Integrating C–H Information to Improve Machine Learning Classification Models for Microplastic Identification from Raman Spectra

Research has shown microplastic particles to be pervasive pollutants in the natural environment, but labor-intensive sample preparation, data acquisition, and analysis protocols continue to be necessary to navigate their diverse chemistry. Machine learning (ML) classification models have shown promise for identifying microplastics from their Raman spectra, but all attempts to date have focused on the lower energy "fingerprint" region of the spectrum. We explore strategies to improve ML classification models based on the k-nearest-neighbor algorithm by including other regions of the Raman spectra. The information content inherent in C–H bonds, which occur in the higher frequency region of 2500–3600 cm–1, is found to be particularly powerful in improving classification model performance. Variations in the relative intensity of peaks arising from C–H vibrations improve identification capabilities for plastics that the fingerprint region alone struggles with, such as resolving acrylonitrile butadiene styrene from polystyrene and identifying poly(vinyl chloride), polyurethane, and polyoxymethylene. Testing of strategies to both acquire and analyze data across the two regions is explored for their efficacy and their compatibility with real-world sampling restrictions. We find that localized normalization of spectra, independently acquired in the two regions, provides the most direct and effective route to improving the ML classification performance.