Computer-assisted simulation and optimisation of retention in ion chromatography

https://doi.org/10.1016/j.trac.2015.07.015Get rights and content

Abstract

Trends in the use of in silico approaches for the prediction of analyte retention and optimisation of separations in ion chromatography (IC) over the past decade are reviewed. The applicability of gradient elution and the use of complex eluent profiles containing both isocratic and gradient eluent steps are discussed in terms of their applicability for efficient and fast separations. Experimental optimisation of both gradients and complex elution profiles involves a large input in time; thus, in silico optimisation is a highly attractive option. Consequently, the core of this review focuses on insightful modelling of retention time and peak width for simulating isocratic, gradient and multistep gradient separations in IC. Optimisation strategies, current trends in IC modelling as well as challenges and prospects for future development are also discussed.

Section snippets

Ion chromatography

Ion chromatography (IC) is a powerful analytical technique for the separation and determination of inorganic and low molecular weight organic ions. IC falls under the general category of liquid–solid chromatographic methods, in which a liquid mobile phase (or eluent) is passed over a solid stationary phase (SP) and usually then through a suppression device before entering a flow-through detector (typically a conductivity type). The sample to be separated is introduced into the flowing eluent

Isocratic elution

The most fundamental elution regime in IC is isocratic elution, whereby the eluent composition remains constant throughout the entire separation. The constant eluent strength typically leads to several general elution problems in separating mixtures containing analytes with widely differing distribution coefficients. Co-elution, insufficient peak capacity and excessive separation time of the later eluting peaks are typical problems observed in isocratic separations (Fig. 1) [2].

Gradient elution

Gradient elution

Retention time modelling in IC

To perform optimisation in silico, the main factors that determine the resolution in a chromatographic separation must be modelled mathematically. The resolution is given by [21]:Rs=2tR1tR2w1+w2where tR1 and tR2 are the retention times of the adjacent peaks, and w1 and w2 are the base widths of both peaks. It is important to note that the peak widths at half height can also be used to calculate the resolution of a peak pair.

From Equation (1), it is clear that both retention times and peak

Peak width modelling

In the isocratic elution mode, peak width is affected by peak broadening processes [53], [54], and it can be predicted using the rearranged theoretical plate count expression:w=4tRNwhere N is the theoretical plate number of the analyte.

Peak width in gradient separations is controlled by two major factors: peak broadening and band compression. Increasing solvent strength in gradient elution tends to increase the trailing edge of the analyte band relative to the leading edge, which results in

Optimisation of chromatographic conditions

Retention time and peak width modelling allows in silico optimisation of IC separations with a two-step procedure. First, a search area (minimum and maximum boundaries) for each parameter influencing the separation (such as initial eluent concentration and gradient slope) needs to be defined. Next, a set of experimental conditions within the defined search area is then selected, followed by assessing the quality of the potential separation. This process is repeated until the potential

Simulation and optimisation software

‘Virtual Column Separation Simulator’, marketed by ThermoFisher Scientific, is currently the only commercial simulation and optimisation tool available for IC method development. It was originally developed at the Australian Centre of Research on Separation Science (ACROSS) in collaboration with the Dionex Corporation [46].

Virtual Column Separation Simulator provides rapid optimisation as well as a simulation tool for IC separations on two different column diameters (4 and 2 mm). It can predict

Conclusions, challenges and perspectives

Time-consuming trial-and-error approaches are no longer used to develop methods of IC separations with the emergence of robust and reliable computer-assisted optimisation procedures. Computer-assisted optimisation allows users to achieve a desired separation in a short time and to select the requirements of the target separation. The predictive accuracy of the chosen retention model is key to the success of the optimisation, which is somewhat less dependent on the accuracy peak width model used.

Acknowledgements

This research was supported by the Australian Research Council's Discovery funding scheme (project DP0663781) and a Federation Fellowship (FF0668673) to PRH, Singapore GSK-EDB Trust Fund Project ‘Large-scale Chromatography with Green Solvents: Fundamentals and Novel Processes’.

References (64)

  • T. Bolanča et al.

    Development of an inorganic cations retention model in ion chromatography by means of artificial neural networks with different two-phase training algorithms

    J. Chromatogr. A

    (2005)
  • Š. Cerjan-Stefanović et al.

    Simultaneous determination of six inorganic anions in drinking water by non-suppressed ion chromatography

    J. Chromatogr. A

    (2001)
  • K. Héberger

    Quantitative structure–(chromatographic) retention relationships

    J. Chromatogr. A

    (2007)
  • J. Havlis et al.

    High-performance liquid chromatographic determination of deoxycytidine monophosphate and methyldeoxycytidine monophosphate for DNA demethylation monitoring: experimental design and artificial neural networks optimization

    J. Chromatogr. B Biomed. Sci. Appl

    (2001)
  • J.E. Madden et al.

    Prediction of retention times for anions in linear gradient elution ion chromatography with hydroxide eluents using artificial neural networks

    J. Chromatogr. A

    (2001)
  • D.T. Gjerde et al.

    Anion chromatography with low-conductivity eluents. II

    J. Chromatogr. A

    (1980)
  • M. Maruo et al.

    Ion chromatographic elution behaviour and prediction of the retention of inorganic monovalent anions using a phosphate eluent

    J. Chromatogr. A

    (1989)
  • J.E. Madden et al.

    Critical comparison of retention models for the optimisation of the separation of anions in ion chromatography: II. Suppressed anion chromatography using carbonate eluents

    J. Chromatogr. A

    (1999)
  • J.E. Madden et al.

    Critical comparison of retention models for optimisation of the separation of anions in ion chromatography: I. Non-suppressed anion chromatography using phthalate eluents and three different stationary phases

    J. Chromatogr. A

    (1998)
  • P. Zakaria et al.

    Application of retention modelling to the simulation of separation of organic anions in suppressed ion chromatography

    J. Chromatogr. A

    (2009)
  • R.D. Rocklin et al.

    Gradient elution in ion chromatography

    J. Chromatogr. A

    (1987)
  • P. Jandera et al.

    Gradient elution in liquid chromatography : I. The influence of the composition of the mobile phase on the capacity ratio (retention volume, band width, and resolution) in isocratic elution – theoretical considerations

    J. Chromatogr. A

    (1974)
  • V. Drgan et al.

    Computational method for modeling of gradient separation in ion-exchange chromatography

    J. Chromatogr. A

    (2009)
  • U.D. Neue

    Theory of peak capacity in gradient elution

    J. Chromatogr. A

    (2005)
  • U.D. Neue et al.

    Peak compression in reversed-phase gradient elution

    J. Chromatogr. A

    (2006)
  • V. Drgan et al.

    Optimization of gradient profiles in ion-exchange chromatography using computer simulation programs

    Anal. Chim. Acta

    (2011)
  • V.M. Morris et al.

    Examination of a new chromatographic function, based on an exponential resolution term, for use in optimization strategies: application to capillary gas chromatography separation of phenols

    J. Chromatogr. A

    (1996)
  • E. Tyteca et al.

    Computer-assisted multi-segment gradient optimization in ion chromatography

    J. Chromatogr. A

    (2015)
  • B.K. Ng et al.

    Methodology for porting retention prediction data from old to new columns and from conventional-scale to miniaturised ion chromatography systems

    J. Chromatogr. A

    (2011)
  • P.R. Haddad et al.

    Ion Chromatography – Principles and Applications

    (1990)
  • R.A. Shellie et al.

    Prediction of analyte retention for ion chromatography separations performed using elution profiles comprising multiple isocratic and gradient steps

    Anal. Chem

    (2008)
  • Dionex Corporation

    Dionex Reference Library Revision 25 Preliminary

    (2005)
  • Cited by (0)

    View full text