A critical review on odor measurement and prediction. (15th June 2023) - Wang, Yujing; Shao, Liming; Kang, Xinyue; Zhang, Hua; Lü, Fan; He, Pinjing
Abstract: Odor pollution has become a global environmental issue of increasing concern in recent years. Odor measurements are the basis of assessing and solving odor problems. Olfactory and chemical analysis can be used for odor and odorant measurements. Olfactory analysis reflects the subjective perception of human, and chemical analysis reveals the chemical composition of odors. As an alternative to olfactory analysis, odor prediction methods have been developed based on chemical and olfactory analysis results. The combination of olfactory and chemical analysis is the best way to control odor pollution, evaluate the performances of the technologies, and predict odor. However, there are still some limitations and obstacles for each method, their combination, and the prediction. Here, we present an overview of odor measurement and prediction. Different olfactory analysis methods (namely, the dynamic olfactometry method and the triangle odor bag method) are compared in detail, the latest revisions of the standard olfactometry methods are summarized, and the uncertainties of olfactory measurement results (i.e., the odor thresholds) are analyzed. The researches, applications, and limitations of chemical analysis and odor prediction are introduced and discussed. Finally, the development and application of odor databases and algorithms for optimizing odor measurement and prediction methods are prospected, and a preliminary framework for an odor database is proposed. This review is expected to provide insights into odor measurement and prediction. Graphical abstract: Image 1 Highlights: Differences in methods and panel could cause uncertainties in olfactory results. Odor source and thresholds should be considered for chemical analysis. Odor prediction based on odorant concentrations is a promising research direction. Algorithms and databases are needed for optimizing odor measurement and prediction. A preliminary framework for an odor database is proposed.