Towards a harmonized identification scoring system in LC-HRMS/MS based non-target screening (NTS) of emerging contaminants

Autor: Nikiforos Alygizakis, Francois Lestremau, Pablo Gago-Ferrero, Rubén Gil-Solsona, Katarzyna Arturi, Juliane Hollender, Emma L. Schymanski, Valeria Dulio, Jaroslav Slobodnik, Nikolaos S. Thomaidis
Přispěvatelé: National and Kapodistrian University of Athens (NKUA), Environmental Institute Kos, Institut National de l'Environnement Industriel et des Risques (INERIS), IMT Mines Alès - ERT (ERT), IMT - MINES ALES (IMT - MINES ALES), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Hydrosciences Montpellier (HSM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM), CONTEM: Contaminats Emergents (CONTEM), Hydrosciences Montpellier (HSM), Institute of Environmental Assessment and Water Research (IDAEA), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Swiss Federal Insitute of Aquatic Science and Technology [Dübendorf] (EAWAG), Institute of Biogeochemistry and Pollutant Dynamics [ETH Zürich] (IBP), Department of Environmental Systems Science [ETH Zürich] (D-USYS), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich)- Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Luxembourg Centre For Systems Biomedicine (LCSB), University of Luxembourg [Luxembourg], PGF acknowledges his Ramon y Cajal fellowship (RYC2019-027913-I) from the AEI-MICI. ELS is supported by the Luxembourg National Research Fund (FNR) for project A18/BM/12341006.
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Trends in Analytical Chemistry
Trends in Analytical Chemistry, 2023, 159, pp.116944. ⟨10.1016/j.trac.2023.116944⟩
ISSN: 0165-9936
DOI: 10.1016/j.trac.2023.116944⟩
Popis: Non-target screening (NTS) methods are rapidly gaining in popularity, empowering researchers to search for an ever-increasing number of chemicals. Given this possibility, communicating the confidence of identification in an automated, concise and unambiguous manner is becoming increasingly important. In this study, we compiled several pieces of evidence necessary for communicating NTS identification confidence and developed a machine learning approach for classification of the identifications as reliable and unreliable. The machine learning approach was trained using data generated by four laboratories equipped with different instrumentation. The model discarded substances with insufficient identification evidence efficiently, while revealing the relevance of different parameters for identification. Based on these results, a harmonized IP-based system is proposed. This new NTS-oriented system is compatible with the currently widely used five level system. It increases the precision in reporting and the reproducibility of current approaches via the inclusion of evidence scores, while being suitable for automation.
PGF acknowledges his Ramon y Cajal fellowship (RYC2019-027913-I) from the AEI-MICI. ELS is supported by the Luxembourg National Research Fund (FNR) for project A18/BM/12341006.
Databáze: OpenAIRE
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