中国中药杂志

2010, v.35(14) 1815-1817

[打印本页] [关闭]
本期目录(Current Issue) | 过刊浏览(Past Issue) | 高级检索(Advanced Search)

基于主成分降维和神经网络模型识别不同产地的荆芥穗
Recognition of spikes of Schizonepeta tenuifolia from different area based on backpropagation neural network coupled with dimension reduction of principal component analysis

姚卫峰;曹琳琳;单鸣秋;张丽;丁安伟;
YAO Weifeng 1,CAO Linlin1,SHAN Mingqiu1,ZHANG Li1,DING Anwei1,2(1.College of Pharmacy,Nanjing University of Chinese Medicine, Nanjing 210046,China;2.Jiangsu Key Laboratory for Traditional Chinese Medicine Formulae Research,Nanjing 210046,China)

摘要(Abstract):

目的:研究利用紫外-可见光谱预测不同产地的荆芥穗样品。方法:首先采用主成分分析法对10个产地的荆芥穗紫外-可见光谱进行降维处理,将累积贡献率达99.82%的前6个新变量进行反向传播神经网络的建模。结果:所建主成分-神经网络模型预测结果的识别率为100%,均方误差为0.001 0。结论:主成分-神经网络预测模型可用于不同产地荆芥穗药材的分类识别,方法简便快速。
Objective: The spikes of Schizonepeta tenuifolia from different habits were predicted by UV-Vis spectrum.Method: The dimensions of spectrum data obtained from ten habits were reduced by principal component analysis,and the first six new variables with 99.82% of cumulative reliability were put into the backpropagation neural network for model building.Result: The recognition rate of backpropagation neural network coupled with principal component analysis(PCA-BPNN) was 100%,and its mean square error was 0.001 0.Conclusion: PCA-BPNN can be used for the classifying of spikes of S.tenuifolia from different producing area,and this method is simple and fast.

关键词(KeyWords): 荆芥穗;主成分降维;神经网络;紫外-可见光谱
spikes of Schizonepeta tenuifolia;dimension reduction of principal component analysis;neural network;UVVis spectrum

Abstract:

Keywords:

基金项目(Foundation): 国家药典委员会项目(YS131)

作者(Author): 姚卫峰;曹琳琳;单鸣秋;张丽;丁安伟;
YAO Weifeng 1,CAO Linlin1,SHAN Mingqiu1,ZHANG Li1,DING Anwei1,2(1.College of Pharmacy,Nanjing University of Chinese Medicine, Nanjing 210046,China;2.Jiangsu Key Laboratory for Traditional Chinese Medicine Formulae Research,Nanjing 210046,China)

Email:

DOI:

扩展功能
本文信息
服务与反馈
本文关键词相关文章
本文作者相关文章
中国知网
分享