Optimization of Accelerated Solvent Extraction of Ginsenosides from Cultivated Wild Ginseng Using Response Surface Methodology.

Prev Nutr Food Sci

Department of Food and Nutrition, Kyungnam University, Gyeongnam 51767, Korea.

Published: September 2022

AI Article Synopsis

  • The study developed a new method using response surface methodology (RSM) to optimize accelerated solvent extraction (ASE) for extracting ginsenosides from cultivated wild ginseng.
  • Optimal extraction conditions identified included using 88.64% ethanol, specific temperatures (105.98°C and 129.66°C), and controlled extraction times and pressures.
  • The experimental yields matched closely with predicted values, achieving 7.45 mg/g for the sum of ginsenosides (Rg1, Rb1, and Rg3) and 32.82 mg/g for total ginsenoside content.

Article Abstract

This study's aim is to apply response surface methodology (RSM) to model and optimize the accelerated solvent extraction (ASE) technique for extracting the sum of ginsenosides (Rg1, Rb1, and Rg3) and total ginsenosides from cultivated wild ginseng. To extract ginsenosides from cultivated wild ginseng, a new ASE-based method, combined with RSM modeling and optimization, was developed. The RSM method, which was based on a five-level, three-factor central composite design, was used to obtain the optimal combination of extraction conditions. Briefly, the optimal extraction conditions for the sum of ginsenosides (Rg1, Rb1, and Rg3) and total ginsenoside were as follows: 88.64% ethanol for each extraction solvent, 105.98°C and 129.66°C of extraction temperature, 28.77 and 15.92 min of extraction time, extraction pressure of 1,500 psi, nitrogen purge of 60 s, flush volume of 60%, and one extraction cycle. A 3D response surface plot and contour plot derived from the mathematical models were applied to obtain the optimal conditions. Under the above conditions, the experimental extraction yields of the sum of ginsenosides (Rg1, Rb1, and Rg3) and total ginsenoside content were 7.45 and 32.82 mg/g, respectively, which closely agrees with the model's prediction values.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9585406PMC
http://dx.doi.org/10.3746/pnf.2022.27.3.315DOI Listing

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