Models and objective functions for the optimisation of selectivity in reversed-phase liquid chromatography

Artificial neural network modelling of retention of pesticides in various octadecylsiloxane-bonded reversed-phase columns and water–acetonitrile mobile phase

“…In order to simplify the optimisation procedure, various statistically based predictive methodologies have been proposed [1][2][3]. The greater efficiency of these approaches rests on the fact that a predictive model, once established on the basis of properly designed experiments, can be utilised to estimate retention at any arbitrary condition within the calibration range.…”

Brief analysis of the retention process in RP‐HPLC systems with a C30 bonded stationary phase

“…These models were mainly applied to the prediction and/ or optimization of analyte retention in different chromatographic systems with RP-2, RP-8, or RP-18 type of stationary phases [4,5]. Note that reviews and applications of the most important retention models for different RP-HPLC systems have been presented and thoroughly analysed in many previous papers, e. g. [3 -10].…”

The Combined Effect of the Organic Modifier Content and pH of the Mobile Phase on the Chromatographic Behavior of Some Arylpropionic and Arylacetic Acids to Optimize Their Liquid Chromatographic Determinations

“…There are several approaches to predict the retention behavior of an ionisable solute as a function of chromatographic conditions. Because of the ionization properties of these types of solutes, the factors generally selected to optimize the chromatographic separation are the pH of the mobile phase and the percentage of the organic solvent of the eluent [4][5][6][7][8][9][10][11][12][13][14]. The effect of these parameters on the chromatographic behavior is somewhat more complex since the variation of organic modifier concentration of the mobile phase induces a variation of the degree of ionization as well.…”