Optimization of ultrasound-assisted extraction of glycyrrhizic acid from licorice using response surface methodology
Background: The present study optimized ultrasound-assisted extraction conditions to maximize extraction yields of glycyrrhizic acid from licorice. Methods: The optimal extraction temperature (X1), extraction time (X2), and methanol concentration (X3) were identified using response surface methodolo...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2017-12-01
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| Series: | Integrative Medicine Research |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213422017301373 |
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| Summary: | Background: The present study optimized ultrasound-assisted extraction conditions to maximize extraction yields of glycyrrhizic acid from licorice. Methods: The optimal extraction temperature (X1), extraction time (X2), and methanol concentration (X3) 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 X1, X2, and X3 had significant effects on the yields and were followed by significant interaction effects between the variables of X2 and X3 (p < 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. Keywords: licorice, glycyrrhizic acid, ultrasound-assisted extraction, optimization, response surface methodology |
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| ISSN: | 2213-4220 |