Far UV circular dichroism (CD) and fluorescence spectroscopy were used to investigate the interaction between bovine serum albumin (BSA) and metallothionein (MT). Both spectroscopic probes gave proofs on the interaction of the two proteins. At pH 4.0, 7.0 and 9.0, BSA showed a negative increase in ellipticity at the far-UV range in the presence of MT indicating an increase in α-helical content and a decrease in β-sheet structure. In the presence of MT at pH 4.0 and 9.0, a decrease in fluorescence intensity was observed. Tryptophan fluorescence quenching experiments were also performed using acrylamide and KI as quenchers. Under acidic conditions, a four-fold increase in Stern-Volmer constant (K SV ) was observed for BSA + MT. At neutral and basic conditions, a decrease in K SV values were observed which indicates conformational changes in BSA upon binding MT. These changes are close to the region where the tryptophan residues are located in the protein.

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.

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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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Simultaneous enantioseparation with sensitive detection of four basic drugs, namely methoxamine, metaproterenol, terbutaline and carvedilol, using a 20-mum ID capillary with native beta-CD as the chiral selector was demonstrated by the large-volume sample stacking method. The procedure included conventional sample loading either hydrodynamically or electrokinetically at longer injection times without polarity switching and EOF manipulation. In comparison to conventional injections, depending on the analyte, about several hundred- and a thousand-fold sensitivity enhancement was achieved with the hydrodynamic and the electrokinetic injections, respectively. The simple method developed was applied to the analysis of racemic analytes in serum samples and better recovery was achieved using hydrodynamic injection than electrokinetic injection.

The development of retention prediction models for the seven ginsenosides Rf, Rg1, Rd, Re, Rc, Rb2, and Rb1 on a polyamine-bonded stationary phase in hydrophilic interaction chromatography (HILIC) is presented. The models were derived using multiple linear regression (MLR) and artificial neural network (ANN) using the logarithm of the retention factor (log k) as the dependent variable for four temperature conditions (0, 10, 25, and 40 degrees C). Using stepwise MLR, the retention of the analytes in all the temperature conditions was satisfactorily described by a two-predictor model wherein the predictors were the percentage of ACN (%ACN) in the mobile phase and local dipole index (LDI) of the compounds. These predictors account for the contribution of the solute-related variable (LDI) and the influence of the mobile phase composition (%ACN) on the retention behavior of the ginsenosides. A comparison of the models derived from both MLR and ANN revealed that the trained ANNs showed better predictive abilities than the MLR models in all temperature conditions as demonstrated by their higher R(2) values for both training and test sets and lower average percentage deviation of the predicted log k from the observed log k of the test compounds. The ANN models also showed excellent performance when applied to the prediction of the seven ginsenosides in different sample matrices.

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