« Previous Abstract"Systematic Synthesis of Multifluorinated alpha,alpha-Difluoro-gamma-lactones through Intramolecular Radical Cyclization"    Next AbstractSynthesis of Selectively gem-Difluorinated Molecules; Chiral gem-Difluorocyclopropanes via Chemo-Enzymatic Reaction and gem-Difluorinated Compounds via Radical Reaction »

Sensors (Basel)

Title:		Selective Detection of Target Volatile Organic Compounds in Contaminated Air Using Sensor Array with Machine Learning: Aging Notes and Mold Smells in Simulated Automobile Interior Contaminant Gases
Author(s):		Itoh T; Koyama Y; Shin W; Akamatsu T; Tsuruta A; Masuda Y; Uchiyama K;
Address:		"National Institute of Advanced Industrial Science and Technology (AIST), Shimo-shidami, Moriyama-ku, Nagoya 463-8560, Japan. Department of Information Science and Technology, Aichi Prefectural University, 1522-3 Ibaragabasama, Nagakute 480-1198, Aichi, Japan. DENSO Corporation, 1-1, Showa-cho, Kariya 448-8661, Aichi, Japan"
Journal Title:		Sensors (Basel)
Year:		2020
Volume:		20200508
Issue:		9
Page Number:		-
DOI:		10.3390/s20092687
ISSN/ISBN:		1424-8220 (Electronic) 1424-8220 (Linking)
Abstract:		"We investigated the selective detection of target volatile organic compounds (VOCs) which are age-related body odors (namely, 2-nonenal, pelargonic acid, and diacetyl) and a fungal odor (namely, acetic acid) in the presence of interference VOCs from car interiors (namely, n-decane, and butyl acetate). We used eight semiconductive gas sensors as a sensor array; analyzing their signals using machine learning; principal-component analysis (PCA), and linear-discriminant analysis (LDA) as dimensionality-reduction methods; k-nearest-neighbor (kNN) classification to evaluate the accuracy of target-gas determination; and random forest and ReliefF feature selections to choose appropriate sensors from our sensor array. PCA and LDA scores from the sensor responses to each target gas with contaminant gases were generally within the area of each target gas; hence; discrimination between each target gas was nearly achieved. Random forest and ReliefF efficiently reduced the required number of sensors, and kNN verified the quality of target-gas discrimination by each sensor set"
Keywords:		age-related body odor fungi odor indoor-air contamination linear discriminant analysis (LDA) machine learning principal-component analysis (PCA) semiconductive-type gas sensor;
Notes:		"PubMed-not-MEDLINEItoh, Toshio Koyama, Yutaro Shin, Woosuck Akamatsu, Takafumi Tsuruta, Akihiro Masuda, Yoshitake Uchiyama, Kazuhisa eng Switzerland 2020/05/14 Sensors (Basel). 2020 May 8; 20(9):2687. doi: 10.3390/s20092687"