Use and Limits of Scouting Experiments for Retention Modelling

We all know gradient data is less reliable than isocratic data for modelling retention. But to what extend? We investigated this together with the group of Dwight Stoll.
Share on facebook
Share on twitter
Share on linkedin
Share on whatsapp
Share on google
Share on email

Retention modelling is a useful technique which can be used to substantially reduce the method-development process for LC separations [1]. Believe it or not, as complicated and distant it may seem from routine use in the analytical, it actually is useful to make life easier.

Retention modelling saves us a lot of time

One branch of retention modelling in liquid chromatography employs scouting (or ‘scanning’) experiments to probe retention of analytes of interest. By fitting models to the recorded retention times, retention parameters are obtained for each individual analyte. Interestingly, this information can be used to predict retention times for the analytes under conditions different from those used for the scouting experiments. 

In other words, a computer can use these to simulate large numbers of hypothetical separation methods. By analyzing the resulting separations, optimal method parameters can be discerned. As a consequence, trial-and-error method development can be replaced by a number of scouting experiments and thus saves valuable time.

Figure 1. Workflow of the method optimization using scanning gradients to obtain retention-model parameters. The workflow starts at the top right with an insufficiently-resolved sample, on which scouting experiments are performed to yield retention parameters. These can be used to compute the most-optimal method parameters, thus saving a lot of valuable method-development time. Reproduced with permission from [2].

We have earlier explained why, ideally, we would like to use gradient elution during these scanning experiments, something also shown by Vivo-Truyóls et al. [3]. In contrast to isocratic elution, gradients are more practical and save a lot of time at the cost of significantly more challenging modelling and less accurate retention parameters.

With the entire method-development automatization approach hinging on the accuracy of retention parameters, it is imperative that usefulness of the gradient experiments is mapped and, if possible, improved.

Optimizing the separation of unknown degradation products

CAST member and PhD candidate Mimi den Uijl develops analytical methods based on 2D-LC to degrade and characterize small molecules and their degradation products. Under the lead of Prof. Maarten van Bommel, her application area ranges from cultural-heritage art objects, to environmental aqueous samples, to foodstuffs (Unilever). While the identify of the parent molecules are sometimes known, this is certainly not the case for degradation products. Yet optimized methods are needed to characterize such samples.

Den Uijl and co-workers participated in a large collaboration between the teams of Bob Pirok (University of Amsterdam) and Dwight Stoll (Gustavus Adolphus College), and – in their project – systematically investigated the use and limits of gradient experiments for method-development workflows.

Figure 2. Combined results of all investigated factors. The box-and-whisker plots represent the average prediction error of all the compounds for a noisy dataset (top) and highly-precise (bottom) dataset. See publication for more details. Reproduced with permission from [2].

For the first stage published in Journal of Chromatography A, two datasets were generated of various small molecules ranging in chemical properties [2]. One dataset was measured was recorded with very high measurement prevision relative to the other. Interestingly, some of the results were different between the two sets.

Several key observations

These and other conclusions are detailed in the article which was published in Journal of Chromatography A as open-access article. This means you can download and read it for free. Meanwhile, we decided to continue this project, so we hope to be able to report more about this at a later stage.


[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.

[2] Measuring and using scanning-gradient data for use in method optimization for liquid chromatography, Mimi J. den Uijl, Peter J. Schoenmakers, Grace K. Schulte, Dwight R. Stoll, Maarten R. van Bommel, Bob W.J. Pirok, J. Chromatogr. A, 2021, 1636, 461780, DOI: 10.1016/j.chroma.2020.461780

[3] Error analysis and performance of different retention models in the transference of data from/to isocratic/gradient elution, Author links open overlay panel, G. Vivó-Truyols, J.R. Torres-Lapasió, M.C. García-Alvarez-Coque, J. Chromatogr. A, 2003, 1018(2), 169-181, DOI: 10.1016/j.chroma.2003.08.044.

[4] Reducing the influence of geometry-induced gradient deformation in liquid chromatographic retention modelling, T.S. Bos, L.E. Niezen, M.J. den Uijl, S.R.A. Molenaar, S. Lege, P.J. Schoenmakers, G.W. Somsen, B.W.J. Pirok, J. Chromatogr. A2020, 1635, 461714, DOI: 10.1016/j.chroma.2020.461714.

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.


Recent applications of retention modelling in LC

PhD candidate Mimi den Uijl has together with her co-workers reviewed recent applications of retention modelling in LC. In the review, den Uijl furthermore identified key application areas where retention modelling received a significant degree of interest.

Recent Posts