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Abstract
Recent applications of retention modelling in liquid chromatography (2015–2020) are comprehensively reviewed. The fundamentals of the field, which date back much longer, are summarized. Retention modeling is used in retention‐mechanism studies, for determining physical parameters, such as lipophilicity, and for various more‐practical purposes, including method development and optimization, method transfer, and stationary‐phase characterization and comparison. The review focusses on the effects of mobile‐phase composition on retention, but other variables and novel models to describe their effects are also considered. The five most‐common models are addressed in detail, i.e. the log‐linear (linear‐solvent‐strength) model, the quadratic model, the log–log (adsorption) model, the mixed‐mode model, and the Neue–Kuss model. Isocratic and gradient‐elution methods are considered for determining model parameters and the evaluation and validation of fitted models is discussed. Strategies in which retention models are applied for developing and optimizing one‐ and two‐dimensional liquid chromatographic separations are discussed. The review culminates in some overall conclusions and several concrete recommendations.
Highlights
Article Related Abbreviations: adsorption model (ADS), adsorption; Akaike Information Criterion (AIC), Akaike information criterion; artificial neural networks (ANNs), artificial neural network; GA, genetic algorithm; hydrophilic interaction chromatography (HILIC), hydrophilic-interaction chromatography; hydrophobic-subtraction model of Snyder (HSM), hydrophobic-subtraction model; ion-exchange separation (IEX), ion-exchange chromatography; LFER, linear-free-energy relationships; linear-solvent-strength model (LSS), linear solvent strength; mode model (MM), mixed mode; NeueKuss model (NK), Neue–Kuss; normal-phase LC (NPLC), normal-phase liquid chromatography; polycyclic aromatic hydrocarbons (PAHs), polycyclic aromatic hydrocarbon; Q, quadraticLC is one of the most essential and pervasive techniques in the toolbox of analytical chemists
The application of retention modelling by means of empirical models has led to a better understanding of general HPLC, RPLC and HILIC
With lipophilicity determination, the ln kw is mostly determined by extrapolating the LSS model, while it has often been shown that there is a clear deviation from linearity, especially in the lower φ range [120]
Summary
Article Related Abbreviations: ADS, adsorption; AIC, Akaike information criterion; ANN, artificial neural network; GA, genetic algorithm; HILIC, hydrophilic-interaction chromatography; HSM, hydrophobic-subtraction model; IEX, ion-exchange chromatography; LFER, linear-free-energy relationships; LSS, linear solvent strength; MM, mixed mode; NK, Neue–Kuss; NPLC, normal-phase liquid chromatography; PAH, polycyclic aromatic hydrocarbon; Q, quadraticLC is one of the most essential and pervasive techniques in the toolbox of analytical chemists. Retention modeling serves as a useful technique available for analytical chemists to rapidly develop methods. Where t0 is the dead time or hold-up time of the column and k is the analyte retention factor, which is related to the distribution coefficient (K) through k = qs qm cs cm Vs Vm K (2). The linear-solvent-strength model (LSS), the quadratic model (Q), the adsorption model (ADS), the mixed-mode model (MM), and the NeueKuss model (NK). Optimization programs, such as Drylab [18], PEWS2 [19], and MOREPEAKS [20], rely on one or more of these retention models, which are all based on the volume fraction of the modifier (φ) and are two- or three-parameter models. The requirements for input data will be discussed, such as the elution mode and the number of datapoints
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