The influences of the organic component of the mobile phase and the column temperature on the retention of ginsenosides on a poly(vinyl alcohol) (PVA) bonded stationary phase operated under hydrophilic interaction chromatographic mode were investigated. The retention of the ginsenosides was found to increase with increasing amount of acetonitrile (MeCN) in the mobile phase, which is typical of hydrophilic interaction chromatographic behavior. It was also found that the retention of the analytes was highly affected by the type of the organic modifier used. Aqueous MeCN (75-90%) gave the most satisfactory retention and separation of ginsenosides Rf, Rg1, Rd, Re, Rc, Rb2 and Rb1 compared with aqueous methanol, isopropyl alcohol or tetrahydrofuran at the same composition levels. The effects of the different types of organic modifiers on the retention of the analytes were attributed to their solvent strength and hydrogen-bond accepting/donating properties. The effect of temperature on the retention of ginsenoside on the PVA-bonded phase was assessed by constructing van't Hoff plots for two temperature ranges: subambient (273-293 K) and ambient-elevated (298-333 K) temperatures. van't Hoff plots for all analytes were linear at the two temperature intervals; however, the slopes of the lines corresponding to ginsenosides Rg1 and Re were completely different from those for the rest of the analytes especially in the subambient temperature range. Enthalpy-entropy compensation (EEC) studies were conducted to verify the difference in thermodynamics observed for ginsenosides Rg1 and Re compared with the other analytes. EEC plots showed that Rf, Rd, Rc, Rb2 and Rb1 were possibly retained by the same retention mechanism, which was completely different from that of Rg1 and Re at subambient temperatures. Retention prediction models were derived using multiple linear regression to identify solute attributes that affected the retention of the analytes on the PVA-bonded phase. The mathematical models derived revealed that the number of hydrogen-bond donors and the ovality of the molecules are important molecular properties that govern the retention of the compounds on the chromatographic system.
Retention prediction models based on multiple linear regression (MLR) and artificial neural network (ANN) for adrenoreceptor agonists and antagonists chromatographed on a polyvinyl alcohol-bonded stationary phase under hydrophilic interaction chromatography were described. The models showed the combined effects of solute structure and mobile phase composition on the retention behavior of the analytes. Using stepwise MLR, the retentions of the studied compounds were satisfactorily described by a five-predictor model; the predictors being the %ACN, the logarithm of the partition coefficient (log D), the number of hydrogen bond donors (HBD), the desolvation energy for octanol (FOct), and the total absolute atomic charge (TAAC). The inclusion of the solute-related descriptors suggested that hydrophilic interactions such as hydrogen bonding and also ionic interactions are possible mechanisms by which analytes are retained on the studied system. ANN prediction models were also derived using the predictors derived from MLR as inputs and log k as outputs. The best network architectures were found to be 5-3-1 for the datasets at pH 3.0 and 4.0, and 5-4-1 for the dataset at pH 5.0. The optimized ANNs showed better predictive properties than the MLR models for both training and test sets under all pH conditions.
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