Non-Targeted Screening Strategies and Challenges to Identify PFAS in Complex Food Matrices

Modern society heavily relies on the use of chemical substances. A 2019 inventory estimated that over 350,000 chemicals and mixtures were registered or used.¹ This is a cause for concern in terms of public health, due to the resulting exposure and contamination. While characterizing the presence of these compounds is a priority for public authorities, scientists involved in these characterizations face the ever-increasing number of compounds that need to be analyzed and prioritized based on their toxic potential. This is particularly the case for persistent chemicals such as per- and polyfluoroalkyl substances (PFAS). PFAS are a group of thousands of synthetic chemicals used in countless commercial applications and industrial products, such as aqueous firefighting foams, water repellent, hydraulic fluids for aircraft, non-stick coatings, and food packaging.² The C–F bonds that characterize PFAS are responsible for their thermal and chemical stability. Such features also make them and their transformation products highly persistent under the EU REACH definition,³ leading to global PFAS distribution.⁴ In addition, many PFAS are toxic and bioaccumulative, properties that may negatively influence aquatic ecosystems or cause risks to human health. In fact, they can enter foods through environmental contamination⁵ or through migration from food packaging.⁶ Diet is the main route of human exposure, accounting for more than 70% of the total intake. The main food items contributing to this exposure vary, but are dominated by fish and seafood products, fruits, vegetables, and eggs.⁵ Consequently, risk management efforts have been made into regulations to protect populations.^7,8 This led to voluntary phase-outs and usage restrictions of some PFAS, as well as the implementation of monitoring and surveillance of foodstuffs contamination.^8–11

To support these various aspects of PFAS risk analysis, robust and reliable analytical methods are implemented. They rely on targeted approaches involving liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS), specifically designed to quantify selected molecules. The robustness of such methods depends on the use of authentic native and isotope-labeled commercially available standards. While the latest EFSA scientific opinion recommends providing data on the presence of all PFAS in the environment and in foodstuffs,⁵ the considerable number of individual PFAS—several thousand molecules fall within the Organisation for Economic Co-operation and Development (OECD) definition of PFAS, i.e. molecules containing CF₃- or -CF₂- moieties¹²—cannot be included in these conventional targeted approaches, because of the lack of reference compounds, leading analytical entities and regulators to give priority to a limited number of substances (approximately 20–30).^13–15 Faced with this new challenge, a paradigm shift was needed to increase analytical coverage and meet expectations for extended PFAS monitoring. Thus, alternative approaches have been integrated, such as the determination of total oxidizable precursors (TOPs). The TOP assay indirectly estimates the amount of PFAS not covered by the targeted LC–MS/MS analysis using the oxidative conversion of so-called PFAS precursors (pre-PFASs) to quantifiable perfluorocarboxylic acids (PFCAs).¹⁶ While it is complementary to targeted quantification, there is currently no standardized method, due to a lack of specificity, loss of structural information, and inconsistencies with mass balance when short-chain PFCAs are produced.¹⁷

To keep track of structural information while extending the analytical coverage beyond the limitations of targeted analysis, non-targeted screening (NTS) approaches based on high-resolution mass-spectrometry (HRMS) coupled to efficient data reduction and prioritization strategies have thus become increasingly essential in PFAS analytical coverage.

The maturation of these strategies was made possible by two major developments: MS instrumentation and data-processing tools. At the same time, these developments have rapidly mobilized scientists towards the necessary harmonization of practices.^18,19 Data in NTS analysis are acquired using full-scan (MS1) and fragmentation experiments (MS2) and need to be processed (post-processing) with workflows using software to generate features (grouping signals over time and mass dimension). However, depending on the complexity of the sample and the workflow, several thousand features may be produced. Hence, post-processing workflows need to be tailored accordingly to prioritize the signal of interest and reduce the labor-intensive manual work of identification. In the specific case of PFAS, there is currently no best practice for selectively prioritizing their structural characteristics. In addition, although modern HRMS instruments, such as the orbital ion trap mass spectrometer, provide essential data for identifying unknown signals, true identification remains a challenge and must be reported with a level of confidence when suitable reference compounds are not available.²⁰ The unique characteristics of PFAS, such as the mass defect linked to fluorine atoms, the presence of a series of homologs, and selective fragmentation patterns, are crucial and can be exploited to monitor them.¹⁸ The use of these prioritization criteria can be advantageously associated with an additional parameter, the collision cross section (CCS Ų), which is related to the molecular size and shape and is matrix independent. This additional descriptor, measured by ion mobility mass spectrometry (IM–MS), provides an additional level of confidence in the identification process, as recommended for NTS strategies¹⁹ and CCS databases for PFAS that have started to emerge.²¹

Our work has focused on the development of PFAS NTS strategies, with the particular use of an efficient prioritization method based on literature data²² to reduce more than 1,100 features to more than 100 potential PFAS signals in complex food matrices. The originality of our strategy consists in generating a single extract used for both targeted and non-targeted determination. NTS data were generated using an orbital ion trap MS instrument in full scan and with a fragmentation experiment using data-independent acquisition (DIA). After prioritizing data reduction on potential PFAS suspect signals, features were tentatively identified using the Kendrick mass defect homolog series, databases, and suspect list matching, along with the investigation of fragmentation patterns and profiles. The results led to the elucidation of unsuspected PFAS in food matrices.²³ Finally, this work explored the specificity and potential of IM–MS for PFAS analysis as an improvement of LC–HRMS strategies. Data processing followed Jeannot et al. 2025,²³ with adaptations to account for intelligent data-dependent acquisitions (iDDAs). Suspect annotations and confidence levels were assigned following the Schymanski framework, as reported by Charbonnet et al. 2022²⁰ Such combinations of analytical methods are critical to fill data gaps in PFAS risk characterization and to protect human health.

References

1. Wang, Z.; Walker, G. W.; Muir, D. C. G. et al. Toward a Global Understanding of Chemical Pollution: A First Comprehensive Analysis of National and rRegional Chemical Inventories. Environ. Sci. Technol. 2020, 54 (5), 2575–2584. DOI: 10.1021/acs.est.9b06379
2. Glüge, J.; Scheringer, M.; Cousins, I. T. et al. An Overview of the Uses of Per- and Polyfluoroalkyl Substances (PFAS). Environ. Sci.: Processes & Impacts 2020, 22 (12), 2345–2373. DOI: 10.1039/d0em00291g
3. European Chemicals Agency. Management of PBT/vPvB Substances Under REACH. ECHA. https://echa.europa.eu/management-of-pbt-vpvb-substances (accessed 2024-09-03).
4. Cousins, I. T.; Johansson, J. H.; Salter, M. E. et al. Outside the Safe Operating Space of a New Planetary Boundary for Per- and Polyfluoroalkyl Substances (PFAS). Environ. Sci. Technol. 2022, 56 (16), 11172–11179. DOI: 10.1021/acs.est.2c02765
5. EFSA Panel on Contaminants in the Food Chain. Risk to Human Health Related to the Presence of Perfluoroalkyl Substances in Food. EFSA 2020, 18 (9) 6223. DOI: 10.2903/j.efsa.2020.6223
6. Dueñas-Mas, M. J.; Ballesteros-Gómez, A.; de Boer, J. Determination of Several PFAS Groups in Food Packaging Material from Fast-Food Restaurants in France. Chemosphere 2023, 339, 139734. DOI: 10.1016/j.chemosphere.2023.139734
7. Stockholm Convention. Information on the 16 Chemicals Added to the Stockholm Convention. https://www.pops.int/TheConvention/ThePOPs/TheNewPOPs/tabid/2511/Default.aspx. (accessed 2024-04-17).
8. European Commission. Commission Regulation (EU) 2022/2388 of 7 December 2022 Amending Regulation (EC) No 1881/2006 as Regards Maximum Levels of Perfluoroalkyl Substances in Certain Foodstuffs. Official Journal of the European Union, 2022. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32022R2388 (accessed 2023-06-06).
9. European Commission. Commission Regulation (EU) 2023/915 of 25 April 2023 on Maximum Levels for Certain Contaminants in Food and Repealing Regulation (EC) No 1881/2006. Official Journal of the European Union, 2023. https://eur-lex.europa.eu/eli/reg/2023/915/oj (accessed 2024-09-06).
10. European Commission. Commission implementing Regulation (EU) 2022/1428 of 24 August 2022 Laying Down Methods of Sampling and Analysis for the Control of Perfluoroalkyl Substances in Certain Foodstuffs. Official Journal of the European Union, 2022. https://eur-lex.europa.eu/eli/reg_impl/2022/1428/oj (accessed 2024-04-02).
11. European Commission. Commission Recommendation (EU) 2022/1431 of 24 August 2022 on the Monitoring of Perfluoroalkyl Substances in Food. Official Journal of the European Union, 2022. https://eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32022H1431 (accessed 2023-11-29).
12. Wang, Z.; Buser, A. M.; Cousins, I. T. et al. A New OECD Definition for Per- and Polyfluoroalkyl Substances. Environ. Sci. Technol. 2021, 55 (23), 15575–15578. DOI: 10.1021/acs.est.1c06896
13. European Commission. Directive 2020/2184/EU of the European Parliament and of the Council of 16 December 2020 on the Quality of Water Itended for Human Consumption. Official Journal of the European Union, 2020. https://eur-lex.europa.eu/eli/dir/2020/2184/oj (accessed 2024-09-09).
14. U.S. Environmental Protection Agency. Method 537.1: Determination of Selected Per- and Polyfluorinated Alkyl Substances in Drinking Water by Solid Phase Extraction and Liquid ChromatographyTandem Mass Spectrometry (LC/MS/MS). U.S. EPA, 2020. https://cfpub.epa.gov/si/si_public_record_Report.cfm?dirEntryId=343042&Lab=NERL (accessed 2024-09-09).
15. European Reference Laboratory for Halogenated Persistent Organic Pollutants. PFAS Working Groups and Guidance Documents. EURL POPs. https://eurl-pops.eu/working-groups#_pfas (accessed 2024-09-10).
16. Houtz, E. F.; Sedlak, D. L. Oxidative Conversion as a Means of Detecting Precursors to Perfluoroalkyl Acids in Urban Runoff. Environ. Sci. Technol. 2012, 46 (17), 9342–9349. DOI: 10.1021/es302274g
17. Martin, D.; Munoz, G.; Mejia-Avendaño, S. et al. Zwitterionic, Cationic, and Anionic Perfluoroalkyl and Polyfluoroalkyl Substances Integrated into Total Oxidizable Precursor Assay of Contaminated Groundwater. Talanta 2019, 195, 533–542. DOI: 10.1016/j.talanta.2018.11.093
18. Strynar, M.; McCord, J.; Newton, S. et al. Practical Application Guide for the Discovery of Novel PFAS in Environmental Samples Using High Resolution Mass Spectrometry. J.. Expo. Sci Environ. Epidemiol. 2023, 33 (4), 575–588. DOI: 10.1038/s41370-023-00578-2
19. Hollender, J.; Schymanski, E. L.; Ahrens, L. et al. NORMAN Guidance on Suspect and Non-Target Screening in Environmental Monitoring. Environ. Sci. Eur. 2023, 35 (1), 75. DOI: 10.1186/s12302-023-00779-4
20. Charbonnet, J. A.; McDonough, C. A.; Xiao, F. et al. Communicating Confidence of Per- and Polyfluoroalkyl Substance Identification via High-Resolution Mass Spectrometry. Environ. Sci. Technol. Lett. 2022, 9(6), 473–481. DOI: 10.1021/acs.estlett.2c00206
21. Díaz-Galiano, F. J.; Murcia-Morales, M.; Monteau, F. et al. Collision Cross-Section as a Universal Molecular Descriptor in the Analysis of PFAS and Use of Ion Mobility Spectrum Filtering for Improved Analytical Sensitivities. Analytica Chimica Acta 2023, 1251, 341026. DOI: 10.1016/j.aca.2023.341026
22. Kaufmann, A.; Butcher, P.; Maden, K. et al. Simplifying Nontargeted Analysis of PFAS in Complex Food Matrixes. J. AOAC Int. 2022, 105 (5), 1280–1287. DOI: 10.1093/jaoacint/qsac071.
23. Jeannot, C.; Macorps, N.; Amziane, A. et al. High-Resolution Mass Spectrometry for Extended PFAS Surveillance in Food: Combining Suspect and Non-Targeted Approaches. Food Chem.: X 2025, 29, 102843. DOI: 10.1016/j.fochx.2025.102843