Forbes Thomas P, Pettibone John M, Windsor Eric, Conny Joseph M, Fletcher Robert A
National Institute of Standards and Technology, Materials Measurement Science Division, Gaithersburg, Maryland 20899, United States.
Anal Chem. 2023 Aug 22;95(33):12373-12382. doi: 10.1021/acs.analchem.3c01897. Epub 2023 Aug 11.
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 analysis. The chromatography-free measurements took advantage of Kendrick mass defect analysis, in-source collision-induced dissociation, and advancements in machine learning approaches for the data analysis of complex mass spectra. In this study, we applied Gaussian mixture models and fuzzy -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.
微塑料和纳米塑料（MNPs）的传输及化学鉴定对于解决环境中塑料积累问题至关重要。化学和物理性质不稳定的MNPs种类，因其尺寸、颜色、表面性质、形态以及化学变化可能性等多种因素，给分离和分析带来了独特挑战。这些因素影响着MNPs最终的环境和毒理学影响。随着针对该应用的分析方法和仪器不断发展，分析测试材料将发挥重要作用。在此，开发了一种直接质谱筛选方法，以快速表征人造和老化的MNPs，作为对冗长的热解气相色谱-质谱分析的补充。无色谱测量利用了肯德里克质量亏损分析、源内碰撞诱导解离以及机器学习方法在复杂质谱数据分析方面的进展。在本研究中，我们应用高斯混合模型和模糊均值聚类对MNP样品光谱进行无监督分析，并结合聚类稳定性和信息准则测量来确定潜在维度。这些模型为混合和老化的MNP样品的组成提供了见解。多参数数据采集和机器学习方法提高了聚合物鉴定和区分的可信度。
