PFΔScreen - an open-source tool for automated PFAS feature prioritization in non-target HRMS data.

Autor: Zweigle J; Environmental Analytical Chemistry, Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, 72076, Tübingen, Germany. jonathan.zweigle@uni-tuebingen.de., Bugsel B; Environmental Analytical Chemistry, Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, 72076, Tübingen, Germany., Fabregat-Palau J; Hydrogeochemistry, Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, 72076, Tübingen, Germany., Zwiener C; Environmental Analytical Chemistry, Department of Geosciences, University of Tübingen, Schnarrenbergstraße 94-96, 72076, Tübingen, Germany. christian.zwiener@uni-tuebingen.de.
Jazyk: angličtina
Zdroj: Analytical and bioanalytical chemistry [Anal Bioanal Chem] 2024 Jan; Vol. 416 (2), pp. 349-362. Date of Electronic Publication: 2023 Nov 30.
DOI: 10.1007/s00216-023-05070-2
Abstrakt: Per- and polyfluoroalkyl substances (PFAS) are a huge group of anthropogenic chemicals with unique properties that are used in countless products and applications. Due to the high stability of their C-F bonds, PFAS or their transformation products (TPs) are persistent in the environment, leading to ubiquitous detection in various samples worldwide. Since PFAS are industrial chemicals, the availability of authentic PFAS reference standards is limited, making non-target screening (NTS) approaches based on high-resolution mass spectrometry (HRMS) necessary for a more comprehensive characterization. NTS usually is a time-consuming process, since only a small fraction of the detected chemicals can be identified. Therefore, efficient prioritization of relevant HRMS signals is one of the most crucial steps. We developed PFΔScreen, a Python-based open-source tool with a simple graphical user interface (GUI) to perform efficient feature prioritization using several PFAS-specific techniques such as the highly promising MD/C-m/C approach, Kendrick mass defect analysis, diagnostic fragments (MS 2 ), fragment mass differences (MS 2 ), and suspect screening. Feature detection from vendor-independent MS raw data (mzML, data-dependent acquisition) is performed via pyOpenMS (or custom feature lists) with subsequent calculations for prioritization and identification of PFAS in both HPLC- and GC-HRMS data. The PFΔScreen workflow is presented on four PFAS-contaminated agricultural soil samples from south-western Germany. Over 15 classes of PFAS (more than 80 single compounds with several isomers) could be identified, including four novel classes, potentially TPs of the precursors fluorotelomer mercapto alkyl phosphates (FTMAPs). PFΔScreen can be used within the Python environment and is easily automatically installable and executable on Windows. Its source code is freely available on GitHub ( https://github.com/JonZwe/PFAScreen ).
(© 2023. The Author(s).)
Databáze: MEDLINE
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