太赫兹时域光谱结合主成分分析线性判别和支持向量机用于大黄样品鉴定（英文）  

Identification of Rhubarb Samples by Terahertz Time Domain Spectroscopy Combined with Principal Component Analysis-Linear Discriminant Analysis and Support Vector Machine

作　　者：汪景荣 张卓勇[1] 杨玉平[2] 相玉红[1] Peter de B.HARRINGTON 

机构地区：[1]首都师范大学化学系,北京100048 [2]中央民族大学理学院,北京100081 [3]Center for Intelligent Chemical Instrumentation,Clippinger Laboratories,Department of Chemistry and Biochemistry,Ohio University

出　　处：《光谱学与光谱分析》2017年第5期1606-1611,共6页Spectroscopy and Spectral Analysis

基　　金：the National Instrumentation Program(2012YQ140005);Natural Science Foundation of China(21275101)

摘　　要：太赫兹时域光谱技术(THz-TDS)结合主成分分析-线性判别分析(PCA-LDA)和支持向量机(SVM)用于正品大黄样品的鉴定。在时域测量41个大黄样品的太赫兹时域透射光谱,然后将这些时域信号转换成频域的吸收系数系数。根据样本的吸收系数建立了主成分分析-线性判别分析和支持向量机的定性分类模型,并对正品和非正品大黄样本的分类模型进行了交叉验证。模型的预测能力和稳定性使用自助拉丁配分进行评价,使用50次自助拉丁配分,配分数为4。使用主成分分析-线性判别分析和支持向量机均得到了满意的结果。提出的方法证明是一种方便、无污染、准确和无需化学处理的鉴定大黄样本的方法。该文提出的步骤可以应用于其他中草药分类和生产的质量控制。Terahertz time domain spectroscopy（THz-TDS）combined with principal component analysis-linear discriminant analysis（PCA-LDA）and support vector machine（SVM）was used for identification of official rhubarb samples.Terahertz time domain transmittance spectra of 41 official and unofficial rhubarb samples were measured in time domain and then were transformed to absorption coefficients in frequency domain.Qualitative classification models of PCA-LDA and SVM were established based on the absorption coefficients and cross validated for identifying official and unofficial rhubarb samples.The predictive ability and stability of the models were evaluated using bootstrapped Latin-partitions method with 50 bootstraps and 4Latin-partitions.Satisfactory results were obtained by using both PCA-LDA and SVM.The proposed method proved to be a convenient,non-polluting,accurate,and non-chemical treatment approach for identifying rhubarb samples.The developed procedure can be easily implemented for quality control in other herbal medicine classification and production.

关 键 词：主成分分析-线性判别分析 支持向量机 太赫兹时域光谱 大黄 

分 类 号：O657.3[理学—分析化学]