Comparison of High-Resolution Data Dependent MSMS Strategies for Best Precursor Coverage of Aqueous Film Forming Foam Formulations

Significance of the Topic

Aqueous film forming foams (AFFF) contain complex mixtures of per- and polyfluoroalkyl substances (PFAS) used in firefighting. Comprehensive profiling of PFAS in AFFF is critical for environmental monitoring, regulatory compliance and risk assessment. High-resolution mass spectrometry (HR-MS) coupled with data-dependent acquisition (DDA) can enhance structural identification but requires optimized acquisition strategies to maximize precursor coverage in complex matrices.

Objectives and Study Overview

This study evaluated three DDA-based workflows to determine which approach yields the broadest precursor coverage for PFAS in two distinct AFFF formulations: a legacy product (F1) and a more recent formulation (F2). The workflows compared were:

• Iterative exclusion (five replicate injections with progressive exclusion lists)
• Smart preferred list only (single injection targeting mass-defect–based suspects)
• Combined preferred list with iterative exclusion (five injections)

Methodology and Instrumentation

The samples were diluted 1:20 000 in 70:30 water:methanol and analyzed by UHPLC–QTOF MS. Separation was achieved on an Agilent Poroshell EC-C18 column (2.1 × 100 mm) using a methanol/5 mM ammonium formate gradient. Data were acquired on an Agilent 6546 QTOF under three DDA schemes:

• Iterative exclusion: automatic exclusion lists built from previous injections to target lower-abundance precursors across five runs
• Smart preferred list: uses a mass defect and nominal mass range filter derived from the EPA CompTox PFASMASTER list for a single injection
• Combined approach: applies the smart preferred list with iterative exclusion over five injections

Data processing and annotation were performed with Fluoromatch Flow v2.431, using confidence scoring categories (A, B, C) based on MS/MS fragmentation and homologous series matching.

Main Results and Discussion

For formulation F1, iterative exclusion improved the proportion of high-confidence annotations (>C) from 59 % (one injection) to 69 % (five injections), compared to 50 % for the preferred-only approach. In F2, >C coverage increased from 42 % to 50 % with iterative exclusion and was 34 % for preferred only. Combined workflows yielded intermediate gains (56 % for F1, 36 % for F2).
The iterative exclusion strategy enhanced fragmentation of lower-abundance ions, improving B-level and above identifications. The limited mass defect window used in the preferred list likely restricted its performance, suggesting that widening the filter or iterative list refinement could close the gap.

Benefits and Practical Applications

• Enhanced PFAS coverage in complex firefighting foams supports regulatory monitoring and environmental fate studies.
• Iterative exclusion DDA workflows can be implemented in routine QA/QC laboratories to improve confidence levels in unknown screening.
• The smart preferred list approach provides a rapid single-injection snapshot of targeted suspects, useful for prioritization in high-throughput settings.

Future Trends and Potential Applications

• Expansion of mass defect and nominal mass criteria to capture broader PFAS chemistry in preferred lists.
• Integration of machine-learning algorithms for dynamic list generation and prioritization of fragmentation targets.
• Application of optimized DDA strategies in environmental matrices such as soil, water and biota to map PFAS distribution.
• Development of combined HR-MS and ion mobility workflows for structural elucidation of isomeric PFAS species.

Conclusion

Optimized iterative exclusion DDA outperformed a single-injection preferred list method for comprehensive PFAS coverage in AFFF formulations, boosting annotation confidence and capturing lower-abundance precursors. Combining tailored acquisition parameters with suspect-screening lists and multiple injections can significantly enhance PFAS identification in complex mixtures.

Instrumentation Used

• Agilent 1290 Infinity II UHPLC system
• Agilent 6546 LC/Q-TOF mass spectrometer

References

• U.S. EPA Chemistry Dashboard, PFASMASTER Chemicals. https://comptox.epa.gov/dashboard/chemical_lists (accessed July 2021).
• Fluoromatch Flow v2.431 [computer software]. Innovative Omics Solutions, 2021.
• Koelmel JP, Paige MK, Aristizabal-Henao JJ, et al. High-confidence structural annotation of PFAS by mass spectrometry and iterative exclusion. Analytical Chemistry. 2020;92(16):11186–11194.