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Peak Tracking for Use in 2D-LC

One of the big challenges preventing routine application of comprehensive 2D-LC (LC×LC) arises from the complex data structure produced by each experiment. Indeed, with the first-dimension separation only scarcely sampled by the modulator, only a handful of datapoints are available to describe the information [1]. In contrast, second-dimension signals generally offer a surplus of datapoints. As a result, describing two-dimensional peaks – in particular those which co-elute with other species, can be rather difficult.

This becomes a significant issue when the chromatograms comprise a vast number of eluting analytes, and an actual bottleneck when chromatograms of a large number of samples must be compared.

Figure 1. Assessing peaks in LC×LC also becomes a problem during computational assessment. Due to the fact that the modulator samples each first-dimension peak multiple times, the peak is divided over different second-dimension modulations. As modern 2D-LC methods often utilize advanced composition programs (e.g. shifting gradients), the peak will elute differently for each cut. With furthermore the risk of undersampling, Molenaar et al. investigated the three strategies to evaluate peak statistical moments. Reproduced with permission from [2].

 

This problem is even more critical for analyte mixtures where species do not resolve as individual peaks but rather as broad envelopes representing distributions of co-eluting peaks (i.e. polymer separations).

Active for the UNMATCHED project, Stef Molenaar works to develop algorithms to improve data analysis and method development of 2D-LC polymer separations. To cross the bridge to polymer data, Molenaar first targeted the already challenging case of resolved “small molecules”.

Meanwhile, the group of Dwight Stoll (Gustavus Adolphus College, MN, United States) specializes in the development of highly-optimized state-of-the-art 2D-LC bioseparations [3].

Figure 2. The algorithm developed by Molenaar et al. was able to automatically track and analyze the majority of the peaks in different LC×LC-MS measurements of monoclonal-antibody peptides. Reproduced with permission from [2].

We therefore teamed up with the group of Dwight Stoll and developed a peak-tracking algorithm to evaluate LC×LC-MS datasets and combine their information [2]. The algorithm computes statistical moments for each dimension and consults mass spectra in order to build an assessment. To reduce the number of evaluations and save computational resources, the algorithm compares the retention patterns between the supplied chromatogram. The work was based on an earlier work by Molenaar [4].

The latter is particularly important because this renders the algorithm useful for method-optimization programs where different methods are used to record chromatograms of the same sample in order to allow retention modelling.

While noting that the performance of the algorithm heavily relied on peak-detection algorithms to locate the peaks, the majority of the sample components were well tracked.

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 Developments in Two-Dimensional Liquid Chromatography: Fundamental Improvements for Practical Applications
B.W.J. Pirok, D.R. Stoll and P.J. Schoenmakers, Anal. Chem., 2019, 91(1), 240-263, DOI: 10.1021/acs.analchem.8b04841 OPEN ACCESS

[2] Peak-tracking algorithm for use in comprehensive two-dimensional liquid chromatography – application to monoclonal-antibody peptides, S.R.A. Molenaar, T.A. Dahlseid, G. Leme, D.R. Stoll, P.J. Schoenmakers, B.W.J. Pirok, J. Chromatogr. A2021, 1639, 461922, DOI: 10.1016/j.chroma.2021.461922 OPEN ACCESS

[3] High resolution two-dimensional liquid chromatography coupled with mass spectrometry for robust and sensitive characterization of therapeutic antibodies at the peptide level, D.R. Stoll, H.R. Lhotka, D.C. Harmes, B. Madigan, J.J. Hsiao, G. O. Staples, J Chromatogr. B, 2019, 1134–1135, 121832, DOI: 10.1016/j.chroma.2021.461922.

[4] Peak-Tracking Algorithm for Use in Automated Interpretive Method-Development Tools in Liquid Chromatography
B.W.J. Pirok, S.R.A. Molenaar, L.S. Roca and P.J. Schoenmakers, Anal. Chem.2018, 90(23), 14011-14019, DOI: 10.1021/acs.analchem.8b03929 OPEN ACCESS

The Author

Stef Molenaar currently conducts his PhD in Amsterdam and his research is specifically aimed towards the development of computational strategies for polymer LC×LC data analysis and method optimization.

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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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Publications

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.

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Detection Challenges in Polymer Analysis with LC

With their large distributions culminating in wide envelopes of – almost exclusively – co-eluting peaks, polymers certainly present a unique challenge relative to the analysis of small molecules. If anything, this challenge has spurred innovations which have also benefited other fields. A good example has been the work on polymer analysis with 2D-LC in the first years of this millennium, which have certainly contributed to recent developments on the technique [1,2].

In his review, Wouter Knol (Van ‘t Hoff Institute for Molecular Sciences) reviews another research area which offers a lot of room for innovations: detection. Together with the co-authors, Knol provided an exhaustive overview of applications of detection techniques in LC for polymer analysis. For each detection technique, notable recent applications are discussed and the authors distilled the key advantages and disadvantages of each approach.

One particularly useful trait of the review is this table which summarizes all key strengths and weaknesses of each detection technique employed for LC in polymer analysis. Reprinted with permission from [3].

Knol and co-workers noted that promising approaches receive surprising attention in recent literature. He concluded that the opportunities deserve more attention. You can download and read the paper, which was published open-access in the special Reviews 2021 issue of Journal of Separation Sciences, here.

Example of a separation of complex polyether polyols with LC×LC-MS by Groeneveld et al. showing a clear structure based on the number of ethylene oxide/propylene oxide units in the polymer. Reprinted with permission from [4].

References

[1] Recent Developments in Two-Dimensional Liquid Chromatography: Fundamental Improvements for Practical Applications
B.W.J. Pirok, D.R. Stoll and P.J. Schoenmakers, Anal. Chem., 2019, 91(1), 240-263, DOI: 10.1021/acs.analchem.8b04841

[2] Comprehensive Two-Dimensional Ultrahigh-Pressure Liquid Chromatography for Separations of Polymers E. Uliyanchenko, P.J.C.H. Cools, Sj. van der Wal and P. J. Schoenmakers, Anal. Chem. 2012, 84, 18, 7802–7809, DOI: 10.1021/ac3011582

[3] Detection challenges in quantitative polymer analysis by liquid chromatography, W.C. Knol, B.W.J. Pirok, and R.A.H. Peters, J. Sep. Sci. 2020, DOI: 10.1002/jssc.202000768

[4] Characterization of complex polyether polyols using comprehensive two-dimensional liquid chromatography hyphenated to high-resolution mass spectrometry G. Groeneveld, M.N. Dunkle, M. Rinken, A.F.G. Gargano, A. de Niet, M. Pursch, E.P.C. Mes, and P.J. Schoenmakers, J. Chromatogr. A, 1569, 2018,  128-138, DOI: 10.1016/j.chroma.2018.07.054

Wouter Knol is a PhD candidate in the group of Peter Schoenmakers. He works in the UNMATCHED project in Amsterdam and mainly focuses on techniques to determine the sequence distribution of polymers.

You can read more about him on our Team page.

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Parallel gradients as alternative to shifted gradients in 2D-LC

In pursuit of full usage of the two-dimensional separation space as prescribed by Giddings [1], the LC×LC chromatographic community is continuously scouting for new methods that yield fully orthogonal separations. With the rich and diverse LC toolkit of available retention mechanisms, chromatographers mainly focus on improving the compatibility of orthogonal – yet incompatible – separation methods. This has spurred the development of active-modulation techniques such as stationary-phase-assisted modulation [2] and active-solvent modulation (ASM) [3]. Ultimately, this angle of innovation is mainly driven by the selection of stationary -and mobile phases, as well as their underlying retention mechanisms.

Rather than fine-tuning selectivity, another branch focuses on tweaking retention factors. For LC×LC separations, this has led to the introduction of shifted gradients. Here, the second-dimension gradient is altered and adapted as a function of the first-dimension gradient program [4]. While extremely effective, the optimization of shifted-gradient assemblies introduces additional complexity to the already more-complex method development process for comprehensive 2D-LC.

The situation of LC×LC contrasts heavily with that of GC×GC. For GC×GC, orthogonal separations are extremely difficult if not impossible due to analyte volatility. As a consequence, wrap-around effects are frequently generated, yet this is rather seen as advantage than disadvantage.

Prof. Tadeusz Gorecki (University of Waterloo, Canada) thus set to investigate what would happen if the same approach would be applied in LC×LC [5]. The project was an international collaboration with Alshymaa Aly (Minia University, Egypt, and University of Waterloo, Canada), Prof. Andre de Villiers and Magriet Muller from Stellenbosch University in South Africa, and Bob Pirok from the CAST team at the University of Amsterdam in the Netherlands.

RPLC×RPLC separations were simulated based on experimental data using the MOREPEAKS framework (formerly PIOTR [6]). Predicted separations using shifted gradients and parallel gradients were compared. The results suggested that parallel gradients indeed may advantageous. To verify this assessment, optimized experimental methods were executed and the resulting separations compared.

Supported by both experimental data and theoretical simulations, the authors concluded that non-orthogonal separation mechanisms could still yield good separation methods in LC×LC.

References

[1] Two-dimensional separations: concept and promise. J.C. Giddings, Anal. Chem. 1984, 56(12), 1258A–1270A, DOI: 10.1021/ac00276a003

[2] Recent Developments in Two-Dimensional Liquid Chromatography: Fundamental Improvements for Practical Applications. B.W.J. Pirok, D.R. Stoll and P.J. Schoenmakers, Anal. Chem., 2019, 91(1), 240-263, DOI: 10.1021/acs.analchem.8b04841

[3] Active Solvent Modulation: A Valve-Based Approach To Improve Separation Compatibility in Two-Dimensional Liquid Chromatography. D.R. Stoll, K. Shoykhet, P. Petersson, and S. Buckenmaier, Anal. Chem. 2017, 89(17), 9260–9267, DOI: 10.1021/acs.analchem.7b02046

[4] Optimizing separations in online comprehensive two-dimensional liquid chromatography
B.W.J. Pirok, A.F.G. Gargano and P.J. Schoenmakers, J. Sep. Sci., 2018, 41(1), 68–98, DOI: 10.1002/jssc.201700863

[5] Parallel gradients in comprehensive multidimensional liquid chromatography enhance utilization of the separation space and the degree of orthogonality when the separation mechanisms are correlated. A.A. Aly, M. Muller, A. de Villiers, B.W.J. Pirok, T. Górecki, J. Chromatogr. A, 1628, 2020, 461452, DOI: 10.1016/j.chroma.2020.461452

[6] Program for the interpretive optimization of two-dimensional resolution. B.W.J. Pirok, S. Pous-Torres, C. Ortiz-Bolsico, G. Vivó-Truyols and P.J. Schoenmakers, J. Chromatogr. A, 2016, 1450, 29–37, DOI: 10.1016/j.chroma.2016.04.061

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Publications

Liquid Chromatography in the Oil and Gas Industry

With the petroleum industry representing roughly 40% of the chemical industry [1], the analysis of petroleum-related samples is of interest for the optimization of refining processes, studying environmental pollution or monitoring of other processes, such as biodegradation. While gas chromatography is commonly used for most petrochemical samples, liquid-phase separations may be employed for the difficult cases.

To cover the applications of liquid chromatography in this context, MSc. graduate student Denice van Herwerden compiled this in a chapter as a contribution to the book Analytical Techniques in the Oil and Gas Industry for Environmental Monitoring [2].

In her review, Van Herwerden observed that LC is mainly applied for analysis of heavier petroleum fractions (due to their high boiling points), thermally labile compounds, and, for example, acidic compounds that would require a derivatization step prior to GC analysis. She also discussed applications where LC is used as a pre-separation technique to decrease the sample dimensionality prior to GC analysis. Denice found that, while both on- or off-line coupling may be used, the off-line coupling is recently more favored. 

Finally, van Herwerden addressed a limited number of applications of comprehensive two-dimensional liquid chromatography to the analysis of heavy-oil fractions and derivatives

Analytical Techniques in the Oil and Gas Industry for Environmental Monitoring (ISBN: 9781119523307) is a book edited by Melissa Dunkle and William Winniford. The book was published this August and includes 11 chapters. The chapter on LC applications, which was co-written by Bob Pirok and Peter Schoenmakers, can be found here.

References

[1] Hazardous Effects of Petrochemical Industries: A Review. A. Sharma, P. Sharma, A. Sharma, R. Tyagi and A. Dixit, Advances in Petrochemical Science, 2018, 3(2), 2–4, DOI: 10.19080/rapsci.2017.03.555607

[2] Liquid Chromatography: Applications for the Oil and Gas Industry, D. van Herwerden, B.W.J. Pirok, and P.J. Schoenmakers, Analytical Techniques in the Oil and Gas Industry for Environmental Monitoring, 2020, John Wiley & Sons, Inc., ISBN: 9781119523307, DOI: 10.1002/9781119523314.ch5

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Challenges in Obtaining Information from 1D- and 2D-LC

Earlier this June, CAST-member Bob Pirok (Van ‘t Hoff Institute for Molecular Sciences) and Johan Westerhuis (Swammerdam Institute for Life Sciences) published their vision on current challenges in data analysis in one-dimensional (1D) and two-dimensional (2D) chromatography [1].

In their article, the authors discuss the caveats of common data-analysis strategies that are typically employed in processing data obtained from 1D and 2D chromatography. The authors discuss the importance of data pre-processing and the associated challenges. Highlighting one of the conclusions of an earlier review [2], the authors again emphasized that no current studies provide an objective numerical comparison of background correction metrics.

image_2020-11-05_085441

Figure 1. Comparison of commonly applied methods to assess the area of a peak. Reprinted from [1] with permission.

Pirok and Westerhuis furthermore explained the difficulties with common curve resolution methods such as matched filtering (a.k.a. curve-fitting) and derivated-based approaches.

While multi-dimensional separations increase the likelihood of resolution, the authors noted that this by no means eases the job of obtaining information of these datasets. The authors also discussed some key opportunities currently in the works by scientists around the globe. You can read the article freely here.

Figure 2. The availability of an additional dimension of data through the detector (in these case DAD) certainly helps to distinguish the peaks, but does not aid in easing extracting the information of the data.

References

[1] Challenges in Obtaining Relevant Information from One- and Two-Dimensional LC Experiments
B.W.J. Pirok & J.A. Westerhuis, LC-GC North America, 6(38), 2020, 8-14 [LINK]

[2] Recent applications of chemometrics in one- and two-dimensional chromatography
T.S. Bos, W.C. Knol, S.R.A. Molenaar, L.E. Niezen, P.J. Schoenmakers, G.W. Somsen, B.W.J. Pirok, J. Sep. Sci. 43(9-10), 2020, 1678-1727, DOI: 10.1002/jssc.202000011

Categories
Publications

Applications of chemometrics in 1D and 2D chromatography

As we strive for more peak capacity to tackle the separation of the samples of tomorrow it is easy to forget that we should also still be able to retrieve the answer to our original question [1]. However, with the ever increasing complexity of our separation systems, the data we obtain from our experiments becomes similarly more sophisticated [2]. One modern example is an LC×LC-MS/MS system, which is capable of generating truly massive amounts of data per experiment.

As chromatographers, we tend to forget the data analysis and tend to rely on commercial software packages. However, as we continue to produce more efficient separation systems, the field of chemometrics is doing its best to keep up. With the technological capabilities of computer systems increasing by the day, the field of chemometrics is unsurprisingly very active.

CAST members Tijmen Bos, Wouter Knol, Leon Niezen and Stef Molenaar reviewed these recent works in their review recently published in Journal of Separation Sciences [3]. In their review, the young authors divided the work in literature into a number of categories data pre-processing (including retention-time alignment), data analysis (peak detection, information extraction, etc.), quantitative approaches and method optimization.

While the authors addressed the multivariate approaches used to tackle highly complex data, the review also focused on developments in the processing and use of day-to-day data such as obtained in 1D chromatography.

Most reported methods were developed to tackle a specific challenge in a data set and comparisons with other approaches supported by numerical data have rarely been reported.

Bos et al.

Within the subject of data preprocessing (background correction, signal smoothening, etc.) the authors found a rather large number (>10) of new methods published in the last few years. Yet, the authors surprisingly observed a complete lack of objective comparisons of these approaches. Consequently, it appears to be rather unclear which of the by now many existing approaches is suitable for the chromatographer in the routine lab. Another conclusion was that modern peak-alignment strategies are not robust for elution-order shifts.

The authors furthermore noted that this issue culminated into issues with data processing, information extraction and – ultimately – method optimization.

For the latter focal point within chemometrics, optimization strategies, the authors found that a shift in attention may be in order. While a large number of optimization strategies are reported, most studies do not take into account the validity of the actual optimization criteria. Reciting also the message from earlier works [4], the authors noted that for optimization in chromatography, more attention should be given to the quality descriptors. 

The authors devoted a lot of attention to also explain every core chemometric approach to help non-experts to understand the importance and significance of each of the featured methods. Image shows and example of an explanation of fundamental signal processing methods. Reproduced with permission from [3].

The review contains a massive table with all recent and important applications and developments within chemometrics for chromatography. The different works are sorted by category, including background correction, peak alignment, peak detection and quantification.

The review was initiated within the collaboration with Agilent Technologies through the University Relations program. The work was published open-access and is available download here.

Researchers Bos, Knol, 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) in the framework of the Innovation Fund for Chemistry and from the Ministry of Economic Affairs in the framework of the “PPS‐toeslagregeling”. 

References

[1] Practical Approaches for Overcoming the Challenges of Comprehensive Two-Dimensional Liquid Chromatography, B.W.J. Pirok and Peter J. Schoenmakers, LC-GC Europe, 2018, 31, 242–249, [LINK].

[2] Recent Developments in Two-Dimensional Liquid Chromatography: Fundamental Improvements for Practical Applications, B.W.J. Pirok, D.R. Stoll and P.J. Schoenmakers, Anal. Chem., 2019, 91(1), 240-263, DOI: 10.1021/acs.analchem.8b04841

[3] Recent applications of chemometrics in one- and two-dimensional chromatography, T.S. Bos, W.C. Knol, S.R.A. Molenaar, L.E. Niezen, P.J. Schoenmakers, G.W. Somsen, B.W.J. Pirok, J. Sep. Sci. 43(9-10), 2020, 1678-1727, DOI: 10.1002/jssc.202000011

[4] Optimizing separations in online comprehensive two-dimensional liquid chromatography, B.W.J. Pirok, A.F.G. Gargano and P.J. Schoenmakers, J. Sep. Sci., 2018, 41(1), 68–98, DOI: 10.1002/jssc.201700863

The Authors

Tijmen Bos

Wouter Knol

Leon Niezen

Stef Molenaar

Researchers Bos, Knol, 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) in the framework of the Innovation Fund for Chemistry and from the Ministry of Economic Affairs in the framework of the “PPS‐toeslagregeling”. You can read more about them and find their contact info on the Team page.