近红外光谱快速鉴别不同产地药用植物重楼的方法研究

摘要

Based on near infrared spectroscopy ,seventy samples of wild medicinal plants of paris polyphylla from Guizhou , Guangxi and Yunnan Provinces were collected to identify their geographical origins .Multiplication signal correction (MSC) , standard normal variate (SNV) ,first derivative (FD) ,second derivative (SD) ,savitzky-Golay filter (SG) ,and Norris deriva-tive filter (ND) were conducted to optimize the original spectra of fifty samples of training set .The results showed that the method MSC combined with SD and ND presented the best results of spectra pretreatment .According to spectrum standard devi-ation ,spectrum range (7 450~ 4 050 cm -1 ) was chosen and principal component analysis-mahalanobis distance (PCA-MD ) method was used to build the model .Its first three principal components ,i .e .cumulative contribution ,determination coefficient (R2 ) ,root-mean-square error of calibration (RMSEC) and root-mean-square error of prediction (RMSEP) were 89.44% , 97.58% ,0.179 6 and 0.266 4 ,respectively ,and the prediction accuracy is 90% .Furthermore ,according to variable importance plot (VIP) ,spectrum range (7 135.33~4 007.35 cm-1 ) was chosen and partial least square discrimination analysis (PLS-DA) was applied to establish the model .Its first three principal components cumulative contribution ,R2 ,RMSEC and RMSEP were 89.28% ,95.88% ,0.234 8 and 0.348 2 ,respectively ,and the prediction accuracy is 100% .Comparing the two methods ,we found that spectrum range chosen by VIP and model built by PLS-DA could provide greater accuracy in identifying paris polyphylla from different origin areas .The method supplied foundation for authenticity and quality evaluation of traditional Chi-nese medicine .%重楼属植物极具药用价值，野生资源主要分布在我国西南省区。应用近红外漫反射光谱，以贵州、广西和云南三个不同产区的70份野生药用植物重楼为研究对象进行产地鉴别。采用多元信号校正、标准正态变量、一阶导数、二阶导数、Norris平滑和Savitzky-Golay 滤波六种方法，对训练集（50份样品）原始光谱进行优化处理。结果表明，多元信号校正结合二阶导数和Norris平滑预处理光谱效果最好。采用光谱标准偏差选择光谱波段（7450～4050 cm -1），结合主成分-马氏距离（principal component analysis-mahalanobis dis-tance ，PCA-MD）建立分类模型，前三个主成分累计贡献率、R2、RMSEC 和 RMSEP 分别为89.44％，97.58％，0.1796，0.2664，预测正确率90％；采用变量重要性图选择光谱波段（7135.33～4007.35 cm -1），结合偏最小二乘判别分析法（partial least square discrimination analysis ，PLS-DA ）建立判别模型，前三个主成分累计贡献率、R2、RMSEC和RMSEP分别为89.28％，95.88％，0.2348，0.3482，预测正确率为100％。比较两种方法的结果可知：采用变量重要性图方法选择光谱波段结合偏最小二乘判别分析法建立的判别模型能更准确地鉴别不同产区的重楼，该方法的建立为中药材真伪和品质评价奠定基础。