High Performance Liquid Chromatographic Separation and Quantitative Analysis of Ginsenosides Using a Polyvinyl Alcohol-Bonded Stationary Phase

Quiming, Noel S.; Denola, Nerissa L.; Soliev, Azamjon B.; Saito, Yoshihiro; Jinno, Kiyokatsu

doi:10.1365/s10337-007-0258-y

Cited by 17 publications

(15 citation statements)

References 28 publications

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“…These preparations, namely, ginseng powder, oral solution and fluid extract, were reported to contain the ginsenosides Rf, Rg1, Rd, Re, Rc, Rb2 and Rb1 in varying concentrations. 25,26,41 A mobile phase composed of MeCN/H2O at a ratio of 82:18 was found to give the best separation of all ginsenosides in the temperature range used in this study. Typical chromatograms of the standard and the three different samples at 5˚C are shown in Fig.…”

Section: Retention Prediction In Different Sample Matricesmentioning

confidence: 87%

“…Hydrophilic interaction chromatography (HILIC) provides an alternative approach for the separation and quantitative determination of ginsenosides in ginseng preparations. 10,25,26 For the analysis of ginsenosides using HILIC, polar stationary phases, such as the diol 10,25 and polyvinyl alcohol (PVA)-bonded phase 26 have been reported. In previous studies 25,26 it was found that under the HILIC mode of separation, temperature is an important factor that affects the retention behavior of ginsenosides and a parameter that can be optimized to obtain good separation of these analytes.…”

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confidence: 99%

“…10,25,26 For the analysis of ginsenosides using HILIC, polar stationary phases, such as the diol 10,25 and polyvinyl alcohol (PVA)-bonded phase 26 have been reported. In previous studies 25,26 it was found that under the HILIC mode of separation, temperature is an important factor that affects the retention behavior of ginsenosides and a parameter that can be optimized to obtain good separation of these analytes. These studies also revealed that complete separation of the seven ginsenosides (Rf, Rg1, Rd, Re, Rc, Rb2 and Rb1) can be achieved in both the diol and PVA-bonded phases at subambient temperatures using aqueous mobile phases containing high percentages of acetonitrile.…”

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confidence: 99%

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Retention Prediction Modeling of Ginsenosides on a Polyvinyl Alcohol-bonded Stationary Phase at Subambient Temperatures Using Multiple Linear Regression and Artificial Neural Network

et al. 2008

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Section: Retention Prediction In Different Sample Matricesmentioning

confidence: 87%

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confidence: 99%

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confidence: 99%

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Retention Prediction Modeling of Ginsenosides on a Polyvinyl Alcohol-bonded Stationary Phase at Subambient Temperatures Using Multiple Linear Regression and Artificial Neural Network

et al. 2008

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“…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.…”

Section: Future Prospectivementioning

confidence: 99%

Miniaturization for the Development of High Performance Separation Systems

Saito

Ueta

2017

Chromatography

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Miniaturization is a key technological approach to realize high performance separation systems that have several important features required in modern analytical methods. A smaller scale of the stationary phase synthesis enables a systematic analysis of the retention behavior and also a more efficient development of novel stationary phases in liquid chromatography. Development of microscale sample preparation technique has been regarded as a valuable supporting approach to the development of microscale separation methods. Effective on-line coupling of the microscale sample preparation and separation allows an efficient and rapid analysis without a loss of sensitivity for most of the analytical separations. In this review, development of the miniaturized separation techniques and the applications have been described from the view point of the authors' research group, including the development of novel stationary phase, microscale sample preparation techniques for liquid and gas sample matrices.

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“…Hydrophilic interaction chromatography (HILIC) using diol [3,22] or poly(vinyl alcohol) (PVA)-bonded stationary phases [23] provides an alternative approach for the separation and quantitative determination of ginsenosides in ginseng preparations. In previous studies [22 -24], it was found that under HILIC mode of ginsenoside separation, different selectivities could be achieved by changing the column temperature.…”

Section: Introductionmentioning

confidence: 99%

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

Quiming

Denola

Saito

et al. 2008

J of Separation Science

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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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High Performance Liquid Chromatographic Separation and Quantitative Analysis of Ginsenosides Using a Polyvinyl Alcohol-Bonded Stationary Phase

Cited by 17 publications

References 28 publications

Retention Prediction Modeling of Ginsenosides on a Polyvinyl Alcohol-bonded Stationary Phase at Subambient Temperatures Using Multiple Linear Regression and Artificial Neural Network

Retention Prediction Modeling of Ginsenosides on a Polyvinyl Alcohol-bonded Stationary Phase at Subambient Temperatures Using Multiple Linear Regression and Artificial Neural Network

Miniaturization for the Development of High Performance Separation Systems

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

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