Algorithm for tracking peaks amongst numerous datasets in comprehensive two-dimensional chromatography to enhance data analysis and interpretation.

Autor: Molenaar SRA; Analytical Chemistry Group, van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands; Centre for Analytical Sciences Amsterdam (CASA), The Netherlands. Electronic address: s.r.a.molenaar@uva.nl., Mommers JHM; DSM Engineering Materials, Geleen, The Netherlands., Stoll DR; Department of Chemistry, Gustavus Adolphus College, Saint Peter, MN 56082, United States., Ngxangxa S; Department of Chemistry and Polymer Science, Stellenbosch University, Private Bag X1, Matieland, 7602, South Africa., de Villiers AJ; Department of Chemistry and Polymer Science, Stellenbosch University, Private Bag X1, Matieland, 7602, South Africa., Schoenmakers PJ; Analytical Chemistry Group, van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands; Centre for Analytical Sciences Amsterdam (CASA), The Netherlands., Pirok BWJ; Analytical Chemistry Group, van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Science Park 904, 1098 XH Amsterdam, The Netherlands; Centre for Analytical Sciences Amsterdam (CASA), The Netherlands.
Jazyk: angličtina
Zdroj: Journal of chromatography. A [J Chromatogr A] 2023 Aug 30; Vol. 1705, pp. 464223. Date of Electronic Publication: 2023 Jul 20.
DOI: 10.1016/j.chroma.2023.464223
Abstrakt: Analytical data processing often requires the comparison of data, i.e. finding similarities and differences within separations. In this context, a peak-tracking algorithm was developed to compare multiple datasets in one-dimensional (1D) and two-dimensional (2D) chromatography. Two application strategies were investigated: i) data processing where all chromatograms are produced in one sequence and processed simultaneously, and ii) method optimization where chromatograms are produced and processed cumulatively. The first strategy was tested on data from comprehensive 2D liquid chromatography and comprehensive 2D gas chromatography separations of academic and industrial samples of varying compound classes (monoclonal-antibody digest, wine volatiles, polymer granulate headspace, and mayonnaise). Peaks were tracked in up to 29 chromatograms at once, but this could be upscaled when necessary. However, the peak-tracking algorithm performed less accurate for trace analytes, since, peaks that are difficult to detect are also difficult to track. The second strategy was tested with 1D liquid chromatography separations, that were optimized using automated method-development. The strategy for method optimization was quicker to detect peaks that were still poorly separated in earlier chromatograms compared to assigning a target chromatogram, to which all other chromatograms are compared. Rendering it a useful tool for automated method optimization.
Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023. Published by Elsevier B.V.)
Databáze: MEDLINE
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