Abstract:
OBJECTIVE To optimize the extraction process of Shenliancao prescription used a combination of BP neural network-genetic algorithm and AHP-CRTIC method.
METHODS This study determined the mixed weight coefficients of various indicators using the AHP-CRITIC method, with deacetyl asperulosidic acid methyl ester, scutellarin, and extract yield as indicators. Based on the Box-Behnken response surface design, the non-linear effects of water addition ratio, extraction time, and extraction frequency during the extraction process of Shenliancao prescription were reflected using BP neural network-genetic algorithm. Therefore, the optimal extraction process was determined, and the optimized results were validated through process validation.
RESULTS The BP neural network-genetic algorithm optimized the results of adding water 8 times, boiling for 1.5 h, and boiling twice, and the comprehensive score was shown to be 93.24 through validation experiments.
CONCLUSION Based on BP neural network-genetic algorithm, the extraction process of Shenliancao prescription optimized is stable and feasible. It can be effectively applied to optimize the process parameters, and also provides a new idea for the optimization of the extraction process of traditional Chinese medicine compound preparations.
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