Abstract Microplastics pollution is an acknowledged global issue, and new strategies are required to meet the increasing demand of standardized, fast and reliable measurements. Analytical pyrolysis coupled to gas chromatography and mass spectrometry (Py-GC/MS) is a promising technique to obtain qualitative and quantitative data on microplastics mixtures through the selection of a set of characteristic pyrolysis products for each polymer. However, this data processing method is time-consuming, and no automated algorithms are currently available. In the present work, a new method for the qualitative analysis of eleven types of synthetic polymers was developed, automated and implemented in the F-Search software, with the aim of proposing a standardized procedure for data processing in Py-GC/MS analysis of plastics mixtures. The method improves on the current literature, and is based on the generation of summated mass spectra (SMS) for each polymer, obtained by extracting specific m/z and retention index coordinates corresponding to characteristic pyrolysis products. The identification of a polymer is performed by comparing its SMS with those of a built-in library. After validation, the algorithm was tested on a reference sample containing all eleven investigated polymers. The algorithm provided relative standard deviations around 10%, and the results were used to estimate the lowest amount of polymer detectable in a sample, which was found lower than 1 μg for most polymers. The performance of the algorithm was also evaluated on a real sample from ocean water trawling, providing positive results for four different polymers. The performances of the algorithm are discussed, and possible future developments are outlined.

中文翻译：

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基于 Py-GC/MS 的聚合物混合物识别算法及其在环境样品中微塑料分析中的应用

摘要 微塑料污染是一个公认的全球性问题，需要新的策略来满足对标准化、快速和可靠测量日益增长的需求。分析热解与气相色谱和质谱联用 (Py-GC/MS) 是一种很有前景的技术，可通过为每种聚合物选择一组特征热解产物来获得微塑料混合物的定性和定量数据。但是，这种数据处理方法非常耗时，目前还没有可用的自动化算法。在目前的工作中，开发、自动化并在 F-Search 软件中实施了一种定性分析 11 种合成聚合物的新方法，目的是提出 Py-GC/MS 分析中数据处理的标准化程序。塑料混合物。该方法改进了当前文献，并基于生成每种聚合物的总质谱 (SMS)，通过提取与特征热解产物对应的特定 m/z 和保留指数坐标获得。聚合物的识别是通过将其 SMS 与内置库的 SMS 进行比较来执行的。验证后，该算法在包含所有 11 种研究聚合物的参考样品上进行测试。该算法提供了大约 10% 的相对标准偏差，结果用于估计样品中可检测到的最低聚合物量，发现大多数聚合物低于 1 μg。该算法的性能也在来自海水拖网的真实样本上进行了评估，为四种不同的聚合物提供了积极的结果。