Development of Gradient Retention Model in Ion Chromatography. Part I: Conventional QSRR Approach

Abstract

New retention methodology that integrates the conventional quantitative structure-retention relationship (QSRR) approach and gradient retention modeling based on isocratic retention data is developed and presented in this paper. Such an integrated approach removes the general QSRR limitation of highly predefined application conditions (i.e., QSRR are generally applicable only under the conditions used during model development) and allows the prediction of retentions over a wide range of different elution conditions (practically for any isocratic or gradient elution profile). At the same time, it retains the ability to predict retention of components unknown to the model, i.e., the components that have not been used in modeling. Ion-exchange chromatography (IC) analysis of carbohydrates was selected as modeling environment. Three regression techniques were applied and compared during QSRR modeling, namely: stepwise multiple linear regression, partial least squares (PLS), and uninformative variable elimination–PLS regression. The obtained prediction results of the best QSRR model (root-mean-square error of prediction = 22.69 %) were similar to those found in the literature. The upgrade from QSRR to the integrated model did not diminish the predictive ability of the model, indicating an excellent potential of the developed methodology not only in IC but also in chromatography in general.

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Acknowledgments

Generous support and help of Thermo Fisher Scientific Corporation is gratefully acknowledged.

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Correspondence to Šime Ukić.

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Published in the special paper collection 19th International Symposium on Separation Sciences with guest editors Tomislav Bolanča and Bogusław Buszewski.

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Ukić, Š., Novak, M., Žuvela, P. et al. Development of Gradient Retention Model in Ion Chromatography. Part I: Conventional QSRR Approach. Chromatographia 77, 985–996 (2014). https://doi.org/10.1007/s10337-014-2653-5

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Keywords

  • Ion chromatography
  • QSRR
  • Gradient retention model
  • Stepwise MLR
  • PLS
  • UVE–PLS