Enhancing LC×LC separations through multi-task Bayesian optimization.

Autor: Boelrijk J; AI4Science Lab, Informatics Institute, University of Amsterdam, Amsterdam, Science Park 904, 1098 XH, The Netherlands; AMLab, Informatics Institute, University of Amsterdam, Amsterdam, Science Park 904, 1098 XH, The Netherlands. Electronic address: jim.boelrijk@gmail.com., Molenaar SRA; Division of Bioanalytical Chemistry, Amsterdam Institute of Molecular and Life Sciences, De Boelelaan 1085, Amsterdam, 1081 HV, The Netherlands; Analytical Chemistry Group, Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, Science Park 904, 1098 XH, The Netherlands., Bos TS; Division of Bioanalytical Chemistry, Amsterdam Institute of Molecular and Life Sciences, De Boelelaan 1085, Amsterdam, 1081 HV, The Netherlands; Analytical Chemistry Group, Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, Science Park 904, 1098 XH, The Netherlands., Dahlseid TA; Department of Chemistry, Gustavus Adolphus College, Saint Peter, MN 56082, United States., Ensing B; AI4Science Lab, Informatics Institute, University of Amsterdam, Amsterdam, Science Park 904, 1098 XH, The Netherlands; Computational Chemistry Group, Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, Science Park 904, 1098 XH, The Netherlands., Stoll DR; Department of Chemistry, Gustavus Adolphus College, Saint Peter, MN 56082, United States., Forré P; AI4Science Lab, Informatics Institute, University of Amsterdam, Amsterdam, Science Park 904, 1098 XH, The Netherlands; AMLab, Informatics Institute, University of Amsterdam, Amsterdam, Science Park 904, 1098 XH, The Netherlands., Pirok BWJ; AI4Science Lab, Informatics Institute, University of Amsterdam, Amsterdam, Science Park 904, 1098 XH, The Netherlands; Analytical Chemistry Group, Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam, Science Park 904, 1098 XH, The Netherlands. Electronic address: bob.pirok@uva.nl.
Jazyk: angličtina
Zdroj: Journal of chromatography. A [J Chromatogr A] 2024 Jul 05; Vol. 1726, pp. 464941. Date of Electronic Publication: 2024 May 03.
DOI: 10.1016/j.chroma.2024.464941
Abstrakt: Method development in comprehensive two-dimensional liquid chromatography (LC×LC) is a challenging process. The interdependencies between the two dimensions and the possibility of incorporating complex gradient profiles, such as multi-segmented gradients or shifting gradients, make trial-and-error method development time-consuming and highly dependent on user experience. Retention modeling and Bayesian optimization (BO) have been proposed as solutions to mitigate these issues. However, both approaches have their strengths and weaknesses. On the one hand, retention modeling, which approximates true retention behavior, depends on effective peak tracking and accurate retention time and width predictions, which are increasingly challenging for complex samples and advanced gradient assemblies. On the other hand, Bayesian optimization may require many experiments when dealing with many adjustable parameters, as in LC×LC. Therefore, in this work, we investigate the use of multi-task Bayesian optimization (MTBO), a method that can combine information from both retention modeling and experimental measurements. The algorithm was first tested and compared with BO using a synthetic retention modeling test case, where it was shown that MTBO finds better optima with fewer method-development iterations than conventional BO. Next, the algorithm was tested on the optimization of a method for a pesticide sample and we found that the algorithm was able to improve upon the initial scanning experiments. Multi-task Bayesian optimization is a promising technique in situations where modeling retention is challenging, and the high number of adjustable parameters and/or limited optimization budget makes traditional Bayesian optimization impractical.
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 © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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