摘要
该文采用中心复合设计(central composite design),以醇沉后的糖去除率、五味子醇甲保留率及可溶性固形物含量为指标考察醇沉乙醇加入量、乙醇浓度、冷藏温度及冷藏时间4个醇沉工艺参数对于醇沉效果的影响。运用贝叶斯网络分析发现乙醇加入量和乙醇浓度为2个重要的醇沉工艺参数,然后运用遗传算法优化的BP人工神经网络建立2输入4输出的网络模型,所得训练集回归模型R^2=0.983 8,MSE=0.001 1,验证集回归模型R^2=0.975 9,MSE=0.001 8,模型拟合精度和预测效果均比较理想。研究表明该方法可有效地用于五味子醇沉过程关键工艺参数辨析与过程建模。
A set of central composite design experiments were designed by using four factors which were ethanol amount, ethanol concentration, refrigeration temperature and refrigeration time. The relation between these factors with the target variables of the retention rate of schizandrol A, the soluble solids content , the removal rate of fructose and the removal rate of glucose were analyzed with Bayesian networks, and ethanol amount and ethanol concentration were found as the critical process parameters. Then a network model was built with 2 inputs and 4 outputs using back propagation artificial neural networks which was optimized by genetic algorithms. The R2 and MSE from the training set were 0. 983 8 and 0. 001 1. The R2 and MSE from the test set were 0. 975 9 and 0.001 8. The results showed that network analysis method could be used for modeling of Scbisandrae Chinensis Fructus ethanol precipitation process and identify critical operating parameters.
出处
《中国中药杂志》
CAS
CSCD
北大核心
2014年第17期3287-3290,共4页
China Journal of Chinese Materia Medica
基金
天津市应用基础与前沿技术研究计划项目(13JCYBJC42000)
关键词
五味子
醇沉
中心复合设计
贝叶斯网络
BP人工神经元网络
Schisandrae Chinensis Fructus
ethanol precipitation
central composite design
Bayesian networks
BP artificial neural networks
