Novel PFAS Detection via Mass Defect-Informed DDA

Christine Fisher from the US FDA presented her work at the recent 2026 American Society for Mass Spectrometry (ASMS) conference on a novel approach to detecting per- and polyfluoroalkyl substances (PFAS) in food matrices. Her research introduces a mass defect-informed data-dependent analysis (DDA) method that leverages the distinct negative mass defect characteristic of fluorine-rich PFAS compounds to improve non-targeted detection.

In this interview clip, Fisher discusses:

• You are giving a talk at ASMS on “Improved Non-Targeted Coverage of PFAS using Mass Defect-Informed Data-Dependent Analysis.” Could you tell our audience a little about this?

PFAS compounds contain high levels of fluorine, an element with a negative mass defect — the difference between nominal and exact mass. This property causes fluorinated compounds to cluster in a specific region of the mass defect spectrum, distinguishing them from most food-matrix interferents. While previous studies have exploited this feature during data processing, Fisher's approach is the first to apply mass defect filtering at the data acquisition stage itself.

Rather than listing individual suspect compounds in an inclusion list, Fisher's method assigns one entry per nominal mass across the full scan range, with the mass tolerance window tuned to capture the PFAS-relevant mass defect range. This simple yet powerful adjustment enables instruments to prioritize MS/MS acquisition on ions most likely to be PFAS, improving detection sensitivity—particularly in complex matrices such as corn silage—and at lower spike concentrations.

The method is highly tunable, allowing operators to adjust the mass defect window to target specific PFAS subclasses or accommodate emerging compound categories. It is compatible with any high-resolution instrument capable of data-dependent acquisition, and the inclusion list itself can be generated in minutes using a standard spreadsheet. Fisher notes a preference for DDA over data-independent acquisition (DIA) on orbital ion trap platforms, where longer scan times make targeted triggering more advantageous.

For identification of triggered features, the approach draws on exact mass, isotopic distribution, and MS/MS library matching to differentiate genuine PFAS signals from sulfur- or other heteroatom-containing matrix compounds with overlapping mass defects. While the technique is most effective for PFAS owing to their uniquely high fluorine content, Fisher noted that heavily halogenated or sulfonated compound classes may also benefit from similar mass defect triggering strategies.

Reference

1. Fisher, C.; Ng, B.; Koelmel, J.; Genualdi, S. Improved Non-Targeted Coverage of PFAS Using Mass Defect Informed Data-Dependent Analysis. Presented at ASMS 2026, in San Diego, California, USA. https://asms.org/docs/default-source/conference/74th-asms-final-program_as-of-may-8-2026.pdf?sfvrsn=1234fc3_0 (accessed 2026-06-15).

Further reading: Non-Targeted Food Analysis: How HRMS and Advanced Data Processing Tools Address the Current Challenges