Multiple linear regression and artificial neural network retention prediction models for ginsenosides on a polyamine‐bonded stationary phase in hydrophilic interaction chromatography

Abstract

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.

Hydrophilic properties as a new contribution for computer-aided identification of short peptides in complex mixtures

“…However, studies regarding complex hydrolysates from non-defined proteins are still limited, and very few retention prediction models have been established for HILIC [46][47][48][49][50][51].…”

Miniaturization for the Development of High Performance Separation Systems

“…Development of novel fibrous extraction/separation media having different chemical structures and then different functionalities on the surface could be realized in the near future based on the concept of molecular shape recognition [3][4][5][6][7][8] and the systematic retention mechanism analysis with a help of various modern computational data calculations in LC [106][107][108][109][110][111][112][113][114][115][116][117][118]. Miniaturization of packed columns in GC [119][120][121][122][123] is also possible for high speed analysis with a unique selectivity but without a loss of sample loading capacity.…”

Theory and Practice of UHPLC and UHPLC–MS

“…The model was applied to predict the retention of adrenoreceptor agonists and antagonists on a PVA-bonded stationary phase under HILIC conditions (Quiming et al, 2008a). Also, a two-parameter model employing the %ACN and the solute local dipole index was used to predict the HILIC retention of seven ginsenosides on a polyamine-bonded stationary phase (Quiming et al, 2008b).…”