Chemometric Strategies for Fully Automated Interpretive Method Development in Liquid Chromatography

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


The great potential gains in separation power and analysis time that can result from rigorously optimizing LC-MS and 2D-LC-MS methods for routine measurements has prompted many scientists to develop computer-aided method-development tools. The applicability of these has been proven in numerous applications, but their proliferation is still limited. Arguably, the majority of LC methods are still developed in a conventional manner, i.e. by analysts who rely on their knowledge and experience. In this work, a novel, open-source algorithm was developed for automated and interpretive method development of LC separations. A closed-loop workflow was constructed that interacted directly with the LC and ran unsupervised in an automated fashion. The algorithm was tested using two newly designed strategies. The first utilized retention modeling, whereas the second used the Bayesian-optimization machine-learning approach. In both cases, the algorithm could arrive within ten iterations at an optimum of the objective function, which included resolution and measurement time. The design of the algorithm was modular, so as to facilitate compatibility with previous works in literature and its performance thus hinged on each module (e.g., signal processing, choice of retention model, objective function). Key focus areas for further improvement were identified. Bayesian optimization did not require any peak tracking or retention modeling. Accurate prediction of elution profiles was found to be indispensable for the strategy using retention modeling. This is the first interpretive algorithm demonstrated with complex samples. Peak tracking was conducted using UV-Vis absorbance detection, but use of MS detection is expected to significantly broaden the applicability of the workflow.


automated method development
machine learning
liquid chromatography
retention modeling
closed-loop optimisation

Supplementary materials

Supporting Information
Additional data supporting the samples, LC-MS peak tracking, retention modeling constraints, employed gradient programs, score function visualizations, UV-Vis peak tracking, gradient deformation, predicted chromatograms, and the algorithm as well further design considerations.


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