Article (Scientific journals)
Towards a harmonized identification scoring system in LC-HRMS/MS based non-target screening (NTS) of emerging contaminants
Alygizakis, Nikiforos; Lestremau, Francois; Gago-Ferrero, Pablo et al.
2023In TrAC: Trends in Analytical Chemistry, 159, p. 116944
Peer reviewed
 

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Abstract :
[en] 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.
Disciplines :
Environmental sciences & ecology
Author, co-author :
Alygizakis, Nikiforos
Lestremau, Francois
Gago-Ferrero, Pablo
Gil-Solsona, Rubén
Arturi, Katarzyna
Hollender, Juliane
Schymanski, Emma  ;  University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Dulio, Valeria
Slobodnik, Jaroslav
Thomaidis, Nikolaos S.
External co-authors :
yes
Language :
English
Title :
Towards a harmonized identification scoring system in LC-HRMS/MS based non-target screening (NTS) of emerging contaminants
Publication date :
2023
Journal title :
TrAC: Trends in Analytical Chemistry
ISSN :
0165-9936
Volume :
159
Pages :
116944
Peer reviewed :
Peer reviewed
Focus Area :
Computational Sciences
FnR Project :
FNR12341006 - Environmental Cheminformatics To Identify Unknown Chemicals And Their Effects, 2018 (01/10/2018-30/09/2023) - Emma Schymanski
Available on ORBilu :
since 27 January 2023

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