Machine Learning-Based Identification of Pesticide Presence in River Water Using Excitation-Emission Matrix Image Analysis

Abstract

The interpretation of measurement result data is crucial in non-target analysis, an approach that has gained prominence in recent years for screening chemical substances in environmental samples. In this context, the authors have proposed a novel method that utilizes machine learning and image classification to analyze excitation-emission matrix (EEM) spectrum image data, offering a streamlined approach for screening environmental samples. This study specifically explored the viability of using AI to identify EEM spectrum image data from river water samples, both with and without added pesticides. Additionally, the qualitative and quantitative efficacy of image data as training data was scrutinized. The findings indicated that this method could be employed as a straightforward screening technique. However, merely increasing the volume of data derived from precise EEM spectrum measurements does not automatically enhance the accuracy of AI-based decisions. This highlights a critical aspect of data analysis in non-target screening methods, highlighting the importance of not only data quantity but also its relevance and quality in improving AI-driven analytical processes.