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基于不同算法对杏贝止咳颗粒中间体物料属性与颗粒溶解性的相关性研究

Correlation between material properties and particle solubility of intermediate of Xingbei Zhike Granules based on different algorithms
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摘要 目的 分析比较5种算法对杏贝止咳颗粒(Xingbei Zhike Granules,XZG)溶解性的模型预测效果,确定最优算法和影响颗粒溶解性的中间体粉末关键物料属性(critical material attributes,CMA),提升XZG的溶解性。方法 以25个制粒前粉末物料属性为输入,颗粒溶化率为输出,对比偏最小二乘(partial least squares,PLS)、决策树(decision tree,DT)、随机森林(random forest,RF)、反向传播神经网络(back propagation neural network,BPNN)和支持向量机回归(support vector regression,SVR)算法的模型拟合与预测效果,同时结合变量重要性与方差膨胀因子(variance inflation factor,VIF),筛选关键物料属性。结果 5个算法中,RF算法的模型拟合与预测效果最好,训练集决定系数(R2)为0.865,测试集R2为0.854,均方根误差(root mean square error,RMSE)为1.375,平均绝对百分比误差(mean absolute percent error,MAPE)为1.153%。筛选出的关键物料属性有休止角(α)、崩溃角(β)、平板角(γ)、吸湿性(H)、含水量(HR)、100~212μm颗粒含量(Fm)、长度平均径(D21)。结论 RF算法建立的模型更适合预测XZG的溶解性;通过控制制剂成型用主药和辅料的关键物料属性,可以提高XZG的溶解性,为提升中药颗粒剂的溶解性和品质提供新的思路。 Objective To analyze and compare the model prediction effects of five algorithms on the solubility of Xingbei Zhike Granules(XZG,杏贝止咳颗粒),to determine optimal algorithm and the critical material attributes(CMA)affecting the solubility of particles,and to improve the solubility of XZG.Methods Taking 25 powder material properties before granulation as input,and the solubility of particles as output.The model fitting and prediction effects of partial least squares(PLS),decision tree(DT),random forest(RF),back propagation neural network(BPNN),and support vector regression(SVR)algorithms were compared.At the same time,combined with the importance of variable and variance inflation factor(VIF),the key material properties were screened.Results The RF algorithm has the best model fitting and prediction effect.The training set determination coefficient is 0.865,the test set determination coefficient is 0.854,the root mean square error is 1.375,and the mean absolute percentage error is 1.153%.The CMA screened by had collapseα,β,γ,H,HR,Fm and D21.Conclusion The model established by the RF algorithm is more suitable for predicting the solubility of XZG.The solubility of XZG can be improved by controlling the key material properties of the main drugs and excipients for preparation molding,which provides a new idea for improving the solubility and quality of traditional Chinese medicinal granules.
作者 陶振 洪韵 安双凤 汪盛华 闫明 朱明岩 章晨峰 王振中 肖伟 TAO Zhen;HONG Yun;AN Shuangfeng;WANG Shenghua;YAN Ming;ZHU Mingyan;ZHANG Chenfeng;WANG Zhenzhong;XIAO Wei(State Key Laboratory on Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture(Jiangsu Kanion Pharmaceutical Co.,Ltd.,&Nanjing University of Chinese Medicine),Nanjing 210000,China;Kanion School of Chinese Materia Medica,Nanjing University of Chinese Medicine,Nanjing 210023,China;Jiangsu Kanion Pharmaceutical Co.,Ltd.,Lianyungang 222001,China)
出处 《中草药》 CAS CSCD 北大核心 2024年第22期7644-7652,共9页 Chinese Traditional and Herbal Drugs
基金 国家工信部2023年产业基础再造和制造业高质量发展专项(TC2308068)。
关键词 杏贝止咳颗粒 溶解性 关键物料属性 物理指纹图谱 偏最小二乘算法 决策树算法 随机森林算法 反向传播神经网络算法 支持向量机回归算法 Xingbei Zhike Granules solubility critical material attributes physical fingerprint partial least squares algorithm decision tree algorithm random forest algorithm back propagation neural network algorithm support vector regression algorithm
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