Bayesian optimization of comprehensive two-dimensional liquid chromatography separations.

Autor: Boelrijk J; AI4Science Lab, University of Amsterdam, The Netherlands; AMLab, Informatics Institute, University of Amsterdam, The Netherlands. Electronic address: jim.boelrijk@gmail.com., Pirok B; AI4Science Lab, University of Amsterdam, The Netherlands; Analytical Chemistry Group, Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, The Netherlands., Ensing B; AI4Science Lab, University of Amsterdam, The Netherlands; Computational Chemistry Group, Van 't Hoff Institute for Molecular Sciences, University of Amsterdam, The Netherlands. Electronic address: b.ensing@uva.nl., Forré P; AI4Science Lab, University of Amsterdam, The Netherlands; AMLab, Informatics Institute, University of Amsterdam, The Netherlands.
Jazyk: angličtina
Zdroj: Journal of chromatography. A [J Chromatogr A] 2021 Dec 06; Vol. 1659, pp. 462628. Date of Electronic Publication: 2021 Oct 14.
DOI: 10.1016/j.chroma.2021.462628
Abstrakt: Comprehensive two-dimensional liquid chromatography (LC×LC), is a powerful, emerging separation technique in analytical chemistry. However, as many instrumental parameters need to be tuned, the technique is troubled by lengthy method development. To speed up this process, we applied a Bayesian optimization algorithm. The algorithm can optimize LC×LC method parameters by maximizing a novel chromatographic response function based on the concept of connected components of a graph. The algorithm was benchmarked against a grid search (11,664 experiments) and a random search algorithm on the optimization of eight gradient parameters for four different samples of 50 compounds. The worst-case performance of the algorithm was investigated by repeating the optimization loop for 100 experiments with random starting experiments and seeds. Given an optimization budget of 100 experiments, the Bayesian optimization algorithm generally outperformed the random search and often improved upon the grid search. Moreover, the Bayesian optimization algorithm offered a considerably more sample-efficient alternative to grid searches, as it found similar optima to the grid search in far fewer experiments (a factor of 16-100 times less). This could likely be further improved by a more informed choice of the initialization experiments, which could be provided by the analyst's experience or smarter selection procedures. The algorithm allows for expansion to other method parameters (e.g., temperature, flow rate, etc.) and unlocks closed-loop automated method development.
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 © 2021. Published by Elsevier B.V.)
Databáze: MEDLINE
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