Improving Regions of Interest Multivariate Curve Resolution: Development of an Empirical Metric System through the Study of Passive Sampling Extracts of Wastewater in Antarctica

The huge potential of liquid chromatography-high-resolution mass spectrometry (LC-HRMS) still comes along with the challenges of data analysis. Regions of interest multivariate curve resolution (ROIMCR) is a valid chemometric tool when working in data-independent acquisition (DIA), since it provides...

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Autores: Benedetti, Barbara, Perez-Lopez, Carlos, MacKeown, Henry, Magi, Emanuele, Tauler, Romà
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/393451
Acceso en línea:http://hdl.handle.net/10261/393451
https://api.elsevier.com/content/abstract/scopus_id/105008399030
Access Level:acceso abierto
Palabra clave:Multivariate Curve Resolution
Wastewater
http://metadata.un.org/sdg/6
http://metadata.un.org/sdg/11
Ensure availability and sustainable management of water and sanitation for all
Make cities and human settlements inclusive, safe, resilient and sustainable
Ensure sustainable consumption and production patterns
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spelling Improving Regions of Interest Multivariate Curve Resolution: Development of an Empirical Metric System through the Study of Passive Sampling Extracts of Wastewater in AntarcticaBenedetti, BarbaraPerez-Lopez, CarlosMacKeown, HenryMagi, EmanueleTauler, RomàMultivariate Curve ResolutionWastewaterhttp://metadata.un.org/sdg/6http://metadata.un.org/sdg/11Ensure availability and sustainable management of water and sanitation for allMake cities and human settlements inclusive, safe, resilient and sustainableEnsure sustainable consumption and production patternsThe huge potential of liquid chromatography-high-resolution mass spectrometry (LC-HRMS) still comes along with the challenges of data analysis. Regions of interest multivariate curve resolution (ROIMCR) is a valid chemometric tool when working in data-independent acquisition (DIA), since it provides a link between precursor and product ions based on chromatographic and spectral profiles. Still, the quality of the ROIMCR models should be carefully evaluated for a consequent reliable annotation of non-target chemicals. The present case study deals with the non-target analysis of extracts coming from passive samplers deployed in a wastewater treatment facility in Antarctica (Italian Research Station). The extracts, derived from polar organic chemical integrative samplers (POCIS), were analyzed by LC-DIA-HRMS/MS, resulting in a rich and complex data set. The use of a fit-for-purpose ROIMCR workflow ended in six models for a total of 770 resolved components; among them, approximately 100 compounds were tentatively identified thanks to the recently developed MSident software, including pharmaceuticals and natural substances. The chemical meaningfulness of all resolved MCR components was carefully checked and rationalized for the first time in a classification system, with 7 classes divided into 3 "goodness levels" (A, B, and C). Level A components were characterized by single chromatographic peaks and mass spectra with a reasonable appearance of precursor and product ions. Level B components presented flaws or anomalies in either the chromatographic or spectral profile, and level C components clearly showed unacceptable features. The percentage of high-quality MCR components (level A) ranged from 15 to 48%, while components of acceptable quality (levels A and B) reached percentages between 65% and 85%. Most annotated compounds were indeed associated with good-quality MCR components. The automatization of the proposed classification system may constitute a powerful additional tool to evaluate MCR models' quality and thus improve the reliability of ROIMCR results when applied to challenging case studies.The authors thank Silvia Lacorte, Roser Chaler Ferrer, and Alexandre Garcia Barrera for the LC-MS technical assistance and Vicky Caponigro for the support in graphics.Peer reviewedAmerican Chemical Society0000-0003-0475-3715Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202520252025info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/393451https://api.elsevier.com/content/abstract/scopus_id/105008399030reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)InglésAnalytical chemistryhttps://doi.org/10.1021/acs.analchem.5c00777Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3934512026-05-22T06:33:51Z
dc.title.none.fl_str_mv Improving Regions of Interest Multivariate Curve Resolution: Development of an Empirical Metric System through the Study of Passive Sampling Extracts of Wastewater in Antarctica
title Improving Regions of Interest Multivariate Curve Resolution: Development of an Empirical Metric System through the Study of Passive Sampling Extracts of Wastewater in Antarctica
spellingShingle Improving Regions of Interest Multivariate Curve Resolution: Development of an Empirical Metric System through the Study of Passive Sampling Extracts of Wastewater in Antarctica
Benedetti, Barbara
Multivariate Curve Resolution
Wastewater
http://metadata.un.org/sdg/6
http://metadata.un.org/sdg/11
Ensure availability and sustainable management of water and sanitation for all
Make cities and human settlements inclusive, safe, resilient and sustainable
Ensure sustainable consumption and production patterns
title_short Improving Regions of Interest Multivariate Curve Resolution: Development of an Empirical Metric System through the Study of Passive Sampling Extracts of Wastewater in Antarctica
title_full Improving Regions of Interest Multivariate Curve Resolution: Development of an Empirical Metric System through the Study of Passive Sampling Extracts of Wastewater in Antarctica
title_fullStr Improving Regions of Interest Multivariate Curve Resolution: Development of an Empirical Metric System through the Study of Passive Sampling Extracts of Wastewater in Antarctica
title_full_unstemmed Improving Regions of Interest Multivariate Curve Resolution: Development of an Empirical Metric System through the Study of Passive Sampling Extracts of Wastewater in Antarctica
title_sort Improving Regions of Interest Multivariate Curve Resolution: Development of an Empirical Metric System through the Study of Passive Sampling Extracts of Wastewater in Antarctica
dc.creator.none.fl_str_mv Benedetti, Barbara
Perez-Lopez, Carlos
MacKeown, Henry
Magi, Emanuele
Tauler, Romà
author Benedetti, Barbara
author_facet Benedetti, Barbara
Perez-Lopez, Carlos
MacKeown, Henry
Magi, Emanuele
Tauler, Romà
author_role author
author2 Perez-Lopez, Carlos
MacKeown, Henry
Magi, Emanuele
Tauler, Romà
author2_role author
author
author
author
dc.contributor.none.fl_str_mv 0000-0003-0475-3715
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Multivariate Curve Resolution
Wastewater
http://metadata.un.org/sdg/6
http://metadata.un.org/sdg/11
Ensure availability and sustainable management of water and sanitation for all
Make cities and human settlements inclusive, safe, resilient and sustainable
Ensure sustainable consumption and production patterns
topic Multivariate Curve Resolution
Wastewater
http://metadata.un.org/sdg/6
http://metadata.un.org/sdg/11
Ensure availability and sustainable management of water and sanitation for all
Make cities and human settlements inclusive, safe, resilient and sustainable
Ensure sustainable consumption and production patterns
description The huge potential of liquid chromatography-high-resolution mass spectrometry (LC-HRMS) still comes along with the challenges of data analysis. Regions of interest multivariate curve resolution (ROIMCR) is a valid chemometric tool when working in data-independent acquisition (DIA), since it provides a link between precursor and product ions based on chromatographic and spectral profiles. Still, the quality of the ROIMCR models should be carefully evaluated for a consequent reliable annotation of non-target chemicals. The present case study deals with the non-target analysis of extracts coming from passive samplers deployed in a wastewater treatment facility in Antarctica (Italian Research Station). The extracts, derived from polar organic chemical integrative samplers (POCIS), were analyzed by LC-DIA-HRMS/MS, resulting in a rich and complex data set. The use of a fit-for-purpose ROIMCR workflow ended in six models for a total of 770 resolved components; among them, approximately 100 compounds were tentatively identified thanks to the recently developed MSident software, including pharmaceuticals and natural substances. The chemical meaningfulness of all resolved MCR components was carefully checked and rationalized for the first time in a classification system, with 7 classes divided into 3 "goodness levels" (A, B, and C). Level A components were characterized by single chromatographic peaks and mass spectra with a reasonable appearance of precursor and product ions. Level B components presented flaws or anomalies in either the chromatographic or spectral profile, and level C components clearly showed unacceptable features. The percentage of high-quality MCR components (level A) ranged from 15 to 48%, while components of acceptable quality (levels A and B) reached percentages between 65% and 85%. Most annotated compounds were indeed associated with good-quality MCR components. The automatization of the proposed classification system may constitute a powerful additional tool to evaluate MCR models' quality and thus improve the reliability of ROIMCR results when applied to challenging case studies.
publishDate 2025
dc.date.none.fl_str_mv 2025
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/393451
https://api.elsevier.com/content/abstract/scopus_id/105008399030
url http://hdl.handle.net/10261/393451
https://api.elsevier.com/content/abstract/scopus_id/105008399030
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Analytical chemistry
https://doi.org/10.1021/acs.analchem.5c00777
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv American Chemical Society
publisher.none.fl_str_mv American Chemical Society
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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