Quantification strategies for two-dimensional liquid chromatography datasets using regions of interest and multivariate curve resolution approaches

In this work, three chemometrics-based approaches are compared for quantification purposes when using two-dimensional liquid chromatography (LC×LC-MS), taking as a study case the quantification of amino acids in commercial drug mixtures. Although the approaches have been already used for one-dimensi...

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Authors: Pérez-Cova, Miriam, Platikanov, Stefan, Tauler, Romà, Jaumot, Joaquim
Format: article
Status:Published version
Publication Date:2022
Country:España
Institution:Consejo Superior de Investigaciones Científicas (CSIC)
Repository:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/275201
Online Access:http://hdl.handle.net/10261/275201
https://api.elsevier.com/content/abstract/scopus_id/85131439500
Access Level:Open access
Keyword:Quantification
Chemometrics
Data analysis
LC×LC
MCR-ALS
ROIMCR
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spelling Quantification strategies for two-dimensional liquid chromatography datasets using regions of interest and multivariate curve resolution approachesPérez-Cova, MiriamPlatikanov, StefanTauler, RomàJaumot, JoaquimQuantificationChemometricsData analysisLC×LCMCR-ALSROIMCRIn this work, three chemometrics-based approaches are compared for quantification purposes when using two-dimensional liquid chromatography (LC×LC-MS), taking as a study case the quantification of amino acids in commercial drug mixtures. Although the approaches have been already used for one-dimensional gas or liquid chromatography, the main novelty of this work is the demonstration of their applicability to LC×LC-MS datasets. Besides, steps such as peak alignment and modelling, commonly applied in this type of data analysis, are not required with the approaches proposed here. In a first step, regions of interest (ROI) strategy is used for the spectral compression of the LC×LC-MS datasets. Then the first strategy consists of building a calibration curve from the areas obtained in this ROI compression step. Alternatively, the ROI intensity matrices can be used as input for a second analysis step employing the multivariate curve resolution alternating least squares (MCR-ALS) method. The main benefit of MCR-ALS is the resolution of elution and spectral profiles for each of the analytes in the mixture, even in the case of strong coelutions and high signal overlapping. Classical MCR-ALS based calibration curve from the peak areas resolved only applying non-negativity constraints (second strategy) is compared to the results obtained when an area correlation constraint is imposed during the ALS optimization (third strategy). All in all, similar quantification results were achieved by the three approaches but, especially in prediction studies, the more accurate quantification is obtained when the calibration curve is built from the peak areas obtained with MCR-ALS when the area correlation constraint is imposed.The research leading to these results have received funding from grants CTQ2017-82598-P and CEX2018-000794-S funded by MCIN/AEI. The authors also want to grant support from the Catalan Agency for Management of University and Research Grants (AGAUR, Grant 2017SGR753). MPC acknowledges a predoctoral FPU 16/02640 scholarship from the Spanish Ministry of Education and Vocational Training (MEFP).Peer reviewedElsevierMinisterio de Ciencia e Innovación (España)0000-0003-4713-94830000-0001-8559-96700000-0003-1461-3273Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202220222022info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/275201https://api.elsevier.com/content/abstract/scopus_id/85131439500reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/MCIN/AEI/CTQ2017-82598-Pinfo:eu-repo/grantAgreement/MCIN/AEI/CEX2018-000794-STalantahttps://doi.org/10.1016/j.talanta.2022.123586Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2752012026-05-22T06:33:51Z
dc.title.none.fl_str_mv Quantification strategies for two-dimensional liquid chromatography datasets using regions of interest and multivariate curve resolution approaches
title Quantification strategies for two-dimensional liquid chromatography datasets using regions of interest and multivariate curve resolution approaches
spellingShingle Quantification strategies for two-dimensional liquid chromatography datasets using regions of interest and multivariate curve resolution approaches
Pérez-Cova, Miriam
Quantification
Chemometrics
Data analysis
LC×LC
MCR-ALS
ROIMCR
title_short Quantification strategies for two-dimensional liquid chromatography datasets using regions of interest and multivariate curve resolution approaches
title_full Quantification strategies for two-dimensional liquid chromatography datasets using regions of interest and multivariate curve resolution approaches
title_fullStr Quantification strategies for two-dimensional liquid chromatography datasets using regions of interest and multivariate curve resolution approaches
title_full_unstemmed Quantification strategies for two-dimensional liquid chromatography datasets using regions of interest and multivariate curve resolution approaches
title_sort Quantification strategies for two-dimensional liquid chromatography datasets using regions of interest and multivariate curve resolution approaches
dc.creator.none.fl_str_mv Pérez-Cova, Miriam
Platikanov, Stefan
Tauler, Romà
Jaumot, Joaquim
author Pérez-Cova, Miriam
author_facet Pérez-Cova, Miriam
Platikanov, Stefan
Tauler, Romà
Jaumot, Joaquim
author_role author
author2 Platikanov, Stefan
Tauler, Romà
Jaumot, Joaquim
author2_role author
author
author
dc.contributor.none.fl_str_mv Ministerio de Ciencia e Innovación (España)
0000-0003-4713-9483
0000-0001-8559-9670
0000-0003-1461-3273
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Quantification
Chemometrics
Data analysis
LC×LC
MCR-ALS
ROIMCR
topic Quantification
Chemometrics
Data analysis
LC×LC
MCR-ALS
ROIMCR
description In this work, three chemometrics-based approaches are compared for quantification purposes when using two-dimensional liquid chromatography (LC×LC-MS), taking as a study case the quantification of amino acids in commercial drug mixtures. Although the approaches have been already used for one-dimensional gas or liquid chromatography, the main novelty of this work is the demonstration of their applicability to LC×LC-MS datasets. Besides, steps such as peak alignment and modelling, commonly applied in this type of data analysis, are not required with the approaches proposed here. In a first step, regions of interest (ROI) strategy is used for the spectral compression of the LC×LC-MS datasets. Then the first strategy consists of building a calibration curve from the areas obtained in this ROI compression step. Alternatively, the ROI intensity matrices can be used as input for a second analysis step employing the multivariate curve resolution alternating least squares (MCR-ALS) method. The main benefit of MCR-ALS is the resolution of elution and spectral profiles for each of the analytes in the mixture, even in the case of strong coelutions and high signal overlapping. Classical MCR-ALS based calibration curve from the peak areas resolved only applying non-negativity constraints (second strategy) is compared to the results obtained when an area correlation constraint is imposed during the ALS optimization (third strategy). All in all, similar quantification results were achieved by the three approaches but, especially in prediction studies, the more accurate quantification is obtained when the calibration curve is built from the peak areas obtained with MCR-ALS when the area correlation constraint is imposed.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022
2022
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/10261/275201
https://api.elsevier.com/content/abstract/scopus_id/85131439500
url http://hdl.handle.net/10261/275201
https://api.elsevier.com/content/abstract/scopus_id/85131439500
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/MCIN/AEI/CTQ2017-82598-P
info:eu-repo/grantAgreement/MCIN/AEI/CEX2018-000794-S
Talanta
https://doi.org/10.1016/j.talanta.2022.123586
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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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score 15,811543

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