounds in Meat-A Review.Molecules (Basel, Switzerland) · 2022Review
- Evaluation of the Impact of Different Natural Zeolite Treatments on the Capacity of Eliminating/Reducing Odors and Toxic Compounds.Materials (Basel, Switzerland) · 2021Article
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Authors and funding
4 authors.
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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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