Optimization of extraction process of main active ingredients in Buyang Huanwu Decoction based on two analytical methods
10.7501/j.issn.0253-2670.2018.01.019
- Author:
Fei YIN
1
Author Information
1. Zhejiang Chinese Medical University
- Publication Type:Journal Article
- Keywords:
Buyang Huanwu Decoction;
Calycosin;
Extraction process;
Ferulic acid;
Formononetin;
Genetic algorithm;
HPLC;
Hydroxyl-safflor yellow A;
Neural network;
Paeoniflorin;
R language;
Response surface methodology
- From:
Chinese Traditional and Herbal Drugs
2018;49(1):135-141
- CountryChina
- Language:Chinese
-
Abstract:
Objective To optimize the extraction process of main active ingredients from Buyang Huanwu Decoction (BHD) by comparing BP neural network combined with genetic algorithm under R language environment with response surface analysis. Methods On the basis of single factor test, the response surface design method was adopted and the main active ingredients of the extract were determined by HPLC. The results were presented in the form of extraction rate. The comprehensive evaluation value of the results was obtained by using the entropy method. Based on this, the best extraction process and the predictive comprehensive evaluation value of extraction rate were firstly obtained by using response surface analysis method. In order to find another best extraction process and comprehensive evaluation predictive value of the main active ingredients in BHD, the optimization of the network model and the discovery of the optimal target were conducted through the BP neural network combined with genetic algorithm under R language environment, respectively. Results The optimum extraction technology in response surface analysis were as follows: Extraction time was 1.8 h; Ethanol concentration was 51%; Extraction temperature was 91 ℃; Liquid material ratio was 14∶1. Under the condition, the comprehensive predicted value was 908.45; The average of the verification test was 897.58; The relative error was 1.20%. The best extraction process by BP neural network combined with genetic algorithm under R language environment was as follows: extraction time was 2 h; Ethanol concentration was 40%; Extraction temperature was 100 ℃; Liquid material ratio was 14∶1. Under the condition, the predicted value was 907.71; The average of the verification test was 905.33; And therelative error was 0.26%. Conclusion Through the comparison of the two analytical methods, it was found that the relative error of neural network combined with genetic algorithm is smaller and the fitting degree with the verification test is higher. That was to say, BP neural network model combined with genetic algorithm in R language environment was more suitable than response surface analysis to optimize the extraction process of the main active ingredients in BHD, which provided a new idea and reference for the discovery of active ingredients and modernization of Traditional Chinese Medicine.
