基于BP神经网络的纤维性根茎药材超滤总皂苷保留率预测模型
Total saponins retention rate prediction model of ultrafiltration for fibrous rhizome herbs based on BP neural network
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摘要: 为了建立纤维性根茎药材超滤的总皂苷保留率预测模型,避免超滤技术在用于同类药材时需重复优化工艺的问题,以红芪超滤数据为基础,采用Levenberg-Marquardt（LM）算法优化后的BP神经网络（LM-BP神经网络）构建总皂苷保留率预测模型,对模型的预测性能及其对黄芪、甘草的适用性进行考察,并利用连接权值计算输入变量对输出变量的相对贡献率。结果表明,该模型具有良好的预测精度和适用性,对红芪、黄芪和甘草总皂苷保留率预测的平均绝对误差和平均误差率分别为1.10%、1.28%、1.52%和1.48%、1.95%、2.20%,输入变量的相对贡献率大小为膜孔径>压力>温度。该研究可为超滤和智能算法在中药领域的应用提供有益参考。Abstract: The objective of this study was to establish the total saponins retention rate prediction model of ultrafiltration for fibrous rhizome herbs. It could avoid optimizing ultrafiltration technology of similar herbs repeatedly.The model was established based on the ultrafiltration data of Hedysari Radix by Levenberg- Marquardt( LM) arithmetic combining BP neural network.The performance and applicability of the improved LM- BP neural network model were evaluated.Then the relative importance of input variables were assessed using the connection weights of the model.Results showed that the model had the better accuracy and applicability.The mean absolute error for Hedysari Radix,Astragali Radix and Glycyrrhiza uralensis was 1.10%,1.28% and 1.52%,respectively.Mean error rate was 1.48%,1.95% and 2.20%,respectively. The relative contribution of input variables to retention rate presented the order of membrane pore size > pressure > temperature. The study could provide a useful reference for the application of ultrafiltration and intelligence algorithm in Chinese medicine industry.
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