Recent applications of retention modelling in LC

Ever since the 1970s, retention modelling has been a point of interest for the characterization of retention mechanisms. In Amsterdam, modelling of retention is mainly conducted for the purpose of method optimization. With the spur of applications of retention modelling to characterize HILIC, the field has recently received a significant number of developments. Furthermore, the rapidly growing technological capabilities in data sciences certainly continue to introduce new opportunities to model retention.

PhD candidate Mimi den Uijl (Van ‘t Hoff Institute of Molecular Sciences) set out to track these developments, covering mainly the last 5 years [1]. Focusing on applications of of the modelling of mobile-phase effects, den Uijl found five main categories under which most applications could be classified. These were method optimization, method transfer, stationary-phase characterization, selectivity characterization and lipophilicity characterization.

Den Uijl identified a number of main focus areas for which retention modelling was mainly applied and mapped their generic workflows. Reprinted with permission from [1].

"There is currently no consensus on the quality of retention models, which frustrates the comparison and evaluation of models. Reported prediction errors range from 0.1 to 10%, but almost all authors speak of “accurate” or “good” models."

Den Uijl et al.

Den Uijl furthermore reviewed the use of individual models and found that a surprising small number of studies reported numerical evaluations of the regression. As one of her conclusion, Den Uijl noted that model parameters may eventually be used as system‐independent retention data, if numerical evaluation data would be provided.

Her review, which she wrote together with Peter Schoenmakers, Maarten van Bommel and Bob Pirok, was published open-access in the special Reviews 2021 issue of Journal of Separation Science. The publication can freely be accessed here.


[1] Recent applications of retention modelling in liquid chromatography, M.J. den Uijl,  P.J. Schoenmakers,  B.W.J. Pirok, and  M.R. van Bommel, J. Sep. Sci., 2020, DOI: 10.1002/jssc.202000905.

Mimi den Uijl is a PhD student in the TooCOLD (Toolbox for studying the Chemistry Of Light-induced Degradation) project at the University of Amsterdam. In this project, Mimi develops light-induced reaction modulators for use in 2D-LC. You can read more about her on the Team page.