Environmental Analytical Chemistry, Center for Applied Geoscience, University of Tübingen, Schnarrenbergstraße 94-96, Tübingen 72076, Germany.
Anal Chem. 2022 Aug 2;94(30):10788-10796. doi: 10.1021/acs.analchem.2c01521. Epub 2022 Jul 22.
The limited availability of analytical reference standards makes non-target screening approaches based on high-resolution mass spectrometry increasingly important for the efficient identification of unknown PFAS (per- and polyfluoroalkyl substances) and their TPs. We developed and optimized a vendor-independent open-source Python-based algorithm (FindPFΔS = FindPolyFluoroDeltas) to search for distinct fragment mass differences in MS/MS raw data (.ms2-files). Optimization with PFAS standards, two pre-characterized paper and soil samples (iterative data-dependent acquisition), revealed Δ(CF), ΔHF, ΔCHF, ΔCHF, ΔCHF, ΔCFSO, ΔCF, and ΔCFO as relevant and selective fragment differences depending on applied collision energies. In a PFAS standard mix, 94% (36 of 38 compounds from 10 compound classes) could be found by FindPFΔS. The use of fragment differences was applicable to a wide range of PFAS classes and appears as a promising new approach for PFAS identification. The influence of mass tolerance and intensity threshold on the identification efficiency and on the detection of false positives was systematically evaluated with the use of selected HR-MS-spectra (20,998) from MassBank. To this end, with the use of FindPFΔS, we could identify different unknown PFAS homologues in the paper extracts. FindPFΔS is freely available as both Python source code on GitHub (https://github.com/JonZwe/FindPFAS) and as an executable windows application (https://doi.org/10.5281/zenodo.6797353) with a graphical user interface on Zenodo.
分析参考标准的有限可用性使得基于高分辨率质谱的非靶向筛选方法对于有效识别未知 PFAS（全氟和多氟烷基物质）及其转化产物（TPs）变得越来越重要。我们开发并优化了一种与供应商无关的开源 Python 算法（FindPFΔS = FindPolyFluoroDeltas），用于在 MS/MS 原始数据（.ms2 文件）中搜索独特的碎片质量差异。通过使用 PFAS 标准、两个预先表征的纸张和土壤样本（迭代数据依赖采集）进行优化，揭示了 Δ(CF)、ΔHF、ΔCHF、ΔCHF、ΔCHF、ΔCFSO、ΔCF 和 ΔCFO 作为与所应用的碰撞能有关的选择性碎片差异。在 PFAS 标准混合物中，94%（38 种化合物中的 36 种来自 10 种化合物类别）可以通过 FindPFΔS 找到。碎片差异的使用适用于广泛的 PFAS 类别，并且似乎是 PFAS 识别的一种很有前途的新方法。使用 MassBank 中的选定高分辨质谱谱图（20,998），系统地评估了质量容限和强度阈值对识别效率和假阳性检测的影响。为此，我们可以使用 FindPFΔS 来识别纸张提取物中的不同未知 PFAS 同系物。FindPFΔS 可在 GitHub（https://github.com/JonZwe/FindPFAS）上作为 Python 源代码以及在 Zenodo 上作为带有图形用户界面的可执行 Windows 应用程序（https://doi.org/10.5281/zenodo.6797353）获得，供大家免费使用。
