This is the R script associated to the article "Assisted Active Learning for Model-Based Method Development in Liquid Chromatography"
The study explores the application of Assisted Active Learning in model-based method development (MD) for liquid chromatography (LC) by using Bayesian statistics to incorporate historical data and analyte information for the generation of initial retention models. The process involves updating the model parameters based on new experiments, coupled with an active data selection method to choose the most informative experiment to run in a subsequent step. This iterative process balances model exploitation and experimental exploration until a satisfactory separation is achieved.
If you use this code, please refer to the following article: Emery Bosten, Marie Pardon, Kai Chen, Valerie Koppen, Gerd Van Herck, Mario Hellings, Deirdre Cabooter; Assisted Active Learning for Model-Based Method Development in Liquid Chromatography. Anal. Chem. 20 August 2024; 96 (33): 13699–13709. https://doi.org/10.1021/acs.analchem.4c02700
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