Dynamic predictive modeling of extraction process for red ginseng using near-infrared spectroscopy / 中国中药杂志

It is the objective of this study to develop dynamic predictive model for the extraction process of red Ginseng using NIR spectroscopy. NIR spectroscopy was collected online and PLSR models were developed for total quantity of ginsenosides. The performance of NIR prediction model achieved R, RMSEC, RMSEP of 0.996 09, 0.018 9, 0.016 8, respectively. A first order dynamic mass transfer model was combined with NIR prediction of the quality indicator to predict the trajectory of the extraction process based upon the initial 3 or 4 data points. The results showed good agreement with actual measurements indicating reasonable accuracy of the predictive model. It could potentially be used for advanced predictive control of the extraction process.

Multi-objective optimization of extraction process for red ginseng based upon extraction efficiency and cost control / 中国中药杂志

It is the objective of this study to optimize the extraction process of red ginseng to minimize the unit cost of extracting effective ingredients. The relation between the target variables of total quantity of ginsenosides and first extraction time, first extraction solution amount, second extraction time, second extract solution amount were studied with Box-Behnken experimental design method. At the same we also considered the cost of extraction solution and energy usage. The objective function was set as unit cost of target (total quantity of ginsenosides or its purity) for the multi-objective optimization of extraction process. As a result, the optimal process parameters were found as first extraction time (108.7 min), first extraction solution amount folds (12), second extraction time (30 min), second extraction solution amount folds (8) to minimize the unit cost. It indicated that this approach could potentially be used to optimize industrial extraction process for manufacturing Chinese medicine.

Network analysis of ethanol precipitation process for Schisandrae chinensis fructus / 中国中药杂志

A set of central composite design experiments were designed by using four factors which were ethanol amount, ethanol concentration, refrigeration temperature and refrigeration time. The relation between these factors with the target variables of the retention rate of schizandrol A, the soluble solids content, the removal rate of fructose and the removal rate of glucose were analyzed with Bayesian networks, and ethanol amount and ethanol concentration were found as the critical process parameters. Then a network model was built with 2 inputs and 4 outputs using back propagation artificial neural networks which was optimized by genetic algorithms. The R2 and MSE from the training set were 0.983 8 and 0.001 1. The R2 and MSE from the test set were 0.975 9 and 0.001 8. The results showed that network analysis method could be used for modeling of Schisandrae Chinensis Fructus ethanol precipitation process and identify critical operating parameters.