Measuring and using scanning-gradient data for use in method optimization for liquid chromatography

Uijl, Mimi J. den; Schoenmakers, Peter J.; Schulte, Grace K.; Stoll, Dwight R.; Bommel, Maarten R. van; Pirok, Bob W.J.

doi:10.1016/j.chroma.2020.461780

Cited by 23 publications

(7 citation statements)

References 42 publications

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“…Retention modeling is based on first determining the retention coefficients of analytes through several scouting runs. 33 These coefficients can then be used to construct retention models that allow the simulation of separations under a large number of different chromatographic conditions ( i.e. , methods).…”

Section: Resultsmentioning

confidence: 99%

“…The method that led to the best-simulated chromatogram can then be selected as optimal and—in our case—directly programmed into the LC system by the AutoLC algorithm without input from the operator. For this workflow, the selection of scouting gradients ( Figure 1 , phase I) was based on our earlier work, 33 sampling the modifier fraction (φ)-range with three different gradient slopes. For retention modeling, no data preprocessing (phase II) was conducted.…”

Section: Resultsmentioning

confidence: 99%

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Chemometric Strategies for Fully Automated Interpretive Method Development in Liquid Chromatography

et al. 2022

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The majority of liquid chromatography (LC) methods are still developed in a conventional manner, that is, by analysts who rely on their knowledge and experience to make method development decisions. In this work, a novel, open-source algorithm was developed for automated and interpretive method development of LC(−mass spectrometry) separations (“AutoLC”). A closed-loop workflow was constructed that interacted directly with the LC system and ran unsupervised in an automated fashion. To achieve this, several challenges related to peak tracking, retention modeling, the automated design of candidate gradient profiles, and the simulation of chromatograms were investigated. The algorithm was tested using two newly designed method development strategies. The first utilized retention modeling, whereas the second used a Bayesian-optimization machine learning approach. In both cases, the algorithm could arrive within 4–10 iterations (i.e., sets of method parameters) at an optimum of the objective function, which included resolution and analysis time as measures of performance. Retention modeling was found to be more efficient while depending on peak tracking, whereas Bayesian optimization was more flexible but limited in scalability. We have deliberately designed the algorithm to be modular to facilitate compatibility with previous and future work (e.g., previously published data handling algorithms).

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Chemometric Strategies for Fully Automated Interpretive Method Development in Liquid Chromatography

et al. 2022

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“…Alternatively, parameters a and m can be determined from at least two gradient runs differing in slopes [4,[17][18][19][20], where measured retention times are fitted versus initial gradient concentration, φ 0 , and its slope, B.…”

Section: Theorymentioning

confidence: 99%

“…Alternatively, using gradient scouting runs in determining retention parameters is advantageous, as fewer analyses are necessary [17][18][19]. On the other hand, the effect of the gradient slopes, mathematical treatment, and instrumental robustness need to be considered [20].…”

Section: Introductionmentioning

confidence: 99%

Retention prediction of monoamine neurotransmitters in gradient liquid chromatography

Urban

Nechvátalová

Hekerle

2022

J of Separation Science

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Retention prediction of monoamine neurotransmitters has been compared for the generally applied linear solvent-strength model and quadratic polynomial three-parameter model. The design of experiments protocol has been applied to plan linear gradients within the experimental space with altered gradient time, mobile phase flow rate, and column temperature. Relative prediction errors increased at elevated temperature, which is more significant for the linear solvent-strength model when compared to the polynomial model. On the other hand, the predefined design of experiments space controls the retention time errors, as predictions for LC conditions that are outside of the plan are much less accurate and should be avoided. The final part of the work deals with the effect of extracolumn band dispersion on the peak capacity of linear gradients at various gradient times, mobile phase flow rates, and column temperature. The peak capacity determined for corrected experimental data were consistent with the published results dealing with the optimization of peak capacity in gradient elution.

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“…In the course of predicting optimal separation conditions, these tools may suggest gradient conditions with very shallow gradient slopes; as these slopes become more and more shallow, they approach isocratic conditions. It is understood that such predictions are error-prone if they involve extrapolation to gradient slopes outside of the scope of the training data [15].…”

Section: Introductionmentioning

confidence: 99%

Accuracy of retention model parameters obtained from retention data in liquid chromatography

Brau

Pirok

Rutan³

et al. 2022

J of Separation Science

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In liquid chromatography, it is often very useful to have an accurate model of the retention factor, k, over a wide range of isocratic elution conditions. In principle, the parameters of a retention model can be obtained by fitting either isocratic or gradient retention factor data. However, in spite of many of our own attempts to accurately predict isocratic k values using retention models trained with gradient retention data, this has not worked in our hands. In the present study, we have used synthetic isocratic and gradient retention data for small molecules under reversed-phase liquid chromatography conditions. This allows us to discover challenges associated with predicting isocratic k values without the confounding influences of experimental issues that are difficult to model or eliminate. The results indicate that it is not currently possible to consistently predict isocratic retention factors for small molecules with accuracies better than 10%, even when using synthetic gradient retention data. Two distinct challenges in fitting gradient retention data were identified: 1) a lack of 'uniqueness' in the parameters and 2) an inability to find the global optimum fit in a complex fitting landscape. Working with experimental data where measurement noise is unavoidable will only make the accuracy worse.

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Measuring and using scanning-gradient data for use in method optimization for liquid chromatography

Cited by 23 publications

References 42 publications

Chemometric Strategies for Fully Automated Interpretive Method Development in Liquid Chromatography

Chemometric Strategies for Fully Automated Interpretive Method Development in Liquid Chromatography

Retention prediction of monoamine neurotransmitters in gradient liquid chromatography

Accuracy of retention model parameters obtained from retention data in liquid chromatography

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