Customizable Machine Learning Models for Rapid Microplastic Identification Using Raman Microscopy

Variations in Raman spectroscopic instrumentation alter data structure, introducing inconsistencies that disrupt the development of community-wide analytical tools. This dataset consists of Raman spectra for a variety of common plastics full-window Raman spectra that are both high resolution (<1 cm-1 wavenumber spacing) and span the full range of 100 to 4000 cm-1. The utility of this data structure for creating advanced data analysis tools is demonstrated by using the data to train several different classification models, then applying the models to classify spectra acquired on 2-dimensional Raman microscopic maps of diverse plastic microparticles. Specifically, the sklearn package in python is used to train models based on random-forest, K-nearest neighbors, and multi-layer perceptron algorithms. This dataset provides flexibility to downgrade the spectroscopic resolution of the data such that classification models can be tailored for individual instrument setups: sample tests show that high classification accuracy is maintained even when downgrading the Raman shift spacing to 1, 2, 4, or 8 cm–1. The training data were created by the authors. The data were also tested using Raman spectra obtained from the public domain.