Identification of Chemical Vapor Mixture Assisted by Artificially Extended Database for Environmental Monitoring
A fully integrated sensor array assisted by pattern recognition algorithm has been a primary candidate for the assessment of complex vapor mixtures based on their chemical fingerprints. Diverse prototypes of electronic nose systems consisting of a multisensory device and a post processing engine hav...
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| Format: | Article |
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MDPI AG
2022-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/3/1169 |
| _version_ | 1797484606852694016 |
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| author | Hi Gyu Moon Youngmo Jung Beomju Shin Donggeun Lee Kayoung Kim Deok Ha Woo Seok Lee Sooyeon Kim Chong-Yun Kang Taikjin Lee Chulki Kim |
| author_facet | Hi Gyu Moon Youngmo Jung Beomju Shin Donggeun Lee Kayoung Kim Deok Ha Woo Seok Lee Sooyeon Kim Chong-Yun Kang Taikjin Lee Chulki Kim |
| author_sort | Hi Gyu Moon |
| collection | DOAJ |
| description | A fully integrated sensor array assisted by pattern recognition algorithm has been a primary candidate for the assessment of complex vapor mixtures based on their chemical fingerprints. Diverse prototypes of electronic nose systems consisting of a multisensory device and a post processing engine have been developed. However, their precision and validity in recognizing chemical vapors are often limited by the collected database and applied classifiers. Here, we present a novel way of preparing the database and distinguishing chemical vapor mixtures with small data acquisition for chemical vapors and their mixtures of interest. The database for individual vapor analytes is expanded and the one for their mixtures is prepared in the first-order approximation. Recognition of individual target vapors of NO<sub>2</sub>, HCHO, and NH<sub>3</sub> and their mixtures was evaluated by applying the support vector machine (SVM) classifier in different conditions of temperature and humidity. The suggested method demonstrated the recognition accuracy of 95.24%. The suggested method can pave a way to analyze gas mixtures in a variety of industrial and safety applications. |
| first_indexed | 2024-03-09T23:06:52Z |
| format | Article |
| id | doaj.art-62abecd171404dfaa3b20d96adbdd4ad |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T23:06:52Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-62abecd171404dfaa3b20d96adbdd4ad2023-11-23T17:51:37ZengMDPI AGSensors1424-82202022-02-01223116910.3390/s22031169Identification of Chemical Vapor Mixture Assisted by Artificially Extended Database for Environmental MonitoringHi Gyu Moon0Youngmo Jung1Beomju Shin2Donggeun Lee3Kayoung Kim4Deok Ha Woo5Seok Lee6Sooyeon Kim7Chong-Yun Kang8Taikjin Lee9Chulki Kim10Center for Ecological Risk Assessment, Korea Institute of Toxicology (KIT), Jinju 52834, KoreaSensor System Research Center, Korea Institute of Science and Technology (KIST), Seoul 02792, KoreaSensor System Research Center, Korea Institute of Science and Technology (KIST), Seoul 02792, KoreaSensor System Research Center, Korea Institute of Science and Technology (KIST), Seoul 02792, KoreaSensor System Research Center, Korea Institute of Science and Technology (KIST), Seoul 02792, KoreaSensor System Research Center, Korea Institute of Science and Technology (KIST), Seoul 02792, KoreaSensor System Research Center, Korea Institute of Science and Technology (KIST), Seoul 02792, KoreaCenter for Ecological Risk Assessment, Korea Institute of Toxicology (KIT), Jinju 52834, KoreaCenter for Electronic Materials, Korea Institute of Science and Technology (KIST), Seoul 02792, KoreaSensor System Research Center, Korea Institute of Science and Technology (KIST), Seoul 02792, KoreaSensor System Research Center, Korea Institute of Science and Technology (KIST), Seoul 02792, KoreaA fully integrated sensor array assisted by pattern recognition algorithm has been a primary candidate for the assessment of complex vapor mixtures based on their chemical fingerprints. Diverse prototypes of electronic nose systems consisting of a multisensory device and a post processing engine have been developed. However, their precision and validity in recognizing chemical vapors are often limited by the collected database and applied classifiers. Here, we present a novel way of preparing the database and distinguishing chemical vapor mixtures with small data acquisition for chemical vapors and their mixtures of interest. The database for individual vapor analytes is expanded and the one for their mixtures is prepared in the first-order approximation. Recognition of individual target vapors of NO<sub>2</sub>, HCHO, and NH<sub>3</sub> and their mixtures was evaluated by applying the support vector machine (SVM) classifier in different conditions of temperature and humidity. The suggested method demonstrated the recognition accuracy of 95.24%. The suggested method can pave a way to analyze gas mixtures in a variety of industrial and safety applications.https://www.mdpi.com/1424-8220/22/3/1169chemiresistive sensor arrayidentification of gas mixturemachine learningsupport vector machine (SVM)principal component analysis (PCA) |
| spellingShingle | Hi Gyu Moon Youngmo Jung Beomju Shin Donggeun Lee Kayoung Kim Deok Ha Woo Seok Lee Sooyeon Kim Chong-Yun Kang Taikjin Lee Chulki Kim Identification of Chemical Vapor Mixture Assisted by Artificially Extended Database for Environmental Monitoring Sensors chemiresistive sensor array identification of gas mixture machine learning support vector machine (SVM) principal component analysis (PCA) |
| title | Identification of Chemical Vapor Mixture Assisted by Artificially Extended Database for Environmental Monitoring |
| title_full | Identification of Chemical Vapor Mixture Assisted by Artificially Extended Database for Environmental Monitoring |
| title_fullStr | Identification of Chemical Vapor Mixture Assisted by Artificially Extended Database for Environmental Monitoring |
| title_full_unstemmed | Identification of Chemical Vapor Mixture Assisted by Artificially Extended Database for Environmental Monitoring |
| title_short | Identification of Chemical Vapor Mixture Assisted by Artificially Extended Database for Environmental Monitoring |
| title_sort | identification of chemical vapor mixture assisted by artificially extended database for environmental monitoring |
| topic | chemiresistive sensor array identification of gas mixture machine learning support vector machine (SVM) principal component analysis (PCA) |
| url | https://www.mdpi.com/1424-8220/22/3/1169 |
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