Identification of Novel Per-and-Polyfluoroalkyl Substance (PFAS) Isoform in Textile Using a Multi-Reflecting Time-ofFlight Mass Spectrometer Technology

Significance of the topic

Per- and polyfluoroalkyl substances (PFAS) are persistent, bioaccumulative chemicals of growing regulatory and public-health concern. Textiles used in outdoor clothing and other consumer goods are recognised sources of human exposure. Traditional targeted LC-MS/MS methods profile a limited subset of known PFAS, whereas non-targeted high-resolution mass spectrometry (HRMS) can broaden detection to unknown or emerging isoforms. This study demonstrates an HRMS-based non-targeted screening workflow tailored to PFAS discovery in textile matrices and introduces a mass-defect-directed data-dependent acquisition (MD-DDA) strategy to prioritise PFAS-like precursors for fragmentation, increasing analytical efficiency and confidence in identification.

Objectives and study overview

The main goals were to:

• Develop a suspect/ screening strategy for PFAS in textiles by merging existing chemical lists into a comprehensive library.
• Exploit characteristic mass-defect patterns related to fluorine content to guide MS/MS acquisition.
• Apply the workflow to a textile reference material (TRM) to demonstrate improved selection of PFAS-like ions and reliable tentative identifications.

Methodology

Sample preparation: PFAS were extracted from milled textile reference material (outdoor clothing) following the EN 17681-1:2025 alkaline-extract protocol using three replicates of 0.5 g each.

Library creation and mass-defect analysis: A merged suspect database combined entries from the CompTox dashboard and an EPA/NIST PFAS list, deduplicated to 19,610 unique compounds. For each entry the number of fluorine atoms and the negative mass defect (MDNeg) were computed to characterise the distribution of PFAS mass-defect values and to define an MD window for prioritisation.

HRMS acquisition strategy: A mass-defect-driven DDA (MD-DDA) filter was implemented so that only survey-scan features with MD values consistent with PFAS-like chemistry were eligible for MS/MS selection (Top 5 DDA). This approach was compared to standard, non-filtered DDA.

Data processing: Acquired data were handled in the waters_connect software platform for feature detection, library matching and spectral interpretation. Identifications were based on accurate mass of precursor and product ions and MS/MS spectral evidence.

Used instrumentation

Liquid chromatography: Waters ACQUITY Premier UPLC system modified with PFAS installation kit. Column: ACQUITY Premier BEH C18, 1.7 µm, 2.1 x 50 mm (90 Å). Column temperature 40 °C, sample temperature 10 °C, injection 5 µL. Mobile phases: A = 95:5 water:methanol with 2 mM ammonium acetate; B = methanol with 2 mM ammonium acetate. Typical flow 0.5 mL/min and a multi-step gradient was used to elute PFAS species.

Mass spectrometry: Waters Xevo MRT P10 multi-reflecting time-of-flight (MRT-ToF) mass spectrometer. Key MS settings included capillary voltage ~0.5 kV, cone 10 V, source temp 100 °C, desolvation temp 250 °C, cone gas 100 L/h, desolvation gas 600 L/h, StepWave RF 100 V, body gradient 5 V. Acquisition mode: DDA Top 5 with collision energy ramps (low-mass 20–40 V; high-mass 35–75 V).

Main results and discussion

Mass-defect distribution: Analysis of the merged library showed a characteristic PFAS mass-defect range (most compounds clustered between MDNeg ~0.05–0.90) correlated with fluorine counts, providing a rational filter for prioritisation.

Acquisition efficiency: Implementing MD-DDA reduced the total number of triggered MS/MS events by approximately 25% per injection compared with unfiltered DDA. This reduction indicates fewer non-specific or irrelevant MS/MS acquisitions, lowering data volume and improving throughput.

Targeting effectiveness: The frequency of DDA-triggered MS/MS focused increasingly on PFAS-like features (mass-defects in the PFAS window) when MD filters were used, demonstrating improved selection specificity for halogenated analytes of interest.

Example identification: The workflow successfully detected and confidently identified N-ethyl perfluorooctane sulfonamidoethanol (N-EtFOSE) at m/z 630.02551 with mass measurement accuracy of ~1.1 ppm. Extracted ion chromatograms and supporting MS/MS spectra validated the assignment.

Practical interpretation: MD-derived DDA improved discovery power by focusing fragmentation resources on chemically plausible PFAS candidates while maintaining spectral evidence needed for structural annotation. The approach also appears applicable to other halogenated persistent pollutants (e.g., polychlorinated and polybrominated biphenyls) that exhibit distinctive mass-defect signatures.

Benefits and practical applications

Key advantages of the proposed workflow include:

• Higher analytical efficiency — lower number of irrelevant MS/MS acquisitions, which saves instrument time and reduces data processing burden.
• Improved selectivity for PFAS-like ions, increasing the rate of meaningful MS/MS spectra for identification.
• Compatibility with existing HRMS platforms and suspect-list driven NTS workflows, enabling routine screening of textile and similar matrices.
• Transferability — the mass-defect prioritisation concept can be adapted to other halogenated contaminant classes.

Future trends and potential uses

Opportunities to extend and enhance this approach include:

1. Expanding and curating larger, more diverse suspect libraries with isotopologue and fragmentation pattern information to improve annotation confidence.
2. Combining MD filtering with ion mobility separation, in silico fragmentation prediction, and machine-learning classifiers to further prioritise relevant features and propose structures.
3. Automating end-to-end workflows (sample extraction → MD-guided acquisition → library matching → semi-quantitative reporting) for regulatory monitoring and industry QA/QC.
4. Validating the approach across a wider range of textile types and environmental matrices and establishing performance metrics for routine laboratory adoption.

Conclusion

Mass-defect-directed DDA on a multi-reflecting TOF HRMS platform offers an effective strategy to prioritise PFAS-like ions for MS/MS in non-targeted screening of textiles. The method reduces unnecessary MS/MS acquisitions by about 25% while increasing the frequency of PFAS-relevant fragmentation events and enabling confident identifications such as N-EtFOSE. The concept is broadly applicable to other halogenated persistent contaminants and presents a practical route to more efficient discovery and monitoring workflows.

Reference

1. EN 17681-1:2025. Textiles and textile products — Per and polyfluoroalkyl substances (PFAS) — Part 1: Analysis of an alkaline extract using liquid chromatography and tandem mass spectrometry.
2. U.S. Environmental Protection Agency, CompTox Chemicals Dashboard, PFAS lists. Accessed November 2023.
3. NIST Suspect List of Possible Per- and Polyfluoroalkyl Substances (PFAS). Accessed March 2023.