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An important challenge in chromatography is the development of adequate separation methods. Accurate retention models can significantly simplify and expedite the development of adequate separation methods for complex mixtures. The purpose of this study was to introduce reinforcement learning to chromatographic method development, by training a double deep Qlearning algorithm to select optimal isocratic scouting runs to generate accurate retention models. These scouting runs were fit to the NeueKuss retention model, which was then used to predict retention factors both under isocratic and gradient conditions. The quality of these predictions was compared to experimental data points, by computing a mean relative percentage error (MRPE) between the predicted and actual retention factors. By providing the reinforcement learning algorithm with a reward whenever the scouting runs led to accurate retention models and a penalty when the analysis time of a selected scouting run was too high (> 1h); it was hypothesized that the reinforcement learning algorithm should by time learn to select good scouting runs for compounds displaying a variety of characteristics. The reinforcement learning algorithm developed in this work was first trained on simulated data, and then evaluated on experimental data for 57 small molecules – each run at 10 different fractions of organic modifier (0.05 to 0.90) and four different linear gradients. The results showed that the MRPE of these retention models (3.77% for isocratic runs and 1.93% for gradient runs), mostly obtained via 3 isocratic scouting runs for each compound, were comparable in performance to retention models obtained by fitting the NeueKuss model to all (10) available isocratic datapoints (3.26% for isocratic runs and 4.97% for gradient runs) and retention models obtained via a “chromatographer's selection” of three scouting runs (3.86% for isocratic runs and 6.66% for gradient runs). It was therefore concluded that the reinforcement learning algorithm learned to select optimal scouting runs for retention modeling, by selecting 3 (out of 10) isocratic scouting runs per compound, that were informative enough to successfully capture the retention behavior of each compound.
Originele taal2  English 

Artikelnummer  461900 
Tijdschrift  Journal of Chromatography A 
Volume  1638 
DOI's  
Status  Published  8 feb 2021 
Bibliografische nota
Copyright © 2021. Published by Elsevier B.V.Vingerafdruk
Duik in de onderzoeksthema's van 'Deep Qlearning for the selection of optimal isocratic scouting runs in liquid chromatography'. Samen vormen ze een unieke vingerafdruk.Projecten
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SRP6: SRP (Zwaartepunt): exploitatie van de voordelen van de Orde in Opsluiting voor een groenere chemie
Desmet, G., Denayer, J., Denayer, J., Desmet, G. & Denayer, J.
1/11/12 → 31/10/22
Project: Fundamenteel