Untangling comprehensive two-dimensional liquid chromatography data sets using regions of interest and multivariate curve resolution approaches
Data analysis remains a major challenge in the global application of comprehensive two-dimensional liquid chromatography (LC × LC). Advanced chemometric tools have been proposed to reduce the complexity of LC × LC datasets. In this work, key aspects of LC × LC are summarized from a chemometrics perspective. In particular, the recently developed ROIMCR method is proposed and adapted for LC × LC data analysis. First, this strategy consists of selecting of the Regions of Interest (ROI), in which data are filtered and compressed. Second, the resolution of the elution profiles of the sample constituents using the Multivariate Curve Resolution – Alternating Least Squares (MCR-ALS) method. A detailed overview of this recently developed tool and examples of its application in LC × LC are given, as well as pre-processing and post-processing tools to facilitate and complement the analysis of LC × LC data and the optimal interpretation of results.
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Elsevier
2021-04-01
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Subjects: | ROIMCR, Chemometrics, Data analysis, LC × LC, MCR-ALS, |
Online Access: | http://hdl.handle.net/10261/266228 http://dx.doi.org/10.13039/501100004837 https://api.elsevier.com/content/abstract/scopus_id/85101469591 |
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dig-idaea-es-10261-2662282024-05-16T20:46:18Z Untangling comprehensive two-dimensional liquid chromatography data sets using regions of interest and multivariate curve resolution approaches Pérez-Cova, Miriam Jaumot, Joaquim Tauler, Romà Ministerio de Ciencia e Innovación (España) ROIMCR Chemometrics Data analysis LC × LC MCR-ALS Data analysis remains a major challenge in the global application of comprehensive two-dimensional liquid chromatography (LC × LC). Advanced chemometric tools have been proposed to reduce the complexity of LC × LC datasets. In this work, key aspects of LC × LC are summarized from a chemometrics perspective. In particular, the recently developed ROIMCR method is proposed and adapted for LC × LC data analysis. First, this strategy consists of selecting of the Regions of Interest (ROI), in which data are filtered and compressed. Second, the resolution of the elution profiles of the sample constituents using the Multivariate Curve Resolution – Alternating Least Squares (MCR-ALS) method. A detailed overview of this recently developed tool and examples of its application in LC × LC are given, as well as pre-processing and post-processing tools to facilitate and complement the analysis of LC × LC data and the optimal interpretation of results. The research leading to these results has received funding from the Spanish Ministry of Science and Innovation (MCI, Grants CTQ2017-82598-P and PID2019-105732GB-C21). The authors also want to grant support from the Catalan Agency for Management of University and Research Grants (AGAUR, Grant 2017SGR753) and the Spanish MCI (Severo Ochoa Project CEX2018-000794-S). MPC acknowledges a predoctoral FPU 16/02640 scholarship from the Spanish Ministry of Education and Vocational Training (MEFP). Peer reviewed 2022-04-05T05:52:51Z 2022-04-05T05:52:51Z 2021-04-01 artículo http://purl.org/coar/resource_type/c_6501 TrAC Trends in Analytical Chemistry 137: 116207 (2021) 01659936 http://hdl.handle.net/10261/266228 10.1016/j.trac.2021.116207 http://dx.doi.org/10.13039/501100004837 2-s2.0-85101469591 https://api.elsevier.com/content/abstract/scopus_id/85101469591 en #PLACEHOLDER_PARENT_METADATA_VALUE# #PLACEHOLDER_PARENT_METADATA_VALUE# #PLACEHOLDER_PARENT_METADATA_VALUE# info:eu-repo/grantAgreement/EC/MCIN/ CTQ2017-82598-P info:eu-repo/grantAgreement/EC/MCIN/PID2019-105732GB-C21 info:eu-repo/grantAgreement/EC/MCIN/CEX2018-000794-S TrAC - Trends in Analytical Chemistry Postprint https://doi.org/10.1016/j.trac.2021.116207 Sí open Elsevier |
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ROIMCR Chemometrics Data analysis LC × LC MCR-ALS ROIMCR Chemometrics Data analysis LC × LC MCR-ALS |
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ROIMCR Chemometrics Data analysis LC × LC MCR-ALS ROIMCR Chemometrics Data analysis LC × LC MCR-ALS Pérez-Cova, Miriam Jaumot, Joaquim Tauler, Romà Untangling comprehensive two-dimensional liquid chromatography data sets using regions of interest and multivariate curve resolution approaches |
description |
Data analysis remains a major challenge in the global application of comprehensive two-dimensional liquid chromatography (LC × LC). Advanced chemometric tools have been proposed to reduce the complexity of LC × LC datasets. In this work, key aspects of LC × LC are summarized from a chemometrics perspective. In particular, the recently developed ROIMCR method is proposed and adapted for LC × LC data analysis. First, this strategy consists of selecting of the Regions of Interest (ROI), in which data are filtered and compressed. Second, the resolution of the elution profiles of the sample constituents using the Multivariate Curve Resolution – Alternating Least Squares (MCR-ALS) method. A detailed overview of this recently developed tool and examples of its application in LC × LC are given, as well as pre-processing and post-processing tools to facilitate and complement the analysis of LC × LC data and the optimal interpretation of results. |
author2 |
Ministerio de Ciencia e Innovación (España) |
author_facet |
Ministerio de Ciencia e Innovación (España) Pérez-Cova, Miriam Jaumot, Joaquim Tauler, Romà |
format |
artículo |
topic_facet |
ROIMCR Chemometrics Data analysis LC × LC MCR-ALS |
author |
Pérez-Cova, Miriam Jaumot, Joaquim Tauler, Romà |
author_sort |
Pérez-Cova, Miriam |
title |
Untangling comprehensive two-dimensional liquid chromatography data sets using regions of interest and multivariate curve resolution approaches |
title_short |
Untangling comprehensive two-dimensional liquid chromatography data sets using regions of interest and multivariate curve resolution approaches |
title_full |
Untangling comprehensive two-dimensional liquid chromatography data sets using regions of interest and multivariate curve resolution approaches |
title_fullStr |
Untangling comprehensive two-dimensional liquid chromatography data sets using regions of interest and multivariate curve resolution approaches |
title_full_unstemmed |
Untangling comprehensive two-dimensional liquid chromatography data sets using regions of interest and multivariate curve resolution approaches |
title_sort |
untangling comprehensive two-dimensional liquid chromatography data sets using regions of interest and multivariate curve resolution approaches |
publisher |
Elsevier |
publishDate |
2021-04-01 |
url |
http://hdl.handle.net/10261/266228 http://dx.doi.org/10.13039/501100004837 https://api.elsevier.com/content/abstract/scopus_id/85101469591 |
work_keys_str_mv |
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