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(CF2)
n
, ΔHF, ΔC
n
H3F2n–3, ΔC
n
H2F2n–4, ΔC
n
HF2n–5, ΔC
n
F2n
SO3, ΔCF3, and ΔCF2O 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-MS2-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
() and as an executable windows application () with a graphical user interface on Zenodo.