Machine learning assisted Raman spectroscopy: A viable approach for the detection of microplastics

Abstract

The accumulation of microplastics (MPs) resulting from disposal of plastic waste into water sources, poses a significant threat to aquatic organisms. These are readily ingested by organisms, leading to the accumulation of harmful substances, disrupting their biological processes. Current methods for identifying microplastics have notable drawbacks, including low resolution, extended imaging time, and restricted particle size analysis. Integrating Raman spectroscopy with machine learning (ML) proves to be an effective approach for identifying and classifying MPs, especially in scenarios where they are found in environmental media or mixed with various types. Machine learning (ML) can be vital tool in assisting Raman analysis, owing to its robust feature extraction capabilities. This comprehensive review outlined the utilization of various machine learning techniques in conjunction with Raman spectral features for diverse investigations related to microplastics. The methodologies discussed encompass Principal Component Analysis, K-Nearest Neighbour, Random Forest, Support Vector Machine, and various deep learning algorithms.