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.

中文翻译：

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基于荧光光谱反向传播神经网络分析的多种农药检测

荧光光谱法以其无损、非接触、高速、无需复杂的前处理程序等优点，在农药残留检测领域越来越受到关注。但是，由于荧光光谱法检测的农药浓度是按照比尔-朗伯定律计算的，该方法只能用于检测含有单一农药或几种荧光完全不同的农药样品。不符合实际情况。在本文中，为了克服这一缺点，引入了反向传播（BP）神经网络算法，通过荧光光谱检测多种农药。