Closed-loop automatic gradient design for liquid chromatography using Bayesian optimization

29 July 2022, Version 1
This content is a preprint and has not undergone peer review at the time of posting.


Contemporary complex samples require sophisticated methods for full analysis. This work describes the development of a Bayesian optimization algorithm for the automated and unsupervised development of gradient programs. The algorithm was tailored to LC using a Gaussian process model with a novel covariance kernel. To facilitate unsupervised learning, the algorithm was designed to interface directly with the chromatographic system. Single-objective and multi-objective Bayesian optimization strategies were investigated for the separation of a complex (n>80) dye mixture. The multi-objective strategy was found to be very powerful and flexible in terms of exploring the Pareto front. The single-objective strategy was found to be slightly faster in finding a satisfactory optimum. One additional advantage of the multi-objective approach was that it allows a trade-off to be made between multiple objectives. In general, the Bayesian optimization strategy was found to be particularly suitable, but not limited to, cases where retention modelling is not possible, although its scalability might be limited in terms of the number of parameters that can be simultaneously optimized.


Bayesian optimization
Machine learning
Liquid chromatography
Closed-loop method development
Gradient elution

Supplementary materials

Supplementary information
Supplementary information of the main paper: Closed-loop automatic gradient design for liquid chromatography using Bayesian optimization


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