Multiple Kinds of Pesticides Detection Based on Back-Propagation Neural Network Analysis of Fluorescence Spectra - Haiyi Bian; Hua Yao; Guohua Lin; Yinshan Yu; Ruiqiang Chen; Xiaoyan Wang; Rendong Ji; Xiao Yang; Tiezhu Zhu; Yongfeng Ju (IEEE Photonics Journal)
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.