Rapid Identification Method for CH<sub>4</sub>/CO/CH<sub>4</sub>-CO Gas Mixtures Based on Electronic Nose
The inherent cross-sensitivity of semiconductor gas sensors makes them extremely challenging to accurately detect mixed gases. In order to solve this problem, this paper designed an electronic nose (E-nose) with seven gas sensors and proposed a rapid method for identifying CH<sub>4</sub>...
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MDPI AG
2023-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/6/2975 |
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| author | Jianxin Yin Yongli Zhao Zhi Peng Fushuai Ba Peng Peng Xiaolong Liu Qian Rong Youmin Guo Yafei Zhang |
| author_facet | Jianxin Yin Yongli Zhao Zhi Peng Fushuai Ba Peng Peng Xiaolong Liu Qian Rong Youmin Guo Yafei Zhang |
| author_sort | Jianxin Yin |
| collection | DOAJ |
| description | The inherent cross-sensitivity of semiconductor gas sensors makes them extremely challenging to accurately detect mixed gases. In order to solve this problem, this paper designed an electronic nose (E-nose) with seven gas sensors and proposed a rapid method for identifying CH<sub>4</sub>, CO, and their mixtures. Most reported methods for E-nose were based on analyzing the entire response process and employing complex algorithms, such as neural network, which result in long time-consuming processes for gas detection and identification. To overcome these shortcomings, this paper firstly proposes a way to shorten the gas detection time by analyzing only the start stage of the E-nose response instead of the entire response process. Subsequently, two polynomial fitting methods for extracting gas features are designed according to the characteristics of the E-nose response curves. Finally, in order to shorten the time consumption of calculation and reduce the complexity of the identification model, linear discriminant analysis (LDA) is introduced to reduce the dimensionality of the extracted feature datasets, and an XGBoost-based gas identification model is trained using the LDA optimized feature datasets. The experimental results show that the proposed method can shorten the gas detection time, obtain sufficient gas features, and achieve nearly 100% identification accuracy for CH<sub>4</sub>, CO, and their mixed gases. |
| first_indexed | 2024-03-11T05:56:33Z |
| format | Article |
| id | doaj.art-bf973bd1bf6447b196b8196e347b37ba |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T05:56:33Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-bf973bd1bf6447b196b8196e347b37ba2023-11-17T13:44:10ZengMDPI AGSensors1424-82202023-03-01236297510.3390/s23062975Rapid Identification Method for CH<sub>4</sub>/CO/CH<sub>4</sub>-CO Gas Mixtures Based on Electronic NoseJianxin Yin0Yongli Zhao1Zhi Peng2Fushuai Ba3Peng Peng4Xiaolong Liu5Qian Rong6Youmin Guo7Yafei Zhang8School of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaSchool of Materials, Sun Yat-sen University, Shenzhen 518107, ChinaSchool of Materials, Sun Yat-sen University, Shenzhen 518107, ChinaSchool and Materials Science and Technology, Anhui University, Hefei 230601, ChinaSchool of Mechanical and Automotive Engineering, Shanghai University of Engineering Science, Shanghai 201620, ChinaThe inherent cross-sensitivity of semiconductor gas sensors makes them extremely challenging to accurately detect mixed gases. In order to solve this problem, this paper designed an electronic nose (E-nose) with seven gas sensors and proposed a rapid method for identifying CH<sub>4</sub>, CO, and their mixtures. Most reported methods for E-nose were based on analyzing the entire response process and employing complex algorithms, such as neural network, which result in long time-consuming processes for gas detection and identification. To overcome these shortcomings, this paper firstly proposes a way to shorten the gas detection time by analyzing only the start stage of the E-nose response instead of the entire response process. Subsequently, two polynomial fitting methods for extracting gas features are designed according to the characteristics of the E-nose response curves. Finally, in order to shorten the time consumption of calculation and reduce the complexity of the identification model, linear discriminant analysis (LDA) is introduced to reduce the dimensionality of the extracted feature datasets, and an XGBoost-based gas identification model is trained using the LDA optimized feature datasets. The experimental results show that the proposed method can shorten the gas detection time, obtain sufficient gas features, and achieve nearly 100% identification accuracy for CH<sub>4</sub>, CO, and their mixed gases.https://www.mdpi.com/1424-8220/23/6/2975electronic nosegas identificationCH<sub>4</sub>-COgas mixtures |
| spellingShingle | Jianxin Yin Yongli Zhao Zhi Peng Fushuai Ba Peng Peng Xiaolong Liu Qian Rong Youmin Guo Yafei Zhang Rapid Identification Method for CH<sub>4</sub>/CO/CH<sub>4</sub>-CO Gas Mixtures Based on Electronic Nose Sensors electronic nose gas identification CH<sub>4</sub>-CO gas mixtures |
| title | Rapid Identification Method for CH<sub>4</sub>/CO/CH<sub>4</sub>-CO Gas Mixtures Based on Electronic Nose |
| title_full | Rapid Identification Method for CH<sub>4</sub>/CO/CH<sub>4</sub>-CO Gas Mixtures Based on Electronic Nose |
| title_fullStr | Rapid Identification Method for CH<sub>4</sub>/CO/CH<sub>4</sub>-CO Gas Mixtures Based on Electronic Nose |
| title_full_unstemmed | Rapid Identification Method for CH<sub>4</sub>/CO/CH<sub>4</sub>-CO Gas Mixtures Based on Electronic Nose |
| title_short | Rapid Identification Method for CH<sub>4</sub>/CO/CH<sub>4</sub>-CO Gas Mixtures Based on Electronic Nose |
| title_sort | rapid identification method for ch sub 4 sub co ch sub 4 sub co gas mixtures based on electronic nose |
| topic | electronic nose gas identification CH<sub>4</sub>-CO gas mixtures |
| url | https://www.mdpi.com/1424-8220/23/6/2975 |
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