MDF-Based Workflow for Non-Targeted Screening for Per- and Polyfluoroalkyl Substances

Importance of the Topic

The large and structurally diverse class of per- and polyfluoroalkyl substances (PFAS) has gained increasing attention because of its widespread use since the 1940s and potential risks to human health and the environment. Traditional targeted LC-MS/MS methods rely on known standards, but cannot detect unknown PFAS. High-resolution mass spectrometry (HRMS) combined with non-targeted screening workflows is essential to discover and characterize novel PFAS in environmental, consumer, and biological samples.

Objectives and Study Overview

This study aimed to develop and validate a mass defect filtering (MDF)-based data analysis workflow for non-targeted PFAS screening using data-dependent acquisition (DDA) on the Shimadzu LCMS-9030 Quadrupole-TOF. A mixed standard of 29 PFAS was analyzed to confirm the workflow’s ability to detect known compounds and to identify additional unknown PFAS in the same sample.

Methodology

A sample containing 29 PFAS standards (1 µg/mL and 100 ng/mL) and blank ultrapure water were analyzed by LC-HRMS with DDA acquisition. The data analysis workflow consisted of:

• Exporting the precursor list from LabSolutions Insight Explore – Analyze.
• Calculating the mass defect (MD) for each precursor in Excel and selecting candidates with MD between –10 mDa and –120 mDa.
• Predicting elemental formulas under restricted PFAS settings (C4–C20, F5–F40, H1–H10, O1–O5, N0–N5, S0–S1; adding P, Cl, Br when necessary; preferring DBE 0–1 and low H counts).
• Searching MS/MS spectra against PFAS libraries (MSDIAL-PFAS and in-house) to confirm structures.
• Conducting Assign-MOL searches in public databases (ChemSpider, PubChem, EPA PFAS Master List) for tentative identification of unknown PFAS.

Instrumentation

LC system equipped with Shim-pack Velox C18 columns (2.1 × 100 mm, 2.7 µm and 2.1 × 50 mm delay column). Mobile phase A: 5 mM ammonium acetate in water; B: acetonitrile; gradient from 20% to 80% B over 9.5 min. Flow rate 0.4 mL/min, column temperature 40 °C, injection volume 1 µL. ESI source (negative mode), DDA scanning m/z 50–1000, mass range MS scan m/z 100–1000, collision energy spread ±15 V.

Main Results and Discussion

The MDF workflow successfully detected all 29 target PFAS with MD values between –17.7 mDa and –106.1 mDa. Formula prediction under restricted settings yielded unique or few candidates, with DBE and hydrogen count guiding selection. MS/MS library searches confirmed many known PFAS but were limited by library coverage. Assign-MOL database searches identified all 29 PFAS and provided tentative structures for 30 of the 42 additional PFAS-like features discovered. Blank injections confirmed no PFAS carry-over or contamination.

Benefits and Practical Applications

This approach enables rapid non-targeted detection of PFAS, combining MDF with formula prediction and database searches to reveal both known and novel substances. It supports environmental monitoring, regulatory compliance, and research into PFAS transformation and exposure.

Future Trends and Potential Applications

Further expansion of high-quality PFAS MS/MS libraries and improved fragmentation methods will enhance confidence in structural elucidation. Integration of data-independent acquisition (DIA), machine learning for feature prioritization, and automation of workflow steps can accelerate non-targeted PFAS discovery in complex matrices.

Conclusion

The MDF-based workflow on LCMS-9030 provides an effective strategy for non-targeted PFAS screening. It reliably captures known PFAS and uncovers unknown fluorinated compounds. While database searches offer tentative IDs, further advances in spectral libraries and fragmentation techniques are needed for definitive structural confirmation.

References

1. Wang Z. et al., A Never-Ending Story of PFASs? Environmental Science & Technology, 51(5), 2017, 2508–2518.
2. Liu Y. et al., High-resolution mass spectrometry methods for non-target discovery and characterization of PFASs in environmental and human samples, Trends in Analytical Chemistry, 121, 2019, 115420.
3. Bugsel B. et al., Nontarget screening strategies for PFAS prioritization and identification by high-resolution mass spectrometry: A review, Trends in Environmental Analytical Chemistry, 40, 2023, e00216.
4. Charbonnet J.A. et al., Communicating Confidence of Per- and Polyfluoroalkyl Substance Identification via High-Resolution Mass Spectrometry, Environmental Science & Technology Letters, 9, 2022, 473–481.
5. Lee J.X. et al., Development of Screening Analysis Method for PFAS in Surface Water on LC-Q-TOF, Shimadzu Application News AD-0200A, 2020.
6. Zhan Z. et al., Untargeted Screening of PFAS by HRAM-DIA method on LCMS-9030, Shimadzu Application News 04-AD-0280-en, 2022.
7. EPA PFAS Master List (retired), CompTox Chemicals Dashboard, 2024.