Optimization of polysaccharide extraction from Hippocampus by deep neural network and Box-Behnken design-response surface methodology / 中国中药杂志

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.