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The tools and technology that are currently used to analyze chemical compoundstructures that identify polymer types in microplastics are not well-calibratedfor environmentally weathered microplastics. Microplastics that have beendegraded by environmental weathering factors can offer less analytic certaintythan samples of microplastics that have not been exposed to weatheringprocesses. Machine learning tools and techniques allow us to better calibratethe research tools for certainty in microplastics analysis. In this paper, weinvestigate whether the signatures (Raman shift values) are distinct enoughsuch that well studied machine learning (ML) algorithms can learn to identifypolymer types using a relatively small amount of labeled input data when thesamples have not been impacted by environmental degradation. Several ML modelswere trained on a well-known repository, Spectral Libraries of PlasticParticles (SLOPP), that contain Raman shift and intensity results for a rangeof plastic particles, then tested on environmentally aged plastic particles(SloPP-E) consisting of 22 polymer types. After extensive preprocessing andaugmentation, the trained random forest model was then tested on the SloPP-Edataset resulting in an improvement in classification accuracy of 93.81% from89%.

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