Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array
The gas sensor array has long been a major tool for measuring gas due to its high sensitivity, quick response, and low power consumption. This goal, however, faces a difficult challenge because of the cross-sensitivity of the gas sensor. This paper presents a novel gas mixture analysis method for ga...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2019-09-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/19/18/3917 |
| _version_ | 1797999631650521088 |
|---|---|
| author | Shurui Fan Zirui Li Kewen Xia Dongxia Hao |
| author_facet | Shurui Fan Zirui Li Kewen Xia Dongxia Hao |
| author_sort | Shurui Fan |
| collection | DOAJ |
| description | The gas sensor array has long been a major tool for measuring gas due to its high sensitivity, quick response, and low power consumption. This goal, however, faces a difficult challenge because of the cross-sensitivity of the gas sensor. This paper presents a novel gas mixture analysis method for gas sensor array applications. The features extracted from the raw data utilizing principal component analysis (PCA) were used to complete random forest (RF) modeling, which enabled qualitative identification. Support vector regression (SVR), optimized by the particle swarm optimization (PSO) algorithm, was used to select hyperparameters <i>C</i> and <i>γ</i> to establish the optimal regression model for the purpose of quantitative analysis. Utilizing the dataset, we evaluated the effectiveness of our approach. Compared with logistic regression (LR) and support vector machine (SVM), the average recognition rate of PCA combined with RF was the highest (97%). The fitting effect of SVR optimized by PSO for gas concentration was better than that of SVR and solved the problem of hyperparameters selection. |
| first_indexed | 2024-04-11T11:07:44Z |
| format | Article |
| id | doaj.art-ae9bf7865f22429e9d2e55ebc96eb3b4 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T11:07:44Z |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-ae9bf7865f22429e9d2e55ebc96eb3b42022-12-22T04:28:14ZengMDPI AGSensors1424-82202019-09-011918391710.3390/s19183917s19183917Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor ArrayShurui Fan0Zirui Li1Kewen Xia2Dongxia Hao3Tianjin Key Laboratory of Electronic Materials Devices, School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, ChinaTianjin Key Laboratory of Electronic Materials Devices, School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, ChinaTianjin Key Laboratory of Electronic Materials Devices, School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, ChinaTianjin Key Laboratory of Electronic Materials Devices, School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, ChinaThe gas sensor array has long been a major tool for measuring gas due to its high sensitivity, quick response, and low power consumption. This goal, however, faces a difficult challenge because of the cross-sensitivity of the gas sensor. This paper presents a novel gas mixture analysis method for gas sensor array applications. The features extracted from the raw data utilizing principal component analysis (PCA) were used to complete random forest (RF) modeling, which enabled qualitative identification. Support vector regression (SVR), optimized by the particle swarm optimization (PSO) algorithm, was used to select hyperparameters <i>C</i> and <i>γ</i> to establish the optimal regression model for the purpose of quantitative analysis. Utilizing the dataset, we evaluated the effectiveness of our approach. Compared with logistic regression (LR) and support vector machine (SVM), the average recognition rate of PCA combined with RF was the highest (97%). The fitting effect of SVR optimized by PSO for gas concentration was better than that of SVR and solved the problem of hyperparameters selection.https://www.mdpi.com/1424-8220/19/18/3917gas sensor arraycross-sensitivityPCArandom forestparticle swarm optimization |
| spellingShingle | Shurui Fan Zirui Li Kewen Xia Dongxia Hao Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array Sensors gas sensor array cross-sensitivity PCA random forest particle swarm optimization |
| title | Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array |
| title_full | Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array |
| title_fullStr | Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array |
| title_full_unstemmed | Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array |
| title_short | Quantitative and Qualitative Analysis of Multicomponent Gas Using Sensor Array |
| title_sort | quantitative and qualitative analysis of multicomponent gas using sensor array |
| topic | gas sensor array cross-sensitivity PCA random forest particle swarm optimization |
| url | https://www.mdpi.com/1424-8220/19/18/3917 |
| work_keys_str_mv | AT shuruifan quantitativeandqualitativeanalysisofmulticomponentgasusingsensorarray AT ziruili quantitativeandqualitativeanalysisofmulticomponentgasusingsensorarray AT kewenxia quantitativeandqualitativeanalysisofmulticomponentgasusingsensorarray AT dongxiahao quantitativeandqualitativeanalysisofmulticomponentgasusingsensorarray |