Chemical Fingerprints of New vs Weathered Plastics: A Machine Learning Approach

Abstract Regulatory initiatives to mitigate plastic pollution face challenges in identifying microplastics and their sources. Recent advances in computational fingerprinting offer opportunities for the forensic investigation of plastic pollution sources. Here, we present an open-source computational workflow integrating multi-instrumental, nontargeted (organic and inorganic) data from three distinct mass spectrometry methods to evaluate two approaches for potential plastic pollution source tracking. The workflow includes a novel data imputation, normalization, and feature selection strategy with a Random Forest classifier applied to data sets of 42 pristine (store-bought) and 21 weathered (environmentally collected) plastics from U.S. retail products and California beaches. To develop a workflow for source identification, we first, evaluated an “Universal Fingerprint” approach, which tested whether chemical profiles from pristine, store-bought plastic products could be used to identify weathered environmental counterparts based on their chemical additive patterns. This approach yielded low classification accuracy due to high geographic and manufacturer-specific formulation variability for plastic products that the random forest model could not sufficiently resolve. Second, we evaluated a “Suspect–Source Comparison” approach that simulated a scenario where the chemical fingerprint of an environmentally sampled plastic was matched to the fingerprint of a suspect source. Under this paradigm, the workflow achieved a high classification accuracy across all platforms (up to 1.00 MCC for ICP-MS/MS and 0.79 for HPLC-QToF-MS). Additionally, we found that the different analytical techniques functioned best as complementary data sets to distinguish product categories, while combining all raw data sets prior to classification introduced excessive variability that hindered the classification. Our results demonstrate the feasibility of using relative chemical similarity to connect environmental plastics to potential local pollution sources. The presented workflow establishes a methodological foundation for localized source tracking, environmental litigation, and targeted regulatory monitoring.