中国中药杂志

2019, v.44(24) 5390-5397

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基于PCA-RBF神经网络的中药片剂物料属性-抗张强度预测模型的研究
Material properties and tensile strength prediction model of traditional Chinese medicine tablets based on PCA-RBF neural network

赵海宁;王亚静;商利娜;周梦楠;张怡;叶相印;王雁雯;高迪;
ZHAO Hai-ning;WANG Ya-jing;SHANG Li-na;ZHOU Meng-nan;ZHANG Yi;YE Xiang-yin;WANG Yan-wen;GAO Di;Tianjin University of Traditional Chinese Medicine;Engineering Research Center of Modern Chinese Medicine Discovery and Preparation Technique,Ministry of Education,Tianjin University of Traditional Chinese Medicine;

摘要(Abstract):

构建基于主成分分析-径向基神经网络(PCA-RBF)的物料属性-抗张强度模型,对中药片剂的成型性进行预测。首先采用Design Expert 8. 0软件对不同类型提取物的用量进行混料实验设计,得到具有不同物理性质的中药提取混合物,并测定各提取物的粉体学性质和片剂抗张强度,利用PCA消除原始输入层数据的相关性,降低数据维度,减小网络规模,得到彼此不相关的新变量作为RBF神经网络的输入数据进行训练,并对片剂的抗张强度进行预测。实验结果表明构建的PCA-RBF模型对于片剂抗张强度具有很好的预测效果,最小相对误差为0. 25%,最大相对误差为2. 21%,平均误差为1. 35%,拟合度较高,表现出较好的网络预测精度。该研究初步构建了基于PCA-RBF的中药片剂物料属性-抗张强度的预测模型,为中药制剂质量有效控制方法的建立提供参考。
This paper constructs a prediction model of material attribute-tensile strength based on principal component analysis-radial basis neural network( PCA-RBF),in order to predict the formability of traditional Chinese medicine tablets. Firstly,design Expert8. 0 software was used to design the dosage of different types of extracts,the mixture of traditional Chinese medicine with different physical properties was obtained,the powder properties of each extract and the tensile strength of tablets were determined,the correlation of the original input layer data was eliminated by PCA,the new variables unrelated to each other were trained as the input data of RBF neural network,and the tensile strength of the tablets was predicted. The experimental results showed that the PCA-RBF model had a good predictive effect on the tensile strength of the tablet,the minimum relative error was 0. 25%,the maximum relative error was2. 21%,and the average error was 1. 35%,which had a high fitting degree and better network prediction accuracy. This study initially constructed a prediction model of material properties-tensile strength of Chinese herbal tablets based on PCA-RBF,which provided a reference for the establishment of effective quality control methods for traditional Chinese medicine preparations.

关键词(KeyWords): 中药提取物;粉体学性质;抗张强度;片剂成型性;径向基神经网络;主成分分析;预测模型
traditional Chinese medicine extract;powder properties;tensile strength;tablet formability;radial basis neural network;principal component analysis;prediction model

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基金项目(Foundation): 国家“重大新药创制”科技重大专项(2018ZX09721-005);; 天津市科技计划项目(18ZXXYSY00130)

作者(Author): 赵海宁;王亚静;商利娜;周梦楠;张怡;叶相印;王雁雯;高迪;
ZHAO Hai-ning;WANG Ya-jing;SHANG Li-na;ZHOU Meng-nan;ZHANG Yi;YE Xiang-yin;WANG Yan-wen;GAO Di;Tianjin University of Traditional Chinese Medicine;Engineering Research Center of Modern Chinese Medicine Discovery and Preparation Technique,Ministry of Education,Tianjin University of Traditional Chinese Medicine;

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