星点设计-效应面法优选丹参提取工艺

黄元红; 卫天喜; 张发生; 杨军宣

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星点设计-效应面法优选丹参提取工艺

更新时间：2024-01-04

- 星点设计-效应面法优选丹参提取工艺
- Optimal Extraction of Effective Constituents from Radix Salvia Based on Central Composite Design/Response Surface Methodology
- 中国实验方剂学杂志 2010年16卷第17期页码：28-31
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- 纸质出版日期：2010
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黄元红, 卫天喜, 张发生, 等. 星点设计-效应面法优选丹参提取工艺[J]. 中国实验方剂学杂志, 2010,16(17):28-31.

HUANG Yuan-hong, WEI Tian-xi, ZHANG Fa-sheng, et al. Optimal Extraction of Effective Constituents from Radix Salvia Based on Central Composite Design/Response Surface Methodology[J]. Chinese journal of experimental traditional medical formulae, 2010, 16(17): 28-31.
黄元红, 卫天喜, 张发生, 等. 星点设计-效应面法优选丹参提取工艺[J]. 中国实验方剂学杂志, 2010,16(17):28-31. DOI：

HUANG Yuan-hong, WEI Tian-xi, ZHANG Fa-sheng, et al. Optimal Extraction of Effective Constituents from Radix Salvia Based on Central Composite Design/Response Surface Methodology[J]. Chinese journal of experimental traditional medical formulae, 2010, 16(17): 28-31. DOI：

摘要

目的 :星点设计-效应面法优选丹参的提取工艺。方法 :以丹参酮ⅡA和丹酚酸B提取率

收膏率为考察指标

采用星点设计考察提取乙醇用量、提取时间以及乙醇浓度对提取工艺的影响

对结果进行多元线性回归和二项式拟合

用效应面法选择较佳工艺条件

并进行预测分析。结果 :复相关系数上看

各项指标二项式拟合方程均优于多元线性回归方程

确定最优提取工艺为加9倍量60%乙醇提取3次

每次1.5 h

提取量预测值与理论值偏差小于3%。结论 :星点设计-效应面法优选丹参的提取工艺

方法简便

预测性良好。

Abstract

ObJective: To optimize the process of extracting effective constituents from Radix Salvia by central composite design/response surface methodology. Method: The effects of influence factors such as solvent fold

ethanol concentration and extract time on extraction rate of tanshinone Ⅱa and salvianolic acid were investigated by using box-behnken design and response surface method. The data were imitated using multi-linear equation and second-order polynomial equation. Result: Multiple correlation coefficients from second-order polynomial equation were prior to those from multi-linear equation. The optimum condition of extraction process was 9 fold solvent

1.5 hours for decoction

and 3 times for extraction; the ethanol concentration was 60%. Bias between observed and predicted values was less than 3%. Conclusion: The optimum model was highly predictive.

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