高效液相色谱法结合化学计量学分析不同产地姜黄的指纹图谱  

Application of Chemometrics to the Analysis of High Performance Liquid Chromatographic Fingerprints of C. longaSamples Obtained from Different Origins

作　　者：丁晓晓[1,2] 倪永年[1,2] 

机构地区：[1]南昌大学食品科学与技术国家重点实验室,江西南昌330047 [2]南昌大学化学系,江西南昌330031

出　　处：《分析科学学报》2014年第4期445-450,共6页Journal of Analytical Science

基　　金：国家自然科学基金(No.21065007);南昌大学食品科学与技术国家重点实验室项目(ZZA-201302;ZZB-201303)

摘　　要：本文采用高效液相色谱法(HPLC)对来自于四川、广东和广西的68个姜黄样品进行质量控制方面的研究。通过高效液相色谱-质谱(HPLC-MS)联用技术对姜黄样品中的部分姜黄素类化合物和倍半萜烯类化合物进行了鉴定。进而对所采集到的姜黄样品的HPLC指纹图谱进行主成分分析(PCA)。结果表明,姜黄样品按产地被很好的分成了三类,其所含化合物的含量与产地有关。通过建立偏最小二乘判别分析(PLSDA)、反传人工神经网络(BP-ANN)及最小二乘支持向量机(LS-SVM)这三种有监督模式识别模型对未知样品进行产地预报,结果表明,非线性模型BP-ANN和LS-SVM的预报结果优于线性模型PLS-DA的结果。本文提出的方法可用于姜黄或其他一些中药或食品的质量控制,且一般不需要对它们的组分做定量分析。In this study, a high performance liquid chromatographic（HPLC） method was used for the quality research of sixty-eight C. longa samples from Sichuan,Guangdong and Guangxi provinces. HPLC and HPLC-MS were used to partially identify the chemical constituents of some curcuminoids and bisabolane sesquiterpene. Principal component analysis（PCA） was used to analyze the HPLC fingerprints of the C. longa samples. The results indicated that the C. longa samples could be well classified into three groups according to their geographical origin, and the content of compounds in C. longa samples were also related to the geographical origin. Moreover, three different supervised pattern recognition models including partial least squares discriminant analysis（PLS-DA）, back propagation-artificial neural networks（BP-ANN） and least squares-support vector machine（LS-SVM）,were established to predict the origin of the unknown samples. The results demonstrated that the non-linear models, BP-ANN and LS- SVM,performed better than PLS-DA. Therefore, the presented method might be applicable for the quality control of C. longa samples and other food products, even no quantitative analysis of their components.

关 键 词：姜黄 高效液相色谱指纹图谱 质量控制 化学计量学 模式识别 

分 类 号：O657.72[理学—分析化学]