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. 2025 Jul 29:29:102843.
doi: 10.1016/j.fochx.2025.102843. eCollection 2025 Jul.

High-resolution mass spectrometry for extended PFAS surveillance in food: combining suspect and non-targeted approaches

Cassandre Jeannot  1   2 , Nicolas Macorps  1 , Ahmed Amziane  1 , Bruno Le Bizec  1 , Julien Parinet  2 , Gaud Dervilly  1
Affiliations

High-resolution mass spectrometry for extended PFAS surveillance in food: combining suspect and non-targeted approaches

Cassandre Jeannot et al. Food Chem X. .

Abstract

Per- and polyfluoroalkyl substances (PFAS) are persistent, potentially harmful synthetic chemicals. While they can accumulate in foodstuffs, current monitoring often targets only a few compounds, likely underestimating dietary exposure. In this study, 58 food samples from Europe and North Africa-including commercial products and items from known European contamination hotspots-were analyzed using a validated high-resolution mass spectrometry workflow combining suspect screening (SS) and non-targeted screening (NTS). Seventeen PFAS were confirmed through SS, with up to 15 different PFAS in fish samples from hotspots. While NTS revealed four additional fluorinated substances: Perfluoropropanoic acid (PFPrA) detected in 48 % of samples, 6:2 Fluorotelomer sulfonic acid (6:2 FTS), Fipronil, and Fipronil sulfone. These results highlight the geographical variability of PFAS contamination in food and demonstrate the value of combined SS/NTS approaches in identifying both known and emerging PFAS, supporting more comprehensive, regulation-aligned risk assessments.

Keywords: Chemical surveillance; Emerging pollutants; Environmental hotspots; Exposure variability; Fluorinated contaminants; Food safety monitoring.

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Conflict of interest statement

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Figures

Unlabelled Image
Graphical abstract
Fig. 1
Fig. 1
Distribution of the 58 food samples analyzed, grouped by food category (n = 5), regardless of their origin (France, Algeria, or European hotspots).
Fig. 2
Fig. 2
Detection frequencies (%) of 17 PFAS identified from an initial suspect screening database of 28 compounds, across five food categories (n = 58 samples), by geographical origin: France (FR), Algeria (ALG), and European Union (EU) hotspot samples.
Fig. 3
Fig. 3
md/C vs m/C representation of the PFAS standard solution (blue circles) analyzed by LC-(ESI-)-HRMS and processed in Compound Discoverer (CD) software using the additional filter as initially defined in the literature (Kaufmann et al., 2022) (left) and after applying the optimized filter parameter developed in this study (right). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
Fig. 4
Fig. 4
Chromatographic profile of (a) PFPrA in three egg samples, (b) 6:2 FTS in an egg sample, and (c) Fipronil sulfone in two egg samples from hotspots, analyzed by LC-HRMS using the non-targeted workflow developed in this study. Signals processed with Compound Discoverer.
Fig. 5
Fig. 5
Kendrick mass defect for CF2 repeating unit vs m/z of certain PFAS standard families listed in our internal database and classified by family: PFCA (n = 11), PFSA (n = 10) and Cl-PFESA (n = 2). Homologous series are identified by shifts in the x-axis divisible by 49.9968 (CF2) and the same CF2 normalized mass defect. In green, three suspect PFAS signals highlighted by NTS data processing (in green). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
See this image and copyright information in PMC

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