Approaching a 0% False Positive Rate for PFAS Determination Leveraging Only MS1 Data

Liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS) is commonly used for the analysis of per- and polyfluoroalkyl substances (PFAS). Targeted approaches with LC-HRMS/MS often cover less than 30% of PFAS across various matrices and hence nontargeted strategies are necessary to enhance identification coverage. We expanded FluoroMatch Suite, a nontargeted PFAS data-processing software, to leverage full-scan (MS1) data for highly accurate formula prediction and Kaufmann analysis. Software features include Kaufmann analysis with isoline cutoffs determined using kernel density based on an EPA PFAS data set and an 11-step formula prediction algorithm. Application of the FluoroMatch Suite with the MS1 extension to AFFF contaminated soil revealed 179 PFAS-confirmed features. Kauffman 95% isoline cutoffs captured 94% of the confirmed PFAS and removed 96% of features assigned as likely non-PFAS. The PFAS-formula prediction introduced in this manuscript had a false positive rate of 26% and a false negative rate (no predicted formula) of 30%. Using a novel homologous series voting algorithm, where the predominant subclass from formula prediction were used to predict all formulas for the homologous series, we achieved a 0% false positive rate and 6% false negative rate in formula prediction. The novel nontargeted algorithms developed in this study proved to be highly accurate and by leveraging MS1 data enhances the capacity to identify unknown PFAS in complex environmental matrices.