Comprehensive non-targeted workflow for confident identification of perfluoroalkyl substances (PFAS)

Significance of the topic

Per- and polyfluoroalkyl substances (PFAS) are persistent, bioaccumulative, and potentially toxic chemicals of increasing regulatory and environmental concern. Traditional targeted analyses rely on reference standards and cannot address the thousands of emerging PFAS and their transformation products. A robust non-targeted workflow is essential to detect and identify known and unknown PFAS in complex matrices, support source tracking, and guide remediation efforts.

Goals and Study Overview

This study demonstrates an end-to-end non-targeted PFAS analysis workflow combining high-resolution accurate-mass (HRAM) Orbitrap Exploris 240 mass spectrometry with automated data processing and visualization using Thermo Scientific Compound Discoverer 3.4 software. The approach was evaluated using three unknown samples from the NIST PFAS NTAILS interlaboratory study: a methanolic PFAS standard mix, an aqueous film-forming foam (AFFF) formulation mixture, and a soil extract impacted by AFFF.

Methodology and Instrumentation

UHPLC separation was performed on a Vanquish Flex system equipped with a PFAS Installation Kit to replace fluoropolymer-containing materials and a delay column to shift background contamination. A C18 analytical column was operated at 0.4 mL/min with a water/methanol gradient containing 20 mM ammonium acetate. MS analysis used an Orbitrap Exploris 240 with EASY-IC internal calibration in full-scan MS1 (240 000 resolution) and data-dependent MS2 (30 000 resolution) mode with stepped HCD energies (5, 30, 60).

Used Instrumentation

• Thermo Scientific Vanquish Flex UHPLC with PFAS Installation Kit
• Thermo Scientific Orbitrap Exploris 240 mass spectrometer with EASY-IC
• Thermo Scientific Compound Discoverer 3.4 software

Main Results and Discussion

The automated workflow integrates PFAS-specific libraries and databases—including mzCloud, NIST 2023, Duke University in silico spectral library, and FluoroMatch fragment database—and applies the Schymanski confidence scale (Levels 1–4) via data filtering and tagging. Across the three samples, more than 250 PFAS were annotated: Level 1–2 assignments used reference standards and spectral library matches; Levels 3–4 leveraged in silico and fragment databases to expand coverage. Visualization tools such as Kendrick mass defect plots revealed homologous series and aided formula confirmation. An Orthogonal MS1 plot distinguished perfluorinated compounds with heteroatoms. Statistical analyses (PCA and differential plots) highlighted compositional differences between the AFFF mixture and soil extract, identifying over 125 PFAS unique to the impacted soil. Molecular networking clustered structurally related PFAS, facilitating source fingerprinting.

Benefits and Practical Applications

• Comprehensive non-targeted PFAS coverage against a large suspect list
• Consistent confidence assignment using the Schymanski scale
• Automated data reduction and tagging reduce manual subjectivity
• Rich visualization and statistical tools support comparative studies and remediation planning

Future Trends and Possibilities

The approach can be extended to other non-targeted applications such as extractables and leachables, food safety, clinical metabolomics, and toxicology. Future developments may include expanded spectral and in silico libraries, AI-driven annotation, enhanced fragmentation techniques, and integration with orthogonal separation methods to further improve detection of novel and trace-level analytes.

Conclusion

By combining the high mass accuracy and resolution of the Orbitrap Exploris 240 with a comprehensive, automated PFAS annotation workflow in Compound Discoverer 3.4, laboratories can achieve confident, reproducible non-targeted PFAS analysis. This unified platform addresses challenges of unknown PFAS detection and provides versatile tools for environmental monitoring and beyond.

Reference

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