文献类型：期刊文献

中文题名：基于红芪总多糖保留率建立纤维性根茎药材的超滤预测模型

英文题名：Prediction Model of Ultrafiltration for Fibrous Rhizome Herbs Based on Retention Rate of Total Polysaccharides in Hedysari Radix

作者：李子荣[1];刘晓霞[1];王继龙[1];魏舒畅[1];柳春[1];金辉[1];范凌云[1]

第一作者：李子荣

机构：[1]甘肃中医药大学,兰州730000

第一机构：甘肃中医药大学

年份：2016

卷号：22

期号：3

起止页码：1

中文期刊名：中国实验方剂学杂志

外文期刊名：Chinese Journal of Experimental Traditional Medical Formulae

收录：CSTPCD;;北大核心:【北大核心2014】；CSCD:【CSCD_E2015_2016】；

基金：国家自然科学基金项目(81060345;81460608)

语种：中文

中文关键词：红芪;黄芪;总多糖;超滤;预测模型;BP神经网络

外文关键词：Hedysari Radix; Astragali Radix; total polysaccharides; ultrafiltration; prediction model; BP neural network

摘要：目的:建立人工神经网络用于预测纤维性根茎药材的超滤总多糖保留率。方法:以无机陶瓷膜的膜孔径、滤过压力、药液温度为输入变量,不同超滤条件下红芪酶解提取液中总多糖保留率为输出变量,采用Levenberg-Marquardt算法优化网络参数,建立BP神经网络预测模型,并对模型的性能及适用性进行考察。结果:BP神经网络的拓扑结构为3-6-1,对红芪总多糖保留率预测的平均预测误差、平均绝对误差和平均误差率分别为0.10%,0.98%,1.55%;对黄芪总多糖保留率预测的平均误差率2.77%。结论:建立的模型预测精度较高,适用性较好,可用于预测纤维性根茎药材超滤的总多糖保留率。
Objective： To establish an artificial neural network for predicting retention rate of total polysaccharides in fibrous rhizome herbs by ultrafiltration technology. Method： Taking pore size of inorganic ceramic membrane, pressure and temperature as input parameters, retention rate of total polysaccharides in enzymatic hydrolysate of Hedysari Radix under different ultrafiltration conditions as output parameter,BP neural network was established after network parameters optimized by Levenberg-Marquardt method. Then performance and applicability of model were evaluated. Result： Topological structure of BP neural network was 3-6-1. Mean prediction error, mean absolute error and mean error rate of BP neural network for retention rate of total polysaccharides in Hedysari Radix was 0. 10%,0. 98% and 1. 55%,respectively. Mean error rate for retention rate of total polysaccharides in Astragali Radix was 2. 77%. Conclusion： Accuracy and applicability of BP neural network is good enough to predict retention rate of total polysaccharides in fibrous rhizome herbs by ultrafiltration technology.