Research on Malodor Component Identification Based on Sensor Array
With the rising demand for improved living standards and environmental protection, malodor pollution has emerged as a critical concern for both the public and regulatory authorities. Accurate prediction of malodor gas composition is essential for effective environmental monitoring and safety managem...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/13/3857 |
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| _version_ | 1850116264591097856 |
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| author | Jiaxing Xie Wen Chen Shiyun Chen Peiwen Wu Zhendong Lv Jiatao Wu Zihao Chen Zonghong Li Fan Luo Xiaohong Liu |
| author_facet | Jiaxing Xie Wen Chen Shiyun Chen Peiwen Wu Zhendong Lv Jiatao Wu Zihao Chen Zonghong Li Fan Luo Xiaohong Liu |
| author_sort | Jiaxing Xie |
| collection | DOAJ |
| description | With the rising demand for improved living standards and environmental protection, malodor pollution has emerged as a critical concern for both the public and regulatory authorities. Accurate prediction of malodor gas composition is essential for effective environmental monitoring and safety management. However, existing online malodor detection systems often suffer from short-term sensor drift, compromising their accuracy and long-term stability. To address these challenges, this study proposes an advanced electronic nose (e-nose) detection framework based on a time series data analysis. This study presents a novel approach utilizing a multi-channel sensor array for gas sampling, which establishes a robust mapping relationship between sensor response patterns and gas concentration distributions. To address the challenges of sensor drift and enhance system stability, we propose an innovative Encoder-Decoder architecture IED-CNN-LSTM incorporating external compensation mechanisms. Experimental results demonstrate that the proposed IED-CNN-LSTM model outperforms conventional methods significantly in both prediction accuracy and long-term stability. The framework achieves enhanced feature extraction from sensor time series data, enabling more precise and reliable detection of malodorous compounds. This research contributes an effective solution for real-time environmental monitoring applications while offering substantial improvements in both performance metrics and practical implementation for industrial and regulatory scenarios. |
| format | Article |
| id | doaj-art-e60fb73abd3c495f9ea2e6fefb73ed08 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-e60fb73abd3c495f9ea2e6fefb73ed082025-08-20T02:36:22ZengMDPI AGSensors1424-82202025-06-012513385710.3390/s25133857Research on Malodor Component Identification Based on Sensor ArrayJiaxing Xie0Wen Chen1Shiyun Chen2Peiwen Wu3Zhendong Lv4Jiatao Wu5Zihao Chen6Zonghong Li7Fan Luo8Xiaohong Liu9College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaCollege of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaCollege of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaCollege of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaCollege of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaCollege of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaCollege of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaCollege of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaCollege of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaCollege of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, ChinaWith the rising demand for improved living standards and environmental protection, malodor pollution has emerged as a critical concern for both the public and regulatory authorities. Accurate prediction of malodor gas composition is essential for effective environmental monitoring and safety management. However, existing online malodor detection systems often suffer from short-term sensor drift, compromising their accuracy and long-term stability. To address these challenges, this study proposes an advanced electronic nose (e-nose) detection framework based on a time series data analysis. This study presents a novel approach utilizing a multi-channel sensor array for gas sampling, which establishes a robust mapping relationship between sensor response patterns and gas concentration distributions. To address the challenges of sensor drift and enhance system stability, we propose an innovative Encoder-Decoder architecture IED-CNN-LSTM incorporating external compensation mechanisms. Experimental results demonstrate that the proposed IED-CNN-LSTM model outperforms conventional methods significantly in both prediction accuracy and long-term stability. The framework achieves enhanced feature extraction from sensor time series data, enabling more precise and reliable detection of malodorous compounds. This research contributes an effective solution for real-time environmental monitoring applications while offering substantial improvements in both performance metrics and practical implementation for industrial and regulatory scenarios.https://www.mdpi.com/1424-8220/25/13/3857malodortime series datasensor arrayshort-term driftcontinuous detection |
| spellingShingle | Jiaxing Xie Wen Chen Shiyun Chen Peiwen Wu Zhendong Lv Jiatao Wu Zihao Chen Zonghong Li Fan Luo Xiaohong Liu Research on Malodor Component Identification Based on Sensor Array Sensors malodor time series data sensor array short-term drift continuous detection |
| title | Research on Malodor Component Identification Based on Sensor Array |
| title_full | Research on Malodor Component Identification Based on Sensor Array |
| title_fullStr | Research on Malodor Component Identification Based on Sensor Array |
| title_full_unstemmed | Research on Malodor Component Identification Based on Sensor Array |
| title_short | Research on Malodor Component Identification Based on Sensor Array |
| title_sort | research on malodor component identification based on sensor array |
| topic | malodor time series data sensor array short-term drift continuous detection |
| url | https://www.mdpi.com/1424-8220/25/13/3857 |
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