基于近红外光谱技术建立不同产地骆驼蓬药材定性判别模型  

Establishment of different origin Peganum harmala qualitative discrimination model based on near-infrared spectroscopy

作　　者：李莉[1] 李莹[1] 王婷媛[1] 

机构地区：[1]新疆医科大学药学院,新疆乌鲁木齐830011

出　　处：《中国中药杂志》2015年第14期2862-2865,共4页China Journal of Chinese Materia Medica

基　　金：国家自然科学基金项目(81260485)

摘　　要：该研究将光纤传感技术与近红外漫反射光谱相结合,通过主成分分析、聚类分析、SIMCA等方法直接对骆驼蓬药材进行检测,用于识别不同产地的骆驼蓬,建立快速无损鉴别骆驼蓬产地的新方法。研究中建立全波长原始光谱图,通过主成分分析前2个主成分得分图基本可以区分4个产地的骆驼蓬;波长866-2 507 nm,MSC为预处理方法建立最佳聚类分析模型对预测集预测的正确率达到91.67%,可对4个产地骆驼蓬基本区分;波长为1 085-2 507 nm,预处理方法为归一化法建立最佳SIMCA模型,对样品进行预测,只产地昌吉的样品没有被识别,其余样本判别正确,总识别率97.22%。研究结果表明利用近红外漫反射光纤光谱结合SIMCA法识别能力最佳,可用于骆驼蓬的鉴别。The optic-fiber sensor technology combined with near-infrared diffuse reflection spectroscopy was applied to directly ana- lyze Peganum harmala and identify different origin of P. harmala on the basis of principal component analysis, clustering analysis, SIMCA method, which resulted in the establishment of a new method to rapidly and nondestructively identify the origin of P. harmala. The original full wavelength spectrum for principal component analysis and the score of first two principal components can distinguish four origins of P. harmala basically. In the wavelength range of 866-2 507 run, MSC as pretreatment method to establish the best model of cluste- ring analysis to forecast the samples with the accuracy of 91.67%, can distinguish the four origins of P. harma/a while in the wavelength of 1 085- 2 507 nm, normalization method as pretreatment methods to establish a best model of SIMCA to forecast the sample, all the samples except for the changji sample have been identified with a total recognition rate of 97. 22%. The resuhs show that using near infrared diffuse reflectance spectroscopy combined with SIMCA is the best method that can be effectively used to identify the P. harmala.

关 键 词：骆驼蓬 近红外光纤传感技术 主成分分析 聚类分析 SIMCA分析 

分 类 号：R284[医药卫生—中药学]