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Optimization of ethanol extraction process for Yihuang powder by orthogonal experiment combined with AHP and GA-BP neural network / 中国药房
China Pharmacy ; (12): 327-332, 2024.
Article in Chinese | WPRIM | ID: wpr-1006618
ABSTRACT
OBJECTIVE To optimize ethanol extraction process of Yihuang powder. METHODS An orthogonal experiment was designed by reflux extraction with ethanol volume fraction, liquid-to-material ratio, and extraction time as investigation factors. The parameters used were the contents of hesperidin, nobiletin, tangeretin, gallic acid, chebulagic acid, chebulinic acid, liquiritin, glycyrrhizineugenol, and the paste-forming rate. The analytic hierarchy process (AHP) was used to calculate the comprehensive score. The optimal ethanol extraction process parameters of Yihuang powder were determined by verifying the results predicted by orthogonal experiment and genetic algorithm (GA)-back propagation neural network (BP neural network). RESULTS The optimal ethanol extraction process parameters, as optimized by orthogonal experiment, were as follows ethanol volume fraction of 60%, liquid-solid ratio of 14∶1 (mL/g), extraction time of 90 min, and extraction for 2 times. The comprehensive score obtained by verification was 79.19. Meanwhile, the optimal ethanol extraction process parameters, optimized by GA-BP neural network, were ethanol volume fraction of 65%, liquid-solid ratio of 14∶1 (mL/g ), extraction time of 60 min, and extraction for 2 times. The comprehensive score obtained by verification was 85.30, higher than the results obtained from orthogonal experiment. CONCLUSIONS The optimization method of orthogonal experiment combined with GA-BP neural network is superior to the traditional orthogonal experiment optimization method. The optimized ethanol extraction process of Yihuang powder is stable and reliable.

Full text: Available Index: WPRIM (Western Pacific) Language: Chinese Journal: China Pharmacy Year: 2024 Type: Article

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Full text: Available Index: WPRIM (Western Pacific) Language: Chinese Journal: China Pharmacy Year: 2024 Type: Article