Rapid chemical screening of microplastics and nanoplastics by thermal desorption and pyrolysis mass spectrometry with unsupervised fuzzy clustering

The transport and chemical identification of microplastics and nanoplastics (MNPs) are critical to the concerns over plastic accumulation in the environment. Chemically and physically transient MNP species present unique challenges for isolation and analysis due to many factors such as their size, color, surface properties, morphology, and potential for chemical change. These factors contribute to the eventual environmental and toxicological impact of MNPs. As analytical methods and instrumentation continue to be developed for this application, analytical test materials will play an important role. Here, a direct mass spectrometry screening method was developed to rapidly characterize manufactured and weathered MNPs, complementing lengthy pyrolysis-gas chromatography mass spectrometry analyses. The chromatography-free measurements took advantage of Kendrick mass defect analysis, in-source collision induced dissociation, and advancements in machine learning approaches for data analysis of the complex mass spectra. In this study, we applied Gaussian mixture models and fuzzy c-means clustering for the unsupervised analysis of MNP sample spectra, incorporating clustering stability and information criterion measurements to determine latent dimensionality. These models provided insight into the composition of mixed and weathered MNP samples. The multiparametric data acquisition and machine learning approach presented improved confidence in polymer identification and differentiation.