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采用响应面法优化甘草中甘草酸的超声辅助提取工艺。

Optimization of ultrasound-assisted extraction of glycyrrhizic acid from licorice using response surface methodology.

作者信息

Jang Seol, Lee A Yeong, Lee A Reum, Choi Goya, Kim Ho Kyoung

机构信息

Mibyeong Research Center, Korea Institute of Oriental Medicine, Daejeon, Korea.

K-herb Research Center, Korea Institute of Oriental Medicine, Daejeon, Korea.

出版信息

Integr Med Res. 2017 Dec;6(4):388-394. doi: 10.1016/j.imr.2017.08.003. Epub 2017 Sep 1.

Abstract

BACKGROUND

The present study optimized ultrasound-assisted extraction conditions to maximize extraction yields of glycyrrhizic acid from licorice.

METHODS

The optimal extraction temperature (X), extraction time (X), and methanol concentration (X) were identified using response surface methodology (RSM). A central composite design (CCD) was used for experimental design and analysis of the results to obtain the optimal processing parameters.

RESULTS

Statistical analyses revealed that three variables and the quadratic of X, X, and X had significant effects on the yields and were followed by significant interaction effects between the variables of X and X ( 0.01). A 3D response surface plot and contour plots derived from the mathematical models were applied to determine the optimal conditions. The optimum ultrasound-assisted extraction conditions were as follows: extraction temperature, 69 °C; extraction time, 34 min; and methanol concentration, 57%. Under these conditions, the experimental yield of glycyrrhizic acid was 3.414%, which agreed closely with the predicted value (3.406%).

CONCLUSION

The experimental values agreed with those predicted by RSM models, thus indicating the suitability of the model employed and the success of RSM in optimizing the extraction conditions.

摘要

背景

本研究优化了超声辅助提取条件,以最大限度地提高甘草中甘草酸的提取率。

方法

采用响应面法(RSM)确定最佳提取温度(X)、提取时间(X)和甲醇浓度(X)。采用中心复合设计(CCD)进行实验设计和结果分析,以获得最佳工艺参数。

结果

统计分析表明,三个变量以及X、X和X的二次项对产率有显著影响,随后X和X变量之间存在显著的交互作用(P<0.01)。应用从数学模型导出的三维响应面图和等高线图来确定最佳条件。最佳超声辅助提取条件如下:提取温度69℃;提取时间34分钟;甲醇浓度57%。在此条件下,甘草酸的实验产率为3.414%,与预测值(3.406%)非常接近。

结论

实验值与RSM模型预测值一致,表明所采用模型的适用性以及RSM在优化提取条件方面的成功。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b6d6/5741391/fe6190ab9d9b/gr1.jpg

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