Simultaneous Screening of Multiple Persistent Organic Pollutant Contamination via Excitation–Emission Matrix and Image Recognition Artificial Intelligence

The aim of this study was to devise a methodology for the on-site evaluation of the potential presence of persistent organic pollutants (POPs) in incoming waste materials. A novel approach to POP screening was developed, integrating an excitation–emission matrix method, which facilitates facile and comprehensive measurement, with machine learning of the resulting spectral images. With this approach, it is possible to determine the presence or absence of POPs with very high accuracy for screening, with a correct response rate of ≥75% and a specificity ≥80%. Assuming that this method is applied to actual delivery, we estimate that it is possible to obtain a discrimination result in about 10 min after sample removal.