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

Area of Science:

• Environmental Chemistry
• Analytical Chemistry
• Mass Spectrometry

Background:

• Per- and polyfluoroalkyl substances (PFAS) analysis commonly uses targeted liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS).
• Targeted approaches identify less than 30% of PFAS, necessitating nontargeted strategies for broader coverage.
• Identifying unknown PFAS in complex environmental matrices remains a significant challenge.

Purpose of the Study:

• To expand the FluoroMatch Suite software for nontargeted PFAS analysis.
• To leverage full-scan (MS1) data for enhanced formula prediction and Kaufmann analysis.
• To improve the accuracy and coverage of PFAS identification in environmental samples.

Main Methods:

• Development of an 11-step formula prediction algorithm and Kaufmann analysis with kernel density-based isoline cutoffs.
• Integration of MS1 data into the FluoroMatch Suite for enhanced PFAS identification.
• Implementation of a novel homologous series voting algorithm for formula prediction.

Main Results:

• Application to AFFF-contaminated soil identified 179 PFAS-confirmed features.
• Kaufmann analysis captured 94% of confirmed PFAS while removing 96% of non-PFAS features.
• The homologous series voting algorithm achieved 0% false positive and 6% false negative rates in formula prediction.

Conclusions:

• The expanded FluoroMatch Suite with MS1 data leveraging significantly enhances nontargeted PFAS identification capacity.
• Novel algorithms provide highly accurate PFAS formula prediction, crucial for complex environmental matrices.
• This approach improves the identification of unknown PFAS, addressing limitations of targeted methods.