Lee, Seunghyeon Jeong, Heewon Hong, Seok Min Yun, Daeun Lee, Jiye Kim, Eun Ju Cho, Kyung Hwa 2024-01-12T06:32:59Z 2024-01-12T06:32:59Z 2023-10-18 2023-11 0043-1354 https://pubs.kist.re.kr/handle/201004/79776 Several preprocessing procedures are required for the classification of microplastics (MPs) in aquatic systems using spectroscopic analysis. Procedures such as oxidation, which are employed to remove natural organic matter (NOM) from MPs, can be time- and cost-intensive. Furthermore, the identification process is prone to errors due to the subjective judgment of the operators. Therefore, in this study, deep learning (DL) was applied to improve the classification accuracies for mixtures of microplastic and natural organic matter (MP-NOM). A convolutional neural network (CNN)-based DL model with a spatial attention mechanism was adopted to classify substances from their Raman spectra. Subsequently, the classification results were compared with those obtained using conventional Raman spectral library software to evaluate the applicability of the model. Additionally, the crucial spectral band for training the DL model was investigated by applying gradient-weighted class activation mapping (Grad-CAM) as a post-processing technique. The model achieved an accuracy of 99.54%, which is much higher than the 31.44% achieved by the Raman spectral library. The Grad-CAM approach confirmed that the DL model can effectively identify MPs based on their visually prominent peaks in the Raman spectra. Furthermore, by tracking distinctive spectra without relying solely on visually prominent peaks, we can accurately classify MPs with less prominent peaks, which are characterized by a high standard deviation of intensity. These findings demonstrate the potential for automated and objective classification of MPs without the need for NOM preprocessing, indicating a promising direction for future research in microplastic classification. English Elsevier BV Automatic classification of microplastics and natural organic matter mixtures using a deep learning model Article 10.1016/j.watres.2023.120710 1 Water Research, v.246 Water Research 246 N scie scopus 001100865000001 Engineering, Environmental Environmental Sciences Water Resources Engineering Environmental Sciences & Ecology Water Resources Article RAMAN-SPECTROSCOPIC LIBRARY IDENTIFICATION PIGMENTS WATER FLUORESCENCE RECOGNITION FRACTIONS PARTICLES STABILITY Deep learning model Raman spectrum Microplastics Natural organic matter Automatic identification