Pesticide residues have become a global concern because of the threaten to human health. To realize the non-destructive detection of the pesticides containing in the drinking water, this work introduces multiple layers neural network algorithm to predict multiple kinds of pesticides with a single model of fluorescence spectra. To demonstrate the effectiveness of the algorithm, four traditional pesticides (zhongshengmycin, paclobutrazol, boscalid and pyridaben) were dissolved in the drinking water to achieve different concentrations and then the fluorescence spectra of these samples were measured. A model with four hidden layers was built using these fluorescence spectra. 50 samples were used to validate the model and the results demonstrated the feasibility of the model.

由于对人体健康的威胁，农药残留已成为全球关注的问题。为了实现饮用水中农药的无损检测，本文引入了多层神经网络算法，用一个荧光光谱模型来预测多种农药。为了证明该算法的有效性，将四种传统农药（中生霉素，多效唑，博卡利特和哒虫啉）溶解在饮用水中以达到不同的浓度，然后测量这些样品的荧光光谱。使用这些荧光光谱建立了具有四个隐藏层的模型。使用了50个样本来验证该模型，结果证明了该模型的可行性。