title: Use of a modified vector model for odor intensity prediction of odorant mixtures.
abstract: Odor intensity (OI) indicates the perceived intensity of an odor by the human nose, and it is usually rated by specialized assessors. In order to avoid restrictions on assessor participation in OI evaluations, the Vector Model which calculates the OI of a mixture as the vector sum of its unmixed components' odor intensities was modified. Based on a detected linear relation between the OI and the logarithm of odor activity value (OAV-a ratio between chemical concentration and odor threshold) of individual odorants, OI of the unmixed component was replaced with its corresponding logarithm of OAV. The interaction coefficient (cosα) which represented the degree of interaction between two constituents was also measured in a simplified way. Through a series of odor intensity matching tests for binary, ternary and quaternary odor mixtures, the modified Vector Model provided an effective way of relating the OI of an odor mixture with the lnOAV values of its constituents. Thus, OI of an odor mixture could be directly predicted by employing the modified Vector Model after usual quantitative analysis. Besides, it was considered that the modified Vector Model was applicable for odor mixtures which consisted of odorants with the same chemical functional groups and similar molecular structures.
authors.affiliation: School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Xueyuan Road 30, Haidian District, Beijing 100083, China. yanluchun@126.com.
authors.forename: Luchun
authors.initials: L
authors.lastname: Yan
authors.affiliation: School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Xueyuan Road 30, Haidian District, Beijing 100083, China. liujm@ustb.edu.cn.
authors.forename: Jiemin
authors.initials: J
authors.lastname: Liu
authors.affiliation: School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Xueyuan Road 30, Haidian District, Beijing 100083, China. difang_happy@163.com.
authors.forename: Di
authors.initials: D
authors.lastname: Fang