Fluorescence detection of multiple kinds of pesticides with multi hidden layers neural network algorithm

Abstract 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.