Recent applications of chemometrics in one‐ and two‐dimensional chromatography

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Abstract

The proliferation of increasingly more sophisticated analytical separation systems, often incorporating increasingly more powerful detection techniques, such as high‐resolution mass spectrometry, causes an urgent need for highly efficient data‐analysis and optimization strategies. This is especially true for comprehensive two‐dimensional chromatography applied to the separation of very complex samples. In this contribution, the requirement for chemometric tools is explained and the latest developments in approaches for (pre‐)processing and analyzing data arising from one‐ and two‐dimensional chromatography systems are reviewed. The final part of this review focuses on the application of chemometrics for method development and optimization.

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OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification

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A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding.

Piotr S. Gromski, Howbeer Muhamadali, David Ellis … (2015)

The predominance of partial least squares-discriminant analysis (PLS-DA) used to analyze metabolomics datasets (indeed, it is the most well-known tool to perform classification and regression in metabolomics), can be said to have led to the point that not all researchers are fully aware of alternative multivariate classification algorithms. This may in part be due to the widespread availability of PLS-DA in most of the well-known statistical software packages, where its implementation is very easy if the default settings are used. In addition, one of the perceived advantages of PLS-DA is that it has the ability to analyze highly collinear and noisy data. Furthermore, the calibration model is known to provide a variety of useful statistics, such as prediction accuracy as well as scores and loadings plots. However, this method may provide misleading results, largely due to a lack of suitable statistical validation, when used by non-experts who are not aware of its potential limitations when used in conjunction with metabolomics. This tutorial review aims to provide an introductory overview to several straightforward statistical methods such as principal component-discriminant function analysis (PC-DFA), support vector machines (SVM) and random forests (RF), which could very easily be used either to augment PLS or as alternative supervised learning methods to PLS-DA. These methods can be said to be particularly appropriate for the analysis of large, highly-complex data sets which are common output(s) in metabolomics studies where the numbers of variables often far exceed the number of samples. In addition, these alternative techniques may be useful tools for generating parsimonious models through feature selection and data reduction, as well as providing more propitious results. We sincerely hope that the general reader is left with little doubt that there are several promising and readily available alternatives to PLS-DA, to analyze large and highly complex data sets.

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Author and article information

Contributors

Bob W.J. Pirok:

ORCID: https://orcid.org/0000-0002-4558-3778

B.W.J.Pirok@uva.nl

Journal

Journal ID (nlm-ta): J Sep Sci

Journal ID (iso-abbrev): J Sep Sci

Journal ID (doi): 10.1002/(ISSN)1615-9314

Journal ID (publisher-id): JSSC

Title: Journal of Separation Science

Publisher: John Wiley and Sons Inc. (Hoboken )

ISSN (Print): 1615-9306

ISSN (Electronic): 1615-9314

Publication date (Electronic): 19 March 2020

Publication date (Print): May 2020

Volume: 43

Issue: 9-10 , Emerging Thought Leaders in Separation Science ( doiID: 10.1002/jssc.v43.9-10 )

Pages: 1678-1727

Affiliations

[ ¹ ] Division of Bioanalytical Chemistry Amsterdam Institute for Molecules, Medicines and Systems Vrije Universiteit Amsterdam Amsterdam The Netherlands

[ ² ] Analytical Chemistry Group van ’t Hoff Institute for Molecular Sciences, Faculty of Science University of Amsterdam Amsterdam The Netherlands

[ ³ ] Centre for Analytical Sciences Amsterdam (CASA) Amsterdam The Netherlands

Author notes

[*] [* ] Correspondence

dr. Bob W.J. Pirok, Postal address: Postbus 94157, 1090 GD Amsterdam, The Netherlands.

Email: B.W.J.Pirok@ 123456uva.nl

[*]

Equal contribution

Author information

Tijmen S. Bos https://orcid.org/0000-0002-0728-6385

Wouter C. Knol https://orcid.org/0000-0002-0428-9943

Stef R.A. Molenaar https://orcid.org/0000-0002-4142-7233

Leon E. Niezen https://orcid.org/0000-0002-3849-9228

Peter J. Schoenmakers https://orcid.org/0000-0002-9167-7716

Govert W. Somsen https://orcid.org/0000-0003-4200-2015

Bob W.J. Pirok https://orcid.org/0000-0002-4558-3778

Article

Publisher ID: JSSC6782

DOI: 10.1002/jssc.202000011

PMC ID: 7317490

PubMed ID: 32096604

SO-VID: c1cba22d-1410-4ac9-a604-fb231f88b577

License:

This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

History

Date received : 07 January 2020

Date revision received : 20 February 2020

Date accepted : 21 February 2020

Page count

Figures: 22, Tables: 2, Pages: 50, Words: 32220

Funding

Funded by: Nederlandse Organisatie voor Wetenschappelijk Onderzoek , open-funder-registry 10.13039/501100003246;

Award ID: NWO‐CHIPP UNMATCHED

Funded by: Agilent Technologies , open-funder-registry 10.13039/100004322;

Award ID: UR grant #4354

Custom metadata

source-schema-version-number 2.0

cover-date May 2020

details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_JATSPMC version:5.8.4 mode:remove_FC converted:26.06.2020

Keywords: 1d chromatography,2d chromatography,chemometrics,data processing,optimization

Data availability:

Keywords: 1d chromatography, 2d chromatography, chemometrics, data processing, optimization

Recent applications of chemometrics in one‐ and two‐dimensional chromatography

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Abstract

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Electrospinning for biomedical applications

Most cited references 293

Ant colony optimization

OPLS discriminant analysis: combining the strengths of PLS-DA and SIMCA classification

A tutorial review: Metabolomics and partial least squares-discriminant analysis--a marriage of convenience or a shotgun wedding.

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