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SWATH-F: A Novel Nontarget Strategy Based on the SWATH-MS Deconvolution Method Assisting in Annotating PFAS Homologues in Multisample Studies.

Laihui LiRongjun GaoXuebing WangYiyan DengHong SunHuijing SunBeibei ZhangNan-Yang YuCheng GuBingcai PanHongxia YuSi Wei
Published in: Analytical chemistry (2023)
In order to identify emerging per- and polyfluoroalkyl substances (PFASs) and their alternatives in the environment or population, we need to perform extensive profiling of PFASs to determine their distribution in samples. The sequential window acquisition of all theoretical fragment-ion spectra (SWATH mode) is capable of obtaining a wide range of MS2 spectra but is difficult for direct identification of PFASs due to its complex MS2 spectra, and the nontarget screening method is difficult to identify due to its lack of a priori information. In this study, we demonstrated the great potential of SWATH-F, a nontarget fragment-based homologue screening method in combination with the SWATH-MS deconvolution, for detecting PFASs. We evaluated the application of SWATH-F to gradient spiked samples and real population serum samples, compared it with nontarget homologue screening in the information-dependent acquisition mode (IDA mode), and obtained better results for SWATH-F with 276% improvement (IDA:17 PFASs, SWATH-F: 64 PFASs) in identification. In addition, we automated the screening and identification process of SWATH-F to facilitate its use by researchers. SWATH-F is freely available on GitHub (https://github.com/njuIrene/SWATH-F) under an MIT license.
Keyphrases
  • mass spectrometry
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  • ms ms
  • healthcare
  • machine learning
  • risk assessment
  • deep learning
  • health information
  • human health