摘要
目的基于中心组合试验设计(central-composite design,CCD),采用遗传神经网络(genetic neural network,GNN)和遗传算法(genetic algorithm,GA)优选黄芪皂苷类成分的微波提取工艺条件。方法构建黄芪皂苷的HPLC指纹图谱,选择含量较高的7种成分(黄芪皂苷I^V和异黄芪皂苷Ⅰ、Ⅱ),以峰面积代替质量浓度,将通过熵权法计算得到的综合得分作为评价指标。在单因素实验基础上,运用CCD开展实验,构建遗传神经网络以建立提取工艺条件与评价指标之间的定量关系,并通过遗传算法优选微波提取黄芪皂苷成分的最佳工艺参数,并与响应面法优化结果进行比较。结果通过GNN与GA获得的最佳提取工艺条件为提取时间260 s、提取功率695 W、乙醇体积分数50%、液料比21.5,7个皂苷成分的综合得分为1 432.584;响应面分析法获得最佳提取工艺条件为提取时间190 s、提取功率880 W、乙醇体积分数70%、料液比18.5,7个皂苷成分的综合得分为1 066.236;GNN与GA所获提取工艺条件可有效增加综合得分。结论通过熵权法以及遗传神经网络构建黄芪皂苷成分与微波提取工艺条件之间的数学模型是可行的,可为实现中药有效部位多成分提取、分离纯化的工艺优选提供一种新的模式。
Objective Based on the central-composite design(CCD), the genetic neural network(GNN) and genetic algorithm(GA)were applied to optimize the microwave extraction conditions of astragalus saponins. Methods The HPLC fingerprint of astragaloside was constructed, and seven components(astragaloside I—V, isoastragaloside I, II) were selected to calculate the comprehensive score by the entropy weight method. On the basis of single factor experiment, CCD was used to designed the experimental condition. The quantitative relationship between extraction conditions and comprehensive score was established by GNN, and the optimal microwave extraction parameters of astragalus saponins were optimized by GA. Results The optimal extraction conditions were obtained by GA-GNN. The extraction time was 260 s, the extraction power was 695 W, the ethanol content was 50%, the ratio of material to liquid was 21.5, and the comprehensive score of seven astragalosides was 1 432.584.Meanwhile, the optimal extraction conditions and comprehensive evaluation scores obtained were by response surface methodology(RSM). The extraction time was 190 s, the extraction power was 880 W, the ethanol content was 70%, the ratio of material to liquid was 18.5, and the comprehensive scores of seven astragaloside were 1 066.236. The experimental results showed that the extraction conditions obtained by GA-GNN can effectively increase the comprehensive score. Conclusion It is feasible to construct a mathematical model between astragaloside components and microwave extraction conditions by using entropy weight method combined with GNN, which can provide a new scientific method for optimizing the extraction, separation, and purification of effective components of traditional Chinese medicine.
作者
黄鹏程
金伟锋
万海同
李畅
陈建真
楼小红
何昱
HUANG Peng-cheng;JIN Wei-feng;WAN Hai-tong;LI Chang;CHEN Jian-zhen;LOU Xiao-hong;HE Yu(Zhejiang Chinese Medical University,Hangzhou 310053,China)
出处
《中草药》
CAS
CSCD
北大核心
2019年第16期3815-3823,共9页
Chinese Traditional and Herbal Drugs
基金
浙江省自然科学基金项目(LZ18H270001)
国家自然科学基金项目(81630105)
浙江省卫生高层次创新人才培养工程项目(2014-108-24)
关键词
黄芪皂苷
微波提取工艺
多目标优选
遗传神经网络
遗传算法
黄芪皂苷Ⅴ
黄芪皂苷Ⅳ
黄芪皂苷Ⅲ
黄芪皂苷Ⅱ
黄芪皂苷Ⅰ
异黄芪皂苷Ⅰ
异黄芪皂苷Ⅱ
astragalus saponin
microwave extraction technique
multiple-objective optimization
genetic neural network
genetic algorithm
astragalosideⅤ
astragalosideⅣ
astragalosideⅢ
astragalosideⅡ
astragalosideⅠ
isoastragalosideⅠ
isoastragalosideⅡ
