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PFAS Prophet: Prioritising Per- and Polyfluoroalkyl Substances Using Mass and MS/MS Spectra

  • Mathieu François Feraud 1,*,   
  • Ian A. Wood 2,   
  • Saer Samanipour 1,3,4,   
  • Pradeep Dewapriya 1,   
  • Jake O'Brien 1,3,*,   
  • Kevin Thomas 1

Received: 23 Feb 2026 | Revised: 02 Jun 2026 | Accepted: 03 Jul 2026 | Published: 10 Aug 2026

Abstract

Per- and polyfluoroalkyl substances (PFAS) are a diverse group of over 14,700 synthetic chemicals known for their environmental persistence and association with adverse health effects. Non-target analysis (NTA) using high-resolution mass spectrometry (HRMS) is increasingly used for detecting and discovering new PFAS. However, manual analysis of HRMS data is time-consuming, and current automated systems depend on known reference standards and are restricted to Data Dependent Acquisition (DDA) methods. Here we propose a machine learning algorithm to prioritize potential PFAS-related compounds based on spectral data. The model, trained on distinct compound structures, can prioritize both known and unknown PFAS-related compounds without relying on prior statistical assumptions or reference standards, achieving an F1-macro score of 0.928, however only achieving a F1 score of 0.698 on PFAS related spectra. It leverages complex fragmentation patterns, KMD, and neutral losses to enhance detection accuracy.

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DOI
10.53941/eccs.2026.100007
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Volume 2, Issue 2
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Feraud, M. F.; Wood, I. A.; Samanipour, S.; Dewapriya, P.; O’Brien, J.; Thomas, K. PFAS Prophet: Prioritising Per- and Polyfluoroalkyl Substances Using Mass and MS/MS Spectra. Environmental Contamination: Causes and Solutions 2026, 2 (2), 7. https://doi.org/10.53941/eccs.2026.100007.
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