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, <i>n</i>-decane...
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| Format: | Article |
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MDPI AG
2020-05-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/9/2687 |
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| author | Toshio Itoh Yutaro Koyama Woosuck Shin Takafumi Akamatsu Akihiro Tsuruta Yoshitake Masuda Kazuhisa Uchiyama |
| author_facet | Toshio Itoh Yutaro Koyama Woosuck Shin Takafumi Akamatsu Akihiro Tsuruta Yoshitake Masuda Kazuhisa Uchiyama |
| author_sort | Toshio Itoh |
| collection | DOAJ |
| 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, <i>n</i>-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. |
| first_indexed | 2024-03-10T19:57:58Z |
| format | Article |
| id | doaj.art-f16db5bac1e94313b5f310af21bc1be1 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T19:57:58Z |
| publishDate | 2020-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-f16db5bac1e94313b5f310af21bc1be12023-11-19T23:49:02ZengMDPI AGSensors1424-82202020-05-01209268710.3390/s20092687Selective 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 GasesToshio Itoh0Yutaro Koyama1Woosuck Shin2Takafumi Akamatsu3Akihiro Tsuruta4Yoshitake Masuda5Kazuhisa Uchiyama6National Institute of Advanced Industrial Science and Technology (AIST), Shimo-shidami, Moriyama-ku, Nagoya 463-8560, JapanNational Institute of Advanced Industrial Science and Technology (AIST), Shimo-shidami, Moriyama-ku, Nagoya 463-8560, JapanNational Institute of Advanced Industrial Science and Technology (AIST), Shimo-shidami, Moriyama-ku, Nagoya 463-8560, JapanNational Institute of Advanced Industrial Science and Technology (AIST), Shimo-shidami, Moriyama-ku, Nagoya 463-8560, JapanNational Institute of Advanced Industrial Science and Technology (AIST), Shimo-shidami, Moriyama-ku, Nagoya 463-8560, JapanNational Institute of Advanced Industrial Science and Technology (AIST), Shimo-shidami, Moriyama-ku, Nagoya 463-8560, JapanDENSO Corporation, 1-1, Showa-cho, Kariya 448-8661, Aichi, JapanWe 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, <i>n</i>-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.https://www.mdpi.com/1424-8220/20/9/2687semiconductive-type gas sensorage-related body odorfungi odorindoor-air contaminationmachine learningprincipal-component analysis (PCA) |
| spellingShingle | Toshio Itoh Yutaro Koyama Woosuck Shin Takafumi Akamatsu Akihiro Tsuruta Yoshitake Masuda Kazuhisa Uchiyama 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 Sensors semiconductive-type gas sensor age-related body odor fungi odor indoor-air contamination machine learning principal-component analysis (PCA) |
| 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 | semiconductive-type gas sensor age-related body odor fungi odor indoor-air contamination machine learning principal-component analysis (PCA) |
| url | https://www.mdpi.com/1424-8220/20/9/2687 |
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