{
  "abstract": "Nontargeted identification of non-conventional per- and polyfluoroalkyl substances (PFAS), including fluorinated pharmaceuticals and pesticides, remains challenging. Existing machine learning models for nontargeted analysis, primarily trained on tandem mass spectra of nonfluorinated compounds, may not generalize well to non-conventional PFAS because of their distinct mass spectrometric chemistry. Here, we established FMS2Lib, an experimental tandem mass spectral library containing 34,686 fluorinated drug-like compounds (F 1-4 ), to support machine learning-based nontargeted PFAS analysis. We demonstrated that training with FMS2Lib substantially improved the performance of existing models for both molecular formula prediction and structure annotation. We further applied these models to identify PFAS in passive samplers from wastewater effluents and surface waters and in high-throughput laboratory biotransformation experiments, revealing dozens of non-conventional PFAS and their biotransformation products. Collectively, FMS2Lib addresses a major data bottleneck in machine learning-based nontargeted analysis of non-conventional PFAS.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": null,
  "author_total": 10,
  "authors": [
    {
      "name": "Tao Yang"
    },
    {
      "name": "Xiaoyun Wang"
    },
    {
      "name": "Diwen Yang"
    },
    {
      "name": "Elizabeth Xu"
    },
    {
      "name": "Lifei Wang"
    },
    {
      "name": "Sonya M. Kleywegt"
    },
    {
      "name": "Paul Helm"
    },
    {
      "name": "Frank Wania"
    },
    {
      "name": "Xianzhi Peng"
    },
    {
      "name": "Hui Peng",
      "orcid": "0000-0002-2000-1647"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/1w2f69qp6329m.bib",
  "cited_by": 0,
  "doi": "10.26434/chemrxiv.15010212/v1",
  "fulltext_url": null,
  "id": "1w2f69qp6329m",
  "issns": [],
  "landing_url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010212/v1",
  "language": null,
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2304|Per- and polyfluoroalkyl substances research"
    ]
  ],
  "openalex": "W7221446524",
  "pdf_url": "https://chemrxiv.org/doi/pdf/10.26434/chemrxiv.15010212/v1",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-11",
  "publisher": "American Chemical Society (ACS)",
  "ris": "https://research.draconitic.com/paper/1w2f69qp6329m.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:22Z",
      "native_id": "10.26434/chemrxiv.15010212",
      "source": "chemrxiv",
      "url": "https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010212/v1"
    }
  ],
  "subjects": [
    {
      "field": "chemistry",
      "origin": "openalex:2304|Per- and polyfluoroalkyl substances research",
      "subfield": "environmental"
    }
  ],
  "title": "FMS2Lib: A Machine Learning-Ready Tandem Mass Spectral Library for Nontargeted Analysis of Non-conventional PFAS",
  "title_text": "FMS2Lib: A Machine Learning-Ready Tandem Mass Spectral Library for Nontargeted Analysis of Non-conventional PFAS",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:22Z",
  "url": "https://research.draconitic.com/paper/1w2f69qp6329m",
  "venue": "ChemRxiv",
  "versions": []
}