PlasticAnalytics provides an automated workflow that addresses key bottlenecks in vibrational spectroscopic analysis of microplastics by Raman spectroscopy and Fourier transform infrared spectroscopy (FTIR). The preprocessing framework integrates an iterative asymmetric penalized least-squares (i-arPLS) baseline correction algorithm optimized for spectra with complex environmental backgrounds, coupled with a hybrid rule-based and machine learning framework that automatically removes spurious peaks (cosmic rays and CO2) while handling resampling, normalization, and smoothing. A complementary machine learning module identifies and removes substrate spectra in spectral images, ensuring that downstream classification operates only on particulate-derived signals. The pipeline combines these steps with a deep residual network and an uncertainty-aware quality-control classifier trained on virgin, consumer, and environmentally weathered plastic spectra, achieving classification accuracies of 96.9% (Raman) and 97.9% (FTIR) and matching or exceeding existing architectures. For spectral imaging, automated background removal and high-speed inference reduced processing time by over 90%, from more than 200 min (Raman) and 800 min (FTIR) to under 7 min in both cases. PlasticAnalytics supports the major instrument platforms and file formats, providing a scalable, reproducible pipeline for environmental microplastic analysis.
Levermore et al. (Fri,) studied this question.