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Use and Limits of Scouting Experiments for Retention Modelling

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.

References

[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.

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Reducing Effect of Gradient Deformation For LC Retention Modelling

Retention modelling is a useful technique which can be used to substantially reduce the method-development process for LC separations. One approach utilizes so-called scanning (or ‘scouting’) experiments using isocratic or gradient elution [1]. Here, a number of pre-defined methods are employed to record retention times to which empirical models are fitted. 

Isocratic experiments will generally yield reliable solid datasets that are very suitable for retention modelling. Using isocratic elution is, however, not always very practical. Indeed, scouting experiments can take rather long for the slower experiments. Moreover, some manual fine-tuning and experience with the analytes in question are needed to identify the appropriate modifier concentrations.

In contrast, gradient elution allows rather quick and easy scanning experiments at the significant cost of the usefulness of the resulting data. Where isocratic experiments directly measure the retention factor at a certain modifier (φ) fraction, the retention time in gradient elution depends on the gradient experienced by the analyte.

image_2021-01-02_083104

Figure 1. Schematic illustrating a programmed linear gradient and the experienced gradients for two different systems.

However, as the programmed change in composition produced by the pump migrates through the chromatographic system, its shape is altered. In Figure 1, above, we can see how this leads to the familiar difference between the programmed (dark blue) and effective (purple, light blue) gradients for two different systems.

This deviation is the product of an array of effects, such as the morphology and inefficiencies in the pump components, chromatographic system volumes and the accuracy of pumped mobile-phase composition (A vs. B). The latter can rather easy deviate if the pump does not take into account the change in density as φ increases.

Figure 2. Response functions of systems 1 and 2. The shape essentially represents the differences between the programmed and effective gradients shown in Figure 1.

The overall effect can be represented by response functions. These functions essentially describe the difference between the programmed and measured gradient. Two examples for two different systems are shown above in Figure 2. Indeed, depending on the pump characteristics, dramatic changes can be observed.

The problem is only complicated further as the recorded dwell curve may also in itself represent an inaccurate depiction. Depending on the detector, solvatochromic effects and  the presence of other mobile-phase components can severely convolute the true depicted of the experienced gradient.

For modelling, deformation is a problem because

As part of a larger collaboration with Agilent Technologies in the “DAS PRETSEL” project, Tijmen Bos, with assistance of other CAST members Mimi den Uijl, Leon Niezen and Stef Molenaar, developed an algorithm to reduce partially the effects of gradient deformation.

In their work, Bos et al. showed that the impact of the gradient deformation significantly impacts retention parameters. By modelling so-called Stable distribution functions to the measured dwell curves, the authors were able to significantly reduce the prediction errors for water-water systems (Figure 3). Conveniently, the Stable parameters turned out to be related to physical parameters of the chromatographic system.

Figure 3. Relative errors (%) in the predicted retention times of the test compounds on Instruments 2 (top) and 3 (bottom) obtained when using retention parameters determined for the test compounds on Instrument 1 at different flow rates. Please see the publication for details about the instruments. Reproduced with permission from [2].

This work is part of a larger project. In this first stage, we mainly targeted the geometric-influences. Now, we shift our focus to more complicated solvent systems and also the effect on larger molecular systems.

The work was recently published open-access in Journal of Chromatography A and can be downloaded for free here. An accompanying video pitch can be viewed below. Readers interested in learning more about retention modelling and its application areas are referred elsewhere

References

[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] 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. A, 2021, 1635, 461714, DOI: 10.1016/j.chroma.2020.461714.

The Authors

Tijmen Bos

Mimi den Uijl

Leon Niezen

Stef Molenaar

Researchers Bos, Niezen and Molenaar are part of the UNMATCHED project, which is supported by BASF, DSM and Nouryon, and receives funding from the Netherlands Organization for Scientific Research (NWO). Den Uijl is part of the TooCOLD project, which is supported by Unilever and and NWO. You can read more about them and find their contact info on the Team page.

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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.

References

[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.