FDA Researcher Unveils New PFAS Detection Method

A US Food and Drug Administration researcher has presented a novel data acquisition method for detecting per- and polyfluoroalkyl substances (PFAS) in complex food matrices at the 2026 American Society for Mass Spectrometry Conference. Reported on 18 June 2026, the technique developed by Christine Fisher applies mass defect filtering at the data acquisition stage rather than during post-analysis, addressing one of the most persistent challenges in non-targeted PFAS testing.

PFAS compounds contain high levels of fluorine, which has a distinct negative mass defect, causing fluorinated molecules to cluster in a specific region of the mass defect spectrum that separates them from most food-matrix interferents. Earlier methods used this property only at the data processing stage. Fisher’s approach applies mass defect filtering during data acquisition itself, assigning one entry per nominal mass across the full scan range with the tolerance window tuned to capture the PFAS-relevant range. This enables high-resolution instruments to prioritize MS/MS acquisition on ions most likely to be PFAS.

The method improves sensitivity in complex matrices such as corn silage and detects PFAS at lower spike concentrations than conventional approaches. It is tuneable, allowing operators to adjust the mass defect window to target specific PFAS subclasses or accommodate emerging compound categories, and the inclusion list can be generated in minutes using a standard spreadsheet. The technique is compatible with any high-resolution instrument capable of data-dependent acquisition, with Fisher noting a preference for DDA over data-independent acquisition on orbital ion trap platforms.

The wider commercial implication sits with high-resolution mass spectrometer vendors. As PFAS regulation tightens globally and food, water, and environmental testing programs scale up, methods that improve sensitivity without new hardware are exactly the kind of capability instrument buyers will look for. Expect uptake to spread beyond food matrices into broader environmental and clinical contexts that share similar matrix complexity.

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