Ma Yue, Tang Ke, Xu Yan, Thomas-Danguin Thierry
State Key Laboratory of Food Science and Technology, Jiangnan University, 1800 Lihu Avenue, Wuxi, Jiangsu 214122, China.
Key Laboratory of Industrial Biotechnology of Ministry of Education, Jiangnan University, 1800 Lihu Avenue, Wuxi, Jiangsu 214122, P. R. China.
Data Brief. 2021 May 15;36:107143. doi: 10.1016/j.dib.2021.107143. eCollection 2021 Jun.
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.
本文描述了基于72种主要存在于食物中的气味剂所收集的222组二元混合物的数据，这些数据由30名经过挑选和训练的评估员对气味强度和愉悦度进行评级。数据包括混合物的气味强度（IAB）和愉悦度（PAB）、两种成分的强度（IA、IB）和愉悦度（PA、PB）。此外，还包括混合物中所感知到的两种成分气味的强度（IAmix、IBmix）。通过检查受试者的表现以及使用每个属性的24次重复试验测试重复性来评估数据集的质量。这组实验数据对于研究人类气味混合物感知理论以及测试预测气味混合物气味感知的新模型将特别有价值。
