Raman Spectroscopy and Machine Learning for Microplastics Identification and Classification in Water Environments

As emerging pollutants of concern, microplastics (MPs) have been found in different water environments and have an impact on human health through the aquatic food chain. To advance our understanding of the traceability and environmental fate of MPs, reproducible and accurate methods, techniques, and analytical methods are necessary for MP type identification and characterization. In this study, based on Raman spectroscopy technology to extract characteristic peak information of MPs with fingerprint features, coupled to sparse autoencoder (SAE) and softmax classifier framework, the rapid identification and classification of six common MP (PET, PVC, PP, PS, PC, PE) particles in five water (pure water, rain water, lake water, tap water, and sea water) environments was realized. The results show that the average test accuracy of the trained algorithm is as high as 99.1%, which is better than 93.95% and 74.55% of the classical machine learning algorithms support vector machine (SVM) and back propagation (BP) neural network. Success rate indicates that the proposed method can be used to identify the MP samples.