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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
  • , Aurelie Fildier
  • , Pavla Fialova
  • , Emil Frokjaer
  • , Roman Grabic
  • , Pablo Gago-Ferrero
  • , Thorsten Gravert
  • , Juliane Hollender
  • , Nina Huynh
  • Griet Jacobs, Tim Jonkers, Sarit Kaserzon, Marja Lamoree, Julien Le Roux, Teresa Mairinger, Christelle Margoum, Giuseppe Mascolo, Emmanuelle Mebold, Frank Menger, Cecile Miege, Jeroen Meijer, Regis Moilleron, Sapia Murgolo, Massimo Peruzzo, Martijn Pijnappels, Malcolm Reid, Claudio Roscioli, Coralie Soulier, Sara Valsecchi, Nikolaos Thomaidis, Emmanuelle Vulliet, Robert Young, Saer Samanipour

Publication: Contribution to journal › Journal article › peer-review

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.
Original languageEnglish
Article number223
Number of pages10
JournalScientific Data
Volume8
Issue number1
DOIs
Publication statusPublished - 2021

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 6 - Clean Water and Sanitation
    SDG 6 Clean Water and Sanitation

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