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Mass spectrometry is a powerful characterization technique that can identify and separate a wide variety of compounds ranging from chemical to biological entities, based on their mass-to-charge ratio (m/z). The instruments that allow this detection, known as mass spectrometers, have three components: an ion source, a mass analyzer, and a detector. These spectrometers differ based on the nature of their ion source and analyzers.Matrix-assisted laser desorption ionization (MALDI) is a commonly...
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Identifying Per- and Polyfluorinated Chemical Species with a Combined Targeted and Non-Targeted-Screening High-Resolution Mass Spectrometry Workflow
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Enhanced Environmental PFAS Characterization Using a Virtual High-Resolution Mass Spectral Library Generated by

Yi-Chi Chen1, Hsin-Yi Wu2, Man-Ni Zhuang1

  • 1Department of Environmental and Occupational Health, College of Medicine, National Cheng Kung University, Tainan 704, Taiwan.

Environmental Science & Technology
|June 10, 2026
PubMed
Summary
This summary is machine-generated.

A new AI model, Neural Per- and Polyfluoroalkyl Substances Mass Spectrometry (NPFAS-MS), accurately predicts mass spectra for emerging PFAS contaminants. This tool enhances environmental monitoring by identifying more PFAS in water and foam samples than previous methods.

Keywords:
PFASartificial neural networkhigh-resolution mass spectrometrymass spectral librarytransfer learning

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Area of Science:

  • Environmental Chemistry
  • Analytical Chemistry
  • Computational Chemistry

Background:

  • Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants with growing concerns for ecological and human health.
  • The rapid emergence of novel PFAS compounds has created a significant gap in available reference mass spectral databases for identification.
  • Existing methods for PFAS identification struggle to keep pace with the increasing number and diversity of these chemicals.

Purpose of the Study:

  • To develop and validate a novel computational model for predicting high-resolution mass spectra of Per- and polyfluoroalkyl substances (PFAS).
  • To improve the accuracy and efficiency of PFAS identification in environmental samples through advanced spectral prediction.
  • To create a comprehensive virtual spectral library for enhanced environmental monitoring of emerging PFAS.

Main Methods:

  • Development of Neural Per- and Polyfluoroalkyl Substances Mass Spectrometry (NPFAS-MS), a transfer learning-based neural network model.
  • Fine-tuning a pretrained model using Per- and polyfluoroalkyl substances (PFAS) tandem mass (MS/MS) spectra for spectral prediction.
  • Generation of a virtual Per- and polyfluoroalkyl substances (PFAS) mass spectral library using 10,553 PFAS structures from EPA and NORMAN databases.

Main Results:

  • NPFAS-MS demonstrated superior performance in predicting Per- and polyfluoroalkyl substances (PFAS) spectra compared to other in silico models, based on multiple spectral similarity metrics.
  • In library searching tasks, NPFAS-MS achieved a top-1 recall of 71.1%, significantly outperforming existing models (42.1%–55.4%).
  • Application to groundwater and aqueous film-forming foam (AFFF) samples identified more potential PFAS, including 38 in AFFF and 40 in groundwater, enabling the characterization of emerging PFAS derivatives.

Conclusions:

  • NPFAS-MS provides a powerful tool for accurate Per- and polyfluoroalkyl substances (PFAS) mass spectral prediction, addressing the limitations of current spectral databases.
  • The model facilitates the comprehensive environmental monitoring of rapidly evolving Per- and polyfluoroalkyl substances (PFAS) contamination, including novel and complex derivatives.
  • The web-based tool enables structure-to-spectrum prediction and library searching, supporting broader research and regulatory efforts in PFAS analysis.