Identification of pesticide-spiked river water using machine learning method of excitation-emission matrix image

Abstract

In non-targeted analysis, which has become mainstream in recent years in screening of chemical substances contamination in environmental samples, interpretation of measurement data is important. We have proposed a method to discriminate excitation-emission matrix (EEM) image by machine learning as a preliminary screening of pesticides contamination in environmental samples. In this work, we tried the feasibility of discrimination by AI using EEM image data from river water samples that were spiking pesticides and non-spiking. In addition, we examined the optimality of image data as training data. As a result, it was shown that this method can be used for preliminary screening of contaminated pesticides detection, and even if the amount of information is increased by precisely measuring the EEM image, it is not necessarily possible to improve the accuracy of judgment by AI using AlexNet.