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 Huynh; Griet 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

doi:10.1038/s41597-021-01002-w

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

AIMMS

Research output: Contribution to Journal › Article › Academic › 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 language	English
Article number	223
Pages (from-to)	1-10
Number of pages	10
Journal	Scientific Data
Volume	8
Issue number	1
Early online date	24 Aug 2021
DOIs	https://doi.org/10.1038/s41597-021-01002-w
Publication status	Published - 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:
Copyright 2021 Elsevier B.V., All rights reserved.

UN SDGs

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

Access to Document

10.1038/s41597-021-01002-w

Persistent URL (handle)

Copy link address

Cite this

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., ... Samanipour, S. (2021). Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis. Scientific Data, 8(1), 1-10. Article 223. https://doi.org/10.1038/s41597-021-01002-w

@article{7ef8121226c440808d785dfd66539887,

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

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.",

author = "Bastian Schulze and {van Herwerden}, Denice and Ian Allan and Lubertus Bijlsma and Nestor Etxebarria and Martin Hansen and Sylvain Merel and Branislav Vrana and Reza Aalizadeh and Bernard Bajema and Florian Dubocq and Gianluca Coppola and Aur{\'e}lie Fildier and Pavla Fialov{\'a} and Emil Fr{\o}kj{\ae}r and Roman Grabic and Pablo Gago-Ferrero and Thorsten Gravert and Juliane Hollender and Nina Huynh and Griet Jacobs and Tim Jonkers and Sarit Kaserzon and Marja Lamoree and {Le Roux}, Julien and Teresa Mairinger and Christelle Margoum and Giuseppe Mascolo and Emmanuelle Mebold and Frank Menger and C{\'e}cile Mi{\`e}ge and Jeroen Meijer and R{\'e}gis Moilleron and Sapia Murgolo and Massimo Peruzzo and Martijn Pijnappels and Malcolm Reid and Claudio Roscioli and Coralie Soulier and Sara Valsecchi and Nikolaos Thomaidis and Emmanuelle Vulliet and Robert Young and Saer Samanipour",

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 {\v S}vecov{\'a} for contribution during the sampling. Publisher Copyright: {\textcopyright} 2021, The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",

year = "2021",

month = dec,

doi = "10.1038/s41597-021-01002-w",

language = "English",

volume = "8",

pages = "1--10",

journal = "Scientific Data",

issn = "2052-4463",

publisher = "Nature Publishing Group",

number = "1",

}

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 2021, 'Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis', Scientific Data, vol. 8, no. 1, 223, pp. 1-10. https://doi.org/10.1038/s41597-021-01002-w

TY - JOUR

T1 - Inter-laboratory mass spectrometry dataset based on passive sampling of drinking water for non-target analysis

AU - Schulze, Bastian

AU - van Herwerden, Denice

AU - Allan, Ian

AU - Bijlsma, Lubertus

AU - Etxebarria, Nestor

AU - Hansen, Martin

AU - Merel, Sylvain

AU - Vrana, Branislav

AU - Aalizadeh, Reza

AU - Bajema, Bernard

AU - Dubocq, Florian

AU - Coppola, Gianluca

AU - Fildier, Aurélie

AU - Fialová, Pavla

AU - Frøkjær, Emil

AU - Grabic, Roman

AU - Gago-Ferrero, Pablo

AU - Gravert, Thorsten

AU - Hollender, Juliane

AU - Huynh, Nina

AU - Jacobs, Griet

AU - Jonkers, Tim

AU - Kaserzon, Sarit

AU - Lamoree, Marja

AU - Le Roux, Julien

AU - Mairinger, Teresa

AU - Margoum, Christelle

AU - Mascolo, Giuseppe

AU - Mebold, Emmanuelle

AU - Menger, Frank

AU - Miège, Cécile

AU - Meijer, Jeroen

AU - Moilleron, Régis

AU - Murgolo, Sapia

AU - Peruzzo, Massimo

AU - Pijnappels, Martijn

AU - Reid, Malcolm

AU - Roscioli, Claudio

AU - Soulier, Coralie

AU - Valsecchi, Sara

AU - Thomaidis, Nikolaos

AU - Vulliet, Emmanuelle

AU - Young, Robert

AU - Samanipour, Saer

N1 - 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: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/12

Y1 - 2021/12

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85113330459&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85113330459&partnerID=8YFLogxK

U2 - 10.1038/s41597-021-01002-w

DO - 10.1038/s41597-021-01002-w

M3 - Article

C2 - 34429429

AN - SCOPUS:85113330459

SN - 2052-4463

VL - 8

SP - 1

EP - 10

JO - Scientific Data

JF - Scientific Data

IS - 1

M1 - 223

ER -

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

Abstract

Bibliographical note

UN SDGs

Access to Document

Persistent URL (handle)

Other files and links

Fingerprint

Metadata record for: Inter-laboratory high resolution mass spectrometry dataset based on passive sampling of drinking water for non-target analysis

Cite this

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

Abstract

Bibliographical note

UN SDGs

Access to Document

Persistent URL (handle)

Other files and links

Fingerprint

Datasets

Metadata record for: Inter-laboratory high resolution mass spectrometry dataset based on passive sampling of drinking water for non-target analysis

Cite this