{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T19:41:09Z","timestamp":1792784469573,"version":"4.5.0"},"reference-count":55,"publisher":"American Chemical Society (ACS)","issue":"40","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-045"}],"funder":[{"DOI":"10.13039\/100010230","name":"Queensland Health","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100010230","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000925","name":"National Health and Medical Research Council","doi-asserted-by":"publisher","award":["EL1 2009209"],"award-info":[{"award-number":["EL1 2009209"]}],"id":[{"id":"10.13039\/501100000925","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100016056","name":"Minderoo Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100016056","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Data Science Center, Universiteit van Amsterdam","id":[{"id":"https:\/\/ror.org\/04dkp9463","id-type":"ROR","asserted-by":"crossref"}]},{"name":"Topconsortium voor Kennis en Innovatie, ChemistryNL"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,10,13]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The vast and expanding chemical universe contains hundreds of thousands of substances, yet experimental data on their environmental persistence are available for only a small fraction (&amp;lt;3%). This gap limits our ability to identify persistent organic contaminants. Models have been developed to predict the persistence for already identified chemicals, but no models are able to prioritize unknown features detected in high-resolution mass spectrometry (HRMS). Here, we present two complementary machine-learning models that connect molecular structure and tandem mass-spectrometry fragmentation behavior to ready biodegradability. The first integrates molecular fingerprints with synthetic and retrosynthetic accessibility and molecular complexity metrics, achieving a cross-validated balanced accuracy of 88% for 4,840 curated chemicals. The second model extends predictions to unknowns by using cumulative neutral losses (CNLs) derived from HRMS\/MS data and trained in a semisupervised manner guided by the structure-based model, reaching 82% balanced accuracy. Analysis of key CNLs revealed fragmentation patterns linked to biodegradability, such as hydrolytic- and halogen-related losses. Together, these models provide a data-driven approach to prioritize persistent known and unknown chemicals in complex environmental samples. When applied to HRMS\/MS data from sewage-biodegradation reactors, the models captured trends consistent with observed degradation behavior, with readily biodegradable compounds decreasing in abundance and persistent compounds remaining stable over time.<\/jats:p>","DOI":"10.1021\/acs.analchem.6c02844","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T11:02:01Z","timestamp":1790161321000},"page":"29228-29240","source":"Crossref","is-referenced-by-count":0,"title":["LC-HRMS Known-\nand Unknown-Feature Prioritization\nBased on Machine-Learning-Predicted Ready Biodegradability"],"prefix":"10.1021","volume":"98","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-5439-9702","authenticated-orcid":true,"given":"Tobias","family":"Hulleman","sequence":"first","affiliation":[{"name":"Queensland Alliance for Environmental Health Sciences (QAEHS) , 20 Cornwall Street , Woolloongabba, , ,","place":["Brisbane, Queensland, Australia, 4102"]},{"name":"ARC Training Centre for Hyphenated Analytical Separation Technologies (HyTECH), Queensland Alliance for Environmental Health Sciences (QAEHS) , 20 Cornwall Street , Woolloongabba, , ,","place":["Brisbane, Queensland, Australia, 4102"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8270-6979","authenticated-orcid":true,"given":"Saer","family":"Samanipour","sequence":"additional","affiliation":[{"name":"Queensland Alliance for Environmental Health Sciences (QAEHS) , 20 Cornwall Street , Woolloongabba, , ,","place":["Brisbane, Queensland, Australia, 4102"]},{"name":"University of Amsterdam , , ,","place":["Amsterdam, the Netherlands, 1090 GD"]},{"name":"University of Amsterdam , , ,","place":["Amsterdam, the Netherlands, 1090 GD"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7153-2333","authenticated-orcid":true,"given":"Viktoriia","family":"Turkina","sequence":"additional","affiliation":[{"name":"University of Amsterdam , , ,","place":["Amsterdam, the Netherlands, 1090 GD"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7703-7915","authenticated-orcid":true,"given":"Jinglong","family":"Li","sequence":"additional","affiliation":[{"name":"Queensland Alliance for Environmental Health Sciences (QAEHS) , 20 Cornwall Street , Woolloongabba, , ,","place":["Brisbane, Queensland, Australia, 4102"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2543-9023","authenticated-orcid":true,"given":"Cassandra","family":"Rauert","sequence":"additional","affiliation":[{"name":"Queensland Alliance for Environmental Health Sciences (QAEHS) , 20 Cornwall Street , Woolloongabba, , ,","place":["Brisbane, Queensland, Australia, 4102"]},{"name":"ARC Training Centre for Hyphenated Analytical Separation Technologies (HyTECH), Queensland Alliance for Environmental Health Sciences (QAEHS) , 20 Cornwall Street , Woolloongabba, , ,","place":["Brisbane, Queensland, Australia, 4102"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8773-9761","authenticated-orcid":true,"given":"Elvis D.","family":"Okoffo","sequence":"additional","affiliation":[{"name":"Queensland Alliance for Environmental Health Sciences (QAEHS) , 20 Cornwall Street , Woolloongabba, , ,","place":["Brisbane, Queensland, Australia, 4102"]},{"name":"ARC Training Centre for Hyphenated Analytical Separation Technologies (HyTECH), Queensland Alliance for Environmental Health Sciences (QAEHS) , 20 Cornwall Street , Woolloongabba, , ,","place":["Brisbane, Queensland, Australia, 4102"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2155-100X","authenticated-orcid":true,"given":"Kevin V.","family":"Thomas","sequence":"additional","affiliation":[{"name":"Queensland Alliance for Environmental Health Sciences (QAEHS) , 20 Cornwall Street , Woolloongabba, , ,","place":["Brisbane, Queensland, Australia, 4102"]},{"name":"ARC Training Centre for Hyphenated Analytical Separation Technologies (HyTECH), Queensland Alliance for Environmental Health Sciences (QAEHS) , 20 Cornwall Street , Woolloongabba, , ,","place":["Brisbane, Queensland, Australia, 4102"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9336-9656","authenticated-orcid":true,"given":"Jake W.","family":"O\u2019Brien","sequence":"additional","affiliation":[{"name":"Queensland Alliance for Environmental Health Sciences (QAEHS) , 20 Cornwall Street , Woolloongabba, , ,","place":["Brisbane, Queensland, Australia, 4102"]},{"name":"ARC Training Centre for Hyphenated Analytical Separation Technologies (HyTECH), Queensland Alliance for Environmental Health Sciences (QAEHS) , 20 Cornwall Street , Woolloongabba, , ,","place":["Brisbane, Queensland, Australia, 4102"]},{"name":"University of Amsterdam , , ,","place":["Amsterdam, the Netherlands, 1090 GD"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"316","published-online":{"date-parts":[[2026,9,23]]},"reference":[{"key":"2026101308065289300_cit1","doi-asserted-by":"publisher","first-page":"2575","DOI":"10.1021\/acs.est.9b06379","article-title":"Toward a Global\nUnderstanding of Chemical Pollution: A First Comprehensive Analysis\nof National and Regional Chemical Inventories","volume":"54","author":"Wang","year":"2020","journal-title":"Environ. Sci. Technol."},{"key":"2026101308065289300_cit2","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1146\/annurev-environ-111523-102318","article-title":"The State of the World\u2019s Chemical Pollution","volume":"50","author":"Scheringer","year":"2025","journal-title":"Annu. Rev. Environ. Resour."},{"key":"2026101308065289300_cit3","unstructured":"Programme, U. N. E.\n          \n          Global Chemicals\nOutlook II \u2013 From Legacies to Innovative Solutions: Implementing\nthe 2030 Agenda for Sustainable Development - Synthesis Report; 2019; https:\/\/wedocs.unep.org\/20.500.11822\/27651."},{"key":"2026101308065289300_cit4","doi-asserted-by":"publisher","first-page":"3093","DOI":"10.1021\/acs.jcim.3c02050","article-title":"Predicting\nthe Activity of Unidentified\nChemicals in Complementary Bioassays from the HRMS Data to Pinpoint\nPotential Endocrine Disruptors","volume":"64","author":"Rahu","year":"2024","journal-title":"J. Chem. Inf.\nModel."},{"key":"2026101308065289300_cit5","doi-asserted-by":"publisher","first-page":"18067","DOI":"10.1021\/acs.est.3c00304","article-title":"Machine Learning-Based Hazard-Driven Prioritization\nof Features in Nontarget Screening of Environmental High-Resolution\nMass Spectrometry Data","volume":"57","author":"Arturi","year":"2023","journal-title":"Environ. Sci. Technol."},{"issue":"1","key":"2026101308065289300_cit6","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1186\/s12302-024-00919-4","article-title":"Grouping\nstrategies for assessing and managing persistent and mobile substances","volume":"36","author":"Chirsir","year":"2024","journal-title":"Environ. Sci. Eur."},{"key":"2026101308065289300_cit7","volume-title":"Handbook of Environmental\nDegradation Rates","author":"Howard","year":"1992"},{"issue":"1","key":"2026101308065289300_cit8","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1186\/s13321-018-0263-1","article-title":"OPERA models\nfor\npredicting physicochemical properties and environmental fate endpoints","volume":"10","author":"Mansouri","year":"2018","journal-title":"J. Cheminf."},{"key":"2026101308065289300_cit9","first-page":"78","volume-title":"UNEP Yearbook:\nEmerging Issues in Our Global Environment 2013","author":"United Nations Environment Programme (UNEP).","year":"2013"},{"key":"2026101308065289300_cit10","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1002\/etc.5620110502","article-title":"Predictive\nmodel for aerobic biodegradability\ndeveloped from a file of evaluated biodegradation data","volume":"11","author":"Howard","year":"1992","journal-title":"Environ. Toxicol. Chem."},{"key":"2026101308065289300_cit11","doi-asserted-by":"publisher","first-page":"459","DOI":"10.1021\/es00052a018","article-title":"Group Contribution Method for Predicting Probability\nand Rate of Aerobic Biodegradation","volume":"28","author":"Boethling","year":"1994","journal-title":"Environ.\nSci. Technol."},{"key":"2026101308065289300_cit12","doi-asserted-by":"publisher","first-page":"1763","DOI":"10.1002\/etc.5620180822","article-title":"Prediction of Biodegradability from\nChemical Structure: Modeling of Ready Biodegradation Test Data","volume":"18","author":"Loonen","year":"1999","journal-title":"Environ. Toxicol. Chem."},{"key":"2026101308065289300_cit13","doi-asserted-by":"publisher","first-page":"2478","DOI":"10.1002\/etc.5620191013","article-title":"Predicting\nReady Biodegradability in the Japanese Ministry of International Trade\nand Industry Test","volume":"19","author":"Tunkel","year":"2000","journal-title":"Environ. Toxicol. Chem."},{"key":"2026101308065289300_cit14","doi-asserted-by":"publisher","first-page":"655","DOI":"10.1021\/ci200622d","article-title":"In silico assessment\nof chemical\nbiodegradability","volume":"52","author":"Cheng","year":"2012","journal-title":"J. Chem. Inf. Model."},{"key":"2026101308065289300_cit15","doi-asserted-by":"publisher","first-page":"867","DOI":"10.1021\/ci4000213","article-title":"Quantitative structure-activity relationship\nmodels\nfor ready biodegradability of chemicals","volume":"53","author":"Mansouri","year":"2013","journal-title":"J.\nChem. Inf. Model."},{"key":"2026101308065289300_cit16","doi-asserted-by":"publisher","first-page":"2515","DOI":"10.1021\/ci500323t","article-title":"Prediction of chemical biodegradability using support\nvector classifier optimized with differential evolution","volume":"54","author":"Cao","year":"2014","journal-title":"J. Chem. Inf. Model."},{"key":"2026101308065289300_cit17","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.chemosphere.2014.02.073","article-title":"A new in silico classification model\nfor ready biodegradability, based on molecular fragments","volume":"108","author":"Lombardo","year":"2014","journal-title":"Chemosphere"},{"key":"2026101308065289300_cit18","doi-asserted-by":"publisher","first-page":"4224","DOI":"10.1021\/acssuschemeng.6b00717","article-title":"Biodegradability Prediction of Fragrant\nMolecules by Molecular Topology","volume":"4","author":"Blay","year":"2016","journal-title":"ACS Sustainable\nChem. Eng."},{"key":"2026101308065289300_cit19","doi-asserted-by":"publisher","first-page":"1277","DOI":"10.1080\/08927022.2017.1328556","article-title":"Prediction of chemical\nbiodegradability using computational methods","volume":"43","author":"Zhan","year":"2017","journal-title":"Mol. Simul."},{"key":"2026101308065289300_cit20","doi-asserted-by":"publisher","first-page":"171","DOI":"10.1080\/1062936X.2019.1697360","article-title":"Modelling of ready biodegradability\nbased on combined public and industrial data sources","volume":"31","author":"Lunghini","year":"2020","journal-title":"SAR QSAR Environ. Res."},{"key":"2026101308065289300_cit21","doi-asserted-by":"publisher","first-page":"12755","DOI":"10.1021\/acs.est.2c01764","article-title":"Classification and\nRegression Machine Learning Models for Predicting\nAerobic Ready and Inherent Biodegradation of Organic Chemicals in\nWater","volume":"56","author":"Huang","year":"2022","journal-title":"Environ. Sci. Technol."},{"key":"2026101308065289300_cit22","doi-asserted-by":"publisher","first-page":"617","DOI":"10.1021\/acs.chemrestox.2c00330","article-title":"Prediction and Structure-Activity\nRelationship Analysis on Ready Biodegradability of Chemical Using\nMachine Learning Method","volume":"36","author":"Yin","year":"2023","journal-title":"Chem. Res. Toxicol."},{"key":"2026101308065289300_cit23","doi-asserted-by":"publisher","first-page":"1780","DOI":"10.1039\/D4EM00431K","article-title":"Critical insights\ninto data curation and label noise for accurate prediction of aerobic\nbiodegradability of organic chemicals","volume":"26","author":"Ko\u0308rner","year":"2024","journal-title":"Environ.\nSci.: processes Impacts"},{"key":"2026101308065289300_cit24","volume-title":"OECD Guideline for Testing of Chemicals No. 307: aerobic\nand Anaerobic Transformation in Soil","author":"Organisation for Economic Co-operation and Development (OECD).","year":"2000"},{"key":"2026101308065289300_cit25","volume-title":"OECD Guideline for Testing of Chemicals No. 308: aerobic\nand Anaerobic Transformation in Aquatic Sediment Systems","author":"Organisation for Economic Co-operation and Development (OECD).","year":"2002"},{"key":"2026101308065289300_cit26","volume-title":"OECD Guideline For Testing Of Chemicals No. 309: aerobic\nMineralisation In Surface Water \u2013 Simulation Biodegradation\nTest","author":"Organisation for Economic Co-operation and Development (OECD).","year":"2004"},{"key":"2026101308065289300_cit27","unstructured":"U.S. Environmental Protection Agency.\n          \n          Estimation\nPrograms Interface (EPI) Suite for Microsoft\u00ae Windows, v4.11; https:\/\/www.epa.gov\/tsca-screening-tools\/epi-suitetm-estimation-program-interface, 2000 (accessed: 2025\u201307\u201321)."},{"key":"2026101308065289300_cit28","unstructured":"OECD.\n          \n          QSAR Assessment Framework: Guidance\nfor the\nregulatory assessment of (Quantitative) Structure Activity Relationship\nmodels and predictions; 2023, https:\/\/www.oecd.org\/en\/publications\/q-sar-assessment-framework-guidance-for-the-regulatory-assessment-of-quantitative-structure-activity-relationship-models-and-predictions_d96118f6-en.html."},{"key":"2026101308065289300_cit29","doi-asserted-by":"publisher","first-page":"190","DOI":"10.1080\/10643389.2024.2396690","article-title":"Bridging\nknowledge gaps in human chemical exposure\nvia drinking water with non-target screening","volume":"55","author":"Ciccarelli","year":"2025","journal-title":"Crit. Rev. Environ. Sci. Technol."},{"key":"2026101308065289300_cit30","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1021\/acsenvironau.2c00024","article-title":"Assessing the Persistence\nand Mobility of Organic Substances\nto Protect Freshwater Resources","volume":"2","author":"Arp","year":"2022","journal-title":"ACS Environ.\nAu"},{"issue":"1","key":"2026101308065289300_cit31","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1186\/1758-2946-1-8","article-title":"Estimation\nof synthetic accessibility score of drug-like\nmolecules based on molecular complexity and fragment contributions","volume":"1","author":"Ertl","year":"2009","journal-title":"J. Cheminf."},{"key":"2026101308065289300_cit32","doi-asserted-by":"publisher","first-page":"1868","DOI":"10.1111\/j.1462-2920.2005.00966.x","article-title":"Bacterial Degradation\nof Xenobiotic Compounds: Evolution and Distribution of Novel Enzyme\nActivities","volume":"7","author":"Janssen","year":"2005","journal-title":"Environ. Microbiol."},{"key":"2026101308065289300_cit33","volume-title":"Biodegradation and Bioremediation","author":"Alexander","year":"1999","edition":"2nd"},{"key":"2026101308065289300_cit34","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1002\/etc.5620030405","article-title":"Interpreting Results from Biodegradability Tests of\nChemicals in Water and Soil","volume":"3","author":"Howard","year":"1984","journal-title":"Environ. Toxicol.\nChem."},{"key":"2026101308065289300_cit35","doi-asserted-by":"publisher","first-page":"12247","DOI":"10.1021\/acs.analchem.3c00896","article-title":"Cumulative Neutral Loss Model for\nFragment Deconvolution in Electrospray Ionization High-Resolution\nMass Spectrometry Data","volume":"95","author":"van Herwerden","year":"2023","journal-title":"Anal. Chem."},{"key":"2026101308065289300_cit36","doi-asserted-by":"publisher","first-page":"8004","DOI":"10.1021\/acs.est.4c13026","article-title":"Prioritization of Unknown LC-HRMS\nFeatures Based on\nPredicted Toxicity Categories","volume":"59","author":"Turkina","year":"2025","journal-title":"Environ. Sci.\nTechnol."},{"key":"2026101308065289300_cit37","doi-asserted-by":"publisher","first-page":"3339","DOI":"10.1039\/D0SC05401A","article-title":"Retrosynthetic\naccessibility score (RAscore)-rapid machine learned synthesizability\nclassification from AI driven retrosynthetic planning","volume":"12","author":"Thakkar","year":"2021","journal-title":"Chem. Sci."},{"issue":"1","key":"2026101308065289300_cit38","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1186\/s13321-020-00472-1","article-title":"AiZynthFinder: a fast, robust and flexible open-source\nsoftware for retrosynthetic planning","volume":"12","author":"Genheden","year":"2020","journal-title":"J. Cheminf."},{"key":"2026101308065289300_cit39","doi-asserted-by":"publisher","first-page":"8405","DOI":"10.1021\/acs.jcim.5c00334","article-title":"A View on Molecular\nComplexity from the GDB Chemical\nSpace","volume":"65","author":"Buehler","year":"2025","journal-title":"J. Chem. Inf. Model."},{"key":"2026101308065289300_cit40","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1021\/ci010132r","article-title":"Reoptimization of\nMDL keys for use in drug discovery","volume":"42","author":"Durant","year":"2002","journal-title":"J. Chem.\nInf. Comput. Sci."},{"key":"2026101308065289300_cit41","doi-asserted-by":"publisher","first-page":"155","DOI":"10.1177\/026119290503300209","article-title":"Current Status of Methods for Defining the ApplicabilityDomain of\n(Quantitative) Structure\u2013Activity Relationships","volume":"33","author":"Netzeva","year":"2005","journal-title":"Altern. Lab. Anim."},{"key":"2026101308065289300_cit42","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1177\/026119290503300508","article-title":"QSAR applicability\ndomain estimation\nby projection of the training set in descriptor space","volume":"33","author":"Jaworska","year":"2005","journal-title":"Altern. Lab. Anim."},{"key":"2026101308065289300_cit43","unstructured":"OECD.\n          \n          Guidance Document on the Validation\nof (Q)SAR\nModels; 2014, https:\/\/www.oecd.org\/en\/publications\/guidance-document-on-the-validation-of-quantitative-structure-activity-relationship-q-sar-models_9789264085442-en.html."},{"key":"2026101308065289300_cit44","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1002\/qsar.200610151","article-title":"Principles\nof QSAR model validation: internal and external","volume":"26","author":"Gramatica","year":"2007","journal-title":"QSAR Comb. Sci."},{"key":"2026101308065289300_cit45","doi-asserted-by":"publisher","first-page":"17796","DOI":"10.1021\/acs.est.5c04421","article-title":"Stability\nof Antibiotics and Their Metabolites in Sewers: Implications for Wastewater-Based\nEpidemiology and Antibiotic Sustainability","volume":"59","author":"Li","year":"2025","journal-title":"Environ. Sci. Technol."},{"key":"2026101308065289300_cit46","doi-asserted-by":"publisher","first-page":"3816","DOI":"10.1021\/acs.est.6b02755","article-title":"Impact of in-Sewer\nDegradation of Pharmaceutical and Personal Care Products (PPCPs) Population\nMarkers on a Population Model","volume":"51","author":"O\u2019brien","year":"2017","journal-title":"Environ. Sci.\nTechnol."},{"key":"2026101308065289300_cit47","doi-asserted-by":"publisher","first-page":"10800","DOI":"10.1021\/acs.analchem.9b02422","article-title":"Self adjusting algorithm\nfor the nontargeted feature detection of high resolution mass spectrometry\ncoupled with liquid chromatography profile data","volume":"91","author":"Samanipour","year":"2019","journal-title":"Anal. Chem."},{"key":"2026101308065289300_cit48","doi-asserted-by":"publisher","first-page":"104515","DOI":"10.1016\/j.chemolab.2022.104515","article-title":"Naive Bayes classification model\nfor isotopologue detection in LC-HRMS data","volume":"223","author":"van Herwerden","year":"2022","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"2026101308065289300_cit49","doi-asserted-by":"publisher","first-page":"4694","DOI":"10.1021\/acs.est.8b00259","article-title":"Combining a Deconvolution\nand a Universal Library Search Algorithm for the Nontarget Analysis\nof Data-Independent Acquisition Mode Liquid Chromatography-High-Resolution\nMass Spectrometry Results","volume":"52","author":"Samanipour","year":"2018","journal-title":"Environ. Sci. 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