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Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis.

Authors :
Schulze B
van Herwerden D
Allan I
Bijlsma L
Etxebarria N
Hansen M
Merel S
Vrana B
Aalizadeh R
Bajema B
Dubocq F
Coppola G
Fildier A
Fialová P
Frøkjær E
Grabic R
Gago-Ferrero P
Gravert T
Hollender J
Huynh N
Jacobs G
Jonkers T
Kaserzon S
Lamoree M
Le Roux J
Mairinger T
Margoum C
Mascolo G
Mebold E
Menger F
Miège C
Meijer J
Moilleron R
Murgolo S
Peruzzo M
Pijnappels M
Reid M
Roscioli C
Soulier C
Valsecchi S
Thomaidis N
Vulliet E
Young R
Samanipour S
Source :
Scientific data [Sci Data] 2021 Aug 24; Vol. 8 (1), pp. 223. Date of Electronic Publication: 2021 Aug 24.
Publication Year :
2021

Abstract

Non-target analysis (NTA) employing high-resolution mass spectrometry is a commonly applied approach for the detection of novel chemicals of emerging concern in complex environmental samples. NTA typically results in large and information-rich datasets that require computer aided (ideally automated) strategies for their processing and interpretation. Such strategies do however raise the challenge of reproducibility between and within different processing workflows. An effective strategy to mitigate such problems is the implementation of inter-laboratory studies (ILS) with the aim to evaluate different workflows and agree on harmonized/standardized quality control procedures. Here we present the data generated during such an ILS. This study was organized through the Norman Network and included 21 participants from 11 countries. A set of samples based on the passive sampling of drinking water pre and post treatment was shipped to all the participating laboratories for analysis, using one pre-defined method and one locally (i.e. in-house) developed method. The data generated represents a valuable resource (i.e. benchmark) for future developments of algorithms and workflows for NTA experiments.<br /> (© 2021. The Author(s).)

Details

Language :
English
ISSN :
2052-4463
Volume :
8
Issue :
1
Database :
MEDLINE
Journal :
Scientific data
Publication Type :
Academic Journal
Accession number :
34429429
Full Text :
https://doi.org/10.1038/s41597-021-01002-w