FindPFΔS: Non-Target Screening for PFAS—Comprehensive Data Mining for MS(2) Fragment Mass Differences

[Image: see text] 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(2))(n), ΔHF, ΔC(n)H(3)F(2n–3), ΔC(n)H(2)F(2n–4), ΔC(n)HF(2n–5), ΔC(n)F(2n)SO(3), ΔCF(3), and ΔCF(2)O 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(2)-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.