Multiple Kinds of Pesticides Detection Based on Back-Propagation Neural Network Analysis of Fluorescence Spectra
Fluorescence spectroscopy attracted more and more attention in pesticide residue detection field because of its advantages of non-destructive, non-contact, high speed and no requirement of complex pre-process procedure. However, given that the concentration of the pesticide detected via fluorescence...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Photonics Journal |
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| Online Access: | https://ieeexplore.ieee.org/document/9000579/ |
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| author | Haiyi Bian Hua Yao Guohua Lin Yinshan Yu Ruiqiang Chen Xiaoyan Wang Rendong Ji Xiao Yang Tiezhu Zhu Yongfeng Ju |
| author_facet | Haiyi Bian Hua Yao Guohua Lin Yinshan Yu Ruiqiang Chen Xiaoyan Wang Rendong Ji Xiao Yang Tiezhu Zhu Yongfeng Ju |
| author_sort | Haiyi Bian |
| collection | DOAJ |
| description | Fluorescence spectroscopy attracted more and more attention in pesticide residue detection field because of its advantages of non-destructive, non-contact, high speed and no requirement of complex pre-process procedure. However, given that the concentration of the pesticide detected via fluorescence spectroscopy is calculated in accordance with the Beer-Lambert law, this method can only be used to detect samples containing a single kind of pesticide or several kinds of pesticides with completely different fluorescence which is not in accordance with practical cases. In this article, to overcome this disadvantage, back-propagation (BP) neural network algorithm was introduced to detect multiple kinds of pesticides via fluorescence spectroscopy. The results from four kinds of pesticides which are usually used for fruits and vegetables indicated the effectiveness of BP neural network algorithm. |
| format | Article |
| id | doaj-art-810fb0b538f74956bee7e0f3d20c74df |
| institution | DOAJ |
| issn | 1943-0655 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Photonics Journal |
| spelling | doaj-art-810fb0b538f74956bee7e0f3d20c74df2025-08-20T03:15:50ZengIEEEIEEE Photonics Journal1943-06552020-01-011221910.1109/JPHOT.2020.29736539000579Multiple Kinds of Pesticides Detection Based on Back-Propagation Neural Network Analysis of Fluorescence SpectraHaiyi Bian0https://orcid.org/0000-0003-3060-9872Hua Yao1Guohua Lin2Yinshan Yu3https://orcid.org/0000-0002-9379-4174Ruiqiang Chen4Xiaoyan Wang5Rendong Ji6Xiao Yang7Tiezhu Zhu8Yongfeng Ju9Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai'an, Jiangsu, ChinaFaculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai'an, Jiangsu, ChinaFaculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai'an, Jiangsu, ChinaFaculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai'an, Jiangsu, ChinaFaculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai'an, Jiangsu, ChinaFaculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai'an, Jiangsu, ChinaFaculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai'an, Jiangsu, ChinaFaculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai'an, Jiangsu, ChinaFaculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai'an, Jiangsu, ChinaFaculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai'an, Jiangsu, ChinaFluorescence spectroscopy attracted more and more attention in pesticide residue detection field because of its advantages of non-destructive, non-contact, high speed and no requirement of complex pre-process procedure. However, given that the concentration of the pesticide detected via fluorescence spectroscopy is calculated in accordance with the Beer-Lambert law, this method can only be used to detect samples containing a single kind of pesticide or several kinds of pesticides with completely different fluorescence which is not in accordance with practical cases. In this article, to overcome this disadvantage, back-propagation (BP) neural network algorithm was introduced to detect multiple kinds of pesticides via fluorescence spectroscopy. The results from four kinds of pesticides which are usually used for fruits and vegetables indicated the effectiveness of BP neural network algorithm.https://ieeexplore.ieee.org/document/9000579/Pesticide residuefluorescence spectroscopyBP neural network algorithm |
| spellingShingle | Haiyi Bian Hua Yao Guohua Lin Yinshan Yu Ruiqiang Chen Xiaoyan Wang Rendong Ji Xiao Yang Tiezhu Zhu Yongfeng Ju Multiple Kinds of Pesticides Detection Based on Back-Propagation Neural Network Analysis of Fluorescence Spectra IEEE Photonics Journal Pesticide residue fluorescence spectroscopy BP neural network algorithm |
| title | Multiple Kinds of Pesticides Detection Based on Back-Propagation Neural Network Analysis of Fluorescence Spectra |
| title_full | Multiple Kinds of Pesticides Detection Based on Back-Propagation Neural Network Analysis of Fluorescence Spectra |
| title_fullStr | Multiple Kinds of Pesticides Detection Based on Back-Propagation Neural Network Analysis of Fluorescence Spectra |
| title_full_unstemmed | Multiple Kinds of Pesticides Detection Based on Back-Propagation Neural Network Analysis of Fluorescence Spectra |
| title_short | Multiple Kinds of Pesticides Detection Based on Back-Propagation Neural Network Analysis of Fluorescence Spectra |
| title_sort | multiple kinds of pesticides detection based on back propagation neural network analysis of fluorescence spectra |
| topic | Pesticide residue fluorescence spectroscopy BP neural network algorithm |
| url | https://ieeexplore.ieee.org/document/9000579/ |
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