基于广义回归神经网络的中药片剂崩解时限预测

目的:通过构建广义回归神经网络(GRNN)模型对中药浸膏粉制得片剂的崩解时限进行预测。方法:以黄芪为模型药,将黄芪浸膏粉与微晶纤维素、乳糖混匀制备具有不同粉体学性质的混合黄芪粉,通过直接压片法制成黄芪片剂,并测定各组混合黄芪粉的粉体学性质及对应片剂的崩解时限,利用主成分分析(PCA)消除原始数据之间的相关性,得到新的主成分因子作为GRNN模型的输入层,崩解时限作为输出层进行网络训练,并通过验证组数据对崩解时限进行预测,与实际值比较计算网络预测精度。结果:通过PCA将原9个互相存在关联性的变量(Hausner比值、真密度、振实密度、压缩度、休止角、松密度、孔隙率、溶解性固体总量及含水量)进行分析得到3个主成分因子,降低了网络复杂度;基于该预测方法的崩解时限预测值与实际值吻合度较高,崩解时限误差0.01~1.34 min,平均相对误差3.16%。结论:基于建立的GRNN数学模型,可通过测定黄芪浸膏粉物理性质对其片剂崩解时限进行准确预测,对研究中药片剂的崩解时限具有一定参考价值。

Prediction of Disintegration Time of Traditional Chinese Medicine Tablets Based on Generalized Regression Neural Network

Objective: This paper constructs a generalized regression neural network(GRNN)model to predict the disintegration time of traditional Chinese medicine(TCM)tablets. Method: Taking Astragali Radix as a model drug,the mixed Astragali Radix powders with different powder properties were prepared by mixing Astragali Radix extract powders with microcrystalline cellulose and lactose, which were made to Astragali Radix tablets by direct compression method. The powder properties of mixed Astragali Radix powders and the disintegration time of Astragali Radix tablets were determined,respectively. The correlation between the original data was eliminated by principal component analysis(PCA). The principal component factors were used as the input layer of the GRNN model,and the disintegration time was used as the output layer for network training. Finally,the verification group data was used to predict the disintegration time,and the network prediction accuracy was calculated by comparing with the actual value. Result: Three principal component factors were obtained through PCA by analyzing the original nine variables that were correlated with each other(Hausner ratio,true density,tap density,compression degree,angle of repose,bulk density,porosity,water content and total dissolved solids),which reduced the complexity of the network. The prediction value of the disintegration time based on this prediction method was in good agreement with the actual value,the error of disintegration time was 0.01-1.34 min and the average relative error was 3.16%. Conclusion: Based on the GRNN mathematical model,the physical properties of Astragali Radix extract powders can be used to accurately predict the disintegration time of Astragali Radix tablets, which provides a reference for studying the disintegration time of TCM tablets.