  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%.
