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- W3025175013 abstract "We investigate selective detection of target volatile organic compounds (VOCs) as unpleasant odor substances in the presence of interference VOCs from car interiors. We selected four unpleasant odor substances as target gases, such as age-related body odors and fungi, and two interference VOC substances, such as the main olfactory components from car interiors, in this study. We used eight semiconductive gas sensors including six general semiconductive gas sensors and two bulk-response type semiconductive gas sensors. Discrimination of each target gases was carried out using machine learnings, such as principal component analysis (PCA) and linear discriminant analysis (LDA), which are dimensionality reduction methods. PCA is an unsupervised learning method, and finds the axis that maximizes the data's variance. LDA is a supervised learning method, and finds maximizes the separation between multiple classes. The machine learnings used sensor responses (r=Ra/Rg; r: sensor responses, Ra: resistance in humid pure air or in contaminant, Rg: resistance in the target gases) from all sensors." @default.
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- W3025175013 date "2020-05-01" @default.
- W3025175013 modified "2023-09-24" @default.
- W3025175013 title "Detection of Volatile Organic Compounds in Contaminated Air Using Gas Sensors with Machine Learning" @default.
- W3025175013 doi "https://doi.org/10.1149/ma2020-01261844mtgabs" @default.
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