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

Bastian Schulze, Denice van Herwerden, Ian Allan, Lubertus Bijlsma, Nestor Etxebarria, Martin Hansen, Sylvain Merel, Branislav Vrana, Reza Aalizadeh, Bernard Bajema, Florian Dubocq, Gianluca Coppola, Aurélie Fildier, Pavla Fialová, Emil Frøkjær, Roman Grabic, Pablo Gago-Ferrero, Thorsten Gravert, Juliane Hollender, Nina HuynhGriet Jacobs, Tim Jonkers, Sarit Kaserzon, Marja Lamoree, Julien Le Roux, Teresa Mairinger, Christelle Margoum, Giuseppe Mascolo, Emmanuelle Mebold, Frank Menger, Cécile Miège, Jeroen Meijer, Régis Moilleron, Sapia Murgolo, Massimo Peruzzo, Martijn Pijnappels, Malcolm Reid, Claudio Roscioli, Coralie Soulier, Sara Valsecchi, Nikolaos Thomaidis, Emmanuelle Vulliet, Robert Young, Saer Samanipour*

*Corresponding author for this work

Research output: Contribution to JournalArticleAcademicpeer-review


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.

Original languageEnglish
Article number223
Pages (from-to)1-10
Number of pages10
JournalScientific Data
Issue number1
Early online date24 Aug 2021
Publication statusPublished - Dec 2021

Bibliographical note

Funding Information:
This work was supported by the NORMAN network. MH acknowledge funding from the Danish Environmental Protection Agency (MST-667-00207) and The Aarhus University Research Foundation (AUFF-T-2017-FLS-7-4). The authors are also thankful to Biotage for providing the passive samplers and Helena Švecová for contribution during the sampling.

Publisher Copyright:
© 2021, The Author(s).

Copyright 2021 Elsevier B.V., All rights reserved.


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