Enhancing LC×LC separations through Multi-Task Bayesian Optimization

• Published in: ChemRxiv 2024-02
• Main Authors: Boelrijk, Jim, Molenaar, Stef R.A., Bos, Tijmen S., Dahlseid, Tina A., Ensing, Bernd, Stoll, Dwight R., Forré, Patrick, Pirok, Bob W.J.
• Format: Article
• Language: English
• Subjects: Algorithms; Analytical Chemistry; Bayesian analysis; Chemistry; Chemoinformatics; Liquid chromatography; Mass Spectrometry; Mathematical models; Modelling; Optimization; Parameters; Retention; Separation Science; Trial and error methods; User experience
• ISSN: 2573-2293
• DOI: 10.26434/chemrxiv-2024-5mmvj

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 one hand, retention modeling depends on effective peak tracking and accurate retention time and width predictions, becoming 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.