Improving Recognition Accuracy of Pesticides in Groundwater by Applying TrAdaBoost Transfer Learning Method
Accurate and rapid prediction of pesticides in groundwater is important to protect human health. Thus, an electronic nose was used to recognize pesticides in groundwater. However, the e-nose response signals for pesticides are different in groundwater samples from various regions, so a prediction mo...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/8/3856 |
| _version_ | 1797603556061085696 |
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| author | Donghui Chen Bingyang Wang Xiao Yang Xiaohui Weng Zhiyong Chang |
| author_facet | Donghui Chen Bingyang Wang Xiao Yang Xiaohui Weng Zhiyong Chang |
| author_sort | Donghui Chen |
| collection | DOAJ |
| description | Accurate and rapid prediction of pesticides in groundwater is important to protect human health. Thus, an electronic nose was used to recognize pesticides in groundwater. However, the e-nose response signals for pesticides are different in groundwater samples from various regions, so a prediction model built on one region’s samples might be ineffective when tested in another. Moreover, the establishment of a new prediction model requires a large number of sample data, which will cost too much resources and time. To resolve this issue, this study introduced the TrAdaBoost transfer learning method to recognize the pesticide in groundwater using the e-nose. The main work was divided into two steps: (1) qualitatively checking the pesticide type and (2) semi-quantitatively predicting the pesticide concentration. The support vector machine integrated with the TrAdaBoost was adopted to complete these two steps, and the recognition rate can be 19.3% and 22.2% higher than that of methods without transfer learning. These results demonstrated the potential of the TrAdaBoost based on support vector machine approaches in recognizing the pesticide in groundwater when there were few samples in the target domain. |
| first_indexed | 2024-03-11T04:32:44Z |
| format | Article |
| id | doaj.art-1ef02d190768476188e20ef5f31e8dcf |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T04:32:44Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-1ef02d190768476188e20ef5f31e8dcf2023-11-17T21:15:35ZengMDPI AGSensors1424-82202023-04-01238385610.3390/s23083856Improving Recognition Accuracy of Pesticides in Groundwater by Applying TrAdaBoost Transfer Learning MethodDonghui Chen0Bingyang Wang1Xiao Yang2Xiaohui Weng3Zhiyong Chang4Key Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, ChinaKey Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, ChinaKey Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, ChinaWeihai Institute for Bionics, Jilin University, Weihai 264401, ChinaKey Laboratory of Bionic Engineering, Ministry of Education, Jilin University, Changchun 130022, ChinaAccurate and rapid prediction of pesticides in groundwater is important to protect human health. Thus, an electronic nose was used to recognize pesticides in groundwater. However, the e-nose response signals for pesticides are different in groundwater samples from various regions, so a prediction model built on one region’s samples might be ineffective when tested in another. Moreover, the establishment of a new prediction model requires a large number of sample data, which will cost too much resources and time. To resolve this issue, this study introduced the TrAdaBoost transfer learning method to recognize the pesticide in groundwater using the e-nose. The main work was divided into two steps: (1) qualitatively checking the pesticide type and (2) semi-quantitatively predicting the pesticide concentration. The support vector machine integrated with the TrAdaBoost was adopted to complete these two steps, and the recognition rate can be 19.3% and 22.2% higher than that of methods without transfer learning. These results demonstrated the potential of the TrAdaBoost based on support vector machine approaches in recognizing the pesticide in groundwater when there were few samples in the target domain.https://www.mdpi.com/1424-8220/23/8/3856electronic nosegroundwaterpesticidesupport vector machineTrAdaBoost |
| spellingShingle | Donghui Chen Bingyang Wang Xiao Yang Xiaohui Weng Zhiyong Chang Improving Recognition Accuracy of Pesticides in Groundwater by Applying TrAdaBoost Transfer Learning Method Sensors electronic nose groundwater pesticide support vector machine TrAdaBoost |
| title | Improving Recognition Accuracy of Pesticides in Groundwater by Applying TrAdaBoost Transfer Learning Method |
| title_full | Improving Recognition Accuracy of Pesticides in Groundwater by Applying TrAdaBoost Transfer Learning Method |
| title_fullStr | Improving Recognition Accuracy of Pesticides in Groundwater by Applying TrAdaBoost Transfer Learning Method |
| title_full_unstemmed | Improving Recognition Accuracy of Pesticides in Groundwater by Applying TrAdaBoost Transfer Learning Method |
| title_short | Improving Recognition Accuracy of Pesticides in Groundwater by Applying TrAdaBoost Transfer Learning Method |
| title_sort | improving recognition accuracy of pesticides in groundwater by applying tradaboost transfer learning method |
| topic | electronic nose groundwater pesticide support vector machine TrAdaBoost |
| url | https://www.mdpi.com/1424-8220/23/8/3856 |
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