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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

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...

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Autores principales: Itoh, Toshio, Koyama, Yutaro, Shin, Woosuck, Akamatsu, Takafumi, Tsuruta, Akihiro, Masuda, Yoshitake, Uchiyama, Kazuhisa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7248965/
https://www.ncbi.nlm.nih.gov/pubmed/32397213
http://dx.doi.org/10.3390/s20092687
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author Itoh, Toshio
Koyama, Yutaro
Shin, Woosuck
Akamatsu, Takafumi
Tsuruta, Akihiro
Masuda, Yoshitake
Uchiyama, Kazuhisa
author_facet Itoh, Toshio
Koyama, Yutaro
Shin, Woosuck
Akamatsu, Takafumi
Tsuruta, Akihiro
Masuda, Yoshitake
Uchiyama, Kazuhisa
author_sort Itoh, Toshio
collection PubMed
description 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.
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spelling pubmed-72489652020-06-10 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 Itoh, Toshio Koyama, Yutaro Shin, Woosuck Akamatsu, Takafumi Tsuruta, Akihiro Masuda, Yoshitake Uchiyama, Kazuhisa Sensors (Basel) Article 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. MDPI 2020-05-08 /pmc/articles/PMC7248965/ /pubmed/32397213 http://dx.doi.org/10.3390/s20092687 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Itoh, Toshio
Koyama, Yutaro
Shin, Woosuck
Akamatsu, Takafumi
Tsuruta, Akihiro
Masuda, Yoshitake
Uchiyama, Kazuhisa
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
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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7248965/
https://www.ncbi.nlm.nih.gov/pubmed/32397213
http://dx.doi.org/10.3390/s20092687
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