PMID: 18187863Jan 12, 2008Paper

Retention prediction modeling of ginsenosides on a polyvinyl alcohol-bonded stationary phase at subambient temperatures using multiple linear regression and artificial neural network

Analytical Sciences : the International Journal of the Japan Society for Analytical Chemistry
Noel S QuimingKiyokatsu Jinno

Abstract

The development of retention prediction models for the seven ginsenosides (Rf, Rg1, Rd, Re, Rc, Rb2 and Rb1) on a polyvinyl alcohol (PVA)-bonded stationary phase at subambient temperatures 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. Using stepwise MLR, the retention of the analytes under all temperature conditions was satisfactorily described by a three-predictor model; the predictors being the percentage of acetonitrile (%MeCN) in the mobile phase, the number of hydrogen bond donors (HBD) and the ovality (Ov) of the compounds. These predictors account for the contribution of the solute-related variables (HBD and Ov) and the influence of the mobile phase composition (%MeCN) on the retention behavior of the ginsenosides. The MLR models produced adequate fits, as proven by the high calibration R2 values of the predicted versus the observed log k (> 0.95) and good predictive properties, as indicated by the high cross-validated q2 (> 0.93) and high R2 (> 0.95) values obtained from the test set. ANN modeling was also conducted using the predictors that were derived from MLR as inputs and...Continue Reading

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Citations

Dec 14, 2011·Analytical Sciences : the International Journal of the Japan Society for Analytical Chemistry·Kentaro YogoKiyokatsu Jinno
Jan 1, 2009·Acta Crystallographica. Section E, Structure Reports Online·Aliakbar D Khalaji, Jim Simpson
Jan 1, 2009·Acta Crystallographica. Section E, Structure Reports Online·Aliakbar Dehno KhalajiMichal Dušek
Nov 6, 2012·Acta Crystallographica. Section E, Structure Reports Online·Yong Koo KangJoo Hwan Cha
Sep 6, 2008·Phytochemical Analysis : PCA

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