Box-Behnken设计
响应面法
萃取(化学)
人工神经网络
中心组合设计
色谱法
生物系统
计算机科学
材料科学
化学
海马体
海马结构
作者
Zi-Dong Zhang,Yan-Shan He,Hao-Dong Bai,Cong-Jing Shi,Yong-Feng Mo,Yuanning Zeng,Qiuhong Wang,Hai-Xue Kuang
出处
期刊:China journal of Chinese materia medica
[China Journal of Chinese Materia Medica]
日期:2021-05-01
卷期号:46 (10): 2501-2508
标识
DOI:10.19540/j.cnki.cjcmm.20210311.301
摘要
In this paper, the extraction rate of crude polysaccharides and the yield of polysaccharides from Hippocampus served as test indicators. The comprehensive evaluation indicators were assigned by the R language combined with the entropy weight method. The Box-Behnken design-response surface methodology(BBD-RSM) and the deep neural network(DNN) were employed to screen the optimal parameters for the polysaccharide extraction from Hippocampus. These two modeling methods were compared and verified experimentally for the process optimization. This study provides a reference for the industrialization of effective component extraction from Chinese medicinals and achieves the effective combination of modern technology and traditional Chinese medicine.
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