Machine Learning of polymer types from the spectral signature of Raman spectroscopy microplastics data

TL;DR

This study demonstrates that machine learning models, particularly random forests, can effectively identify polymer types in microplastics from Raman spectral data, even after environmental weathering, with high accuracy.

Contribution

The paper shows that ML algorithms trained on pristine samples can accurately classify weathered microplastics, improving analysis certainty for environmental samples.

Findings

Random forest achieved 93.81% accuracy on weathered samples.

ML models trained on unweathered data generalize well to weathered microplastics.

Preprocessing and augmentation significantly improved classification performance.

Abstract

The tools and technology that are currently used to analyze chemical compound structures that identify polymer types in microplastics are not well-calibrated for environmentally weathered microplastics. Microplastics that have been degraded by environmental weathering factors can offer less analytic certainty than samples of microplastics that have not been exposed to weathering processes. Machine learning tools and techniques allow us to better calibrate the research tools for certainty in microplastics analysis. In this paper, we investigate whether the signatures (Raman shift values) are distinct enough such that well studied machine learning (ML) algorithms can learn to identify polymer types using a relatively small amount of labeled input data when the samples have not been impacted by environmental degradation. Several ML models were trained on a well-known repository, Spectral Libraries of Plastic Particles (SLOPP), that contain Raman shift and intensity results for a range of plastic particles, then tested on environmentally aged plastic particles (SloPP-E) consisting of 22 polymer types. After extensive preprocessing and augmentation, the trained random forest model was then tested on the SloPP-E dataset resulting in an improvement in classification accuracy of 93.81% from 89%.