Multiple Kinds of Pesticides Detection Based on Back-Propagation Neural Network Analysis of Fluorescence Spectra

被引：16
|
作者：
Bian, Haiyi [1 ,2 ]
Yao, Hua [1 ]
Lin, Guohua [1 ,2 ]
Yu, Yinshan [1 ,2 ]
Chen, Ruiqiang [1 ,2 ]
Wang, Xiaoyan [1 ,2 ]
Ji, Rendong [1 ,2 ]
Yang, Xiao [1 ,2 ]
Zhu, Tiezhu [1 ,2 ]
Ju, Yongfeng [1 ,2 ]
机构：
[1] Huaiyin Inst Technol, Fac Elect Informat Engn, Huaian 223003, Jiangsu, Peoples R China
[2] Jiangsu Engn Lab Lake Environm Remote Sensing Tec, Huaian 223003, Jiangsu, Peoples R China
来源：
IEEE PHOTONICS JOURNAL | 2020年 / 12卷 / 02期
基金：
中国国家自然科学基金;
关键词：
Pesticide residue; fluorescence spectroscopy; BP neural network algorithm; PHASE MICROEXTRACTION; HUMAN HEALTH; RESIDUES; SHIFT; TAPE;
D O I：
10.1109/JPHOT.2020.2973653
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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.
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页数：10
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