吴喆;张霁;张金渝;徐福荣;王元忠;
WU Zhe;ZHANG Ji;ZHANG Jin-yu;XU Fu-rong;WANG Yuan-zhong;College of Traditional Chinese Medicine,Yunnan University of Traditional Chinese Medicine;Institute of Medicinal Plants,Yunnan Academy of Agricultural Sciences;

• 1:云南中医学院中药学院
• 2:云南省农业科学院药用植物研究所

摘要(Abstract)：

重楼皂苷是中药重楼主要的有效成分,为了快速评价重楼品质,保证重楼在临床治疗中的疗效,本文采用红外光谱结合偏最小二乘回归法(partial least squares regression,PLSR)对重楼中重楼皂苷Ⅰ、重楼皂苷Ⅱ和重楼皂苷Ⅶ进行定量分析,建立快速评价重楼品质的方法。采集78份不同产区、不同种类重楼样品的红外光谱,用高效液相色谱测定重楼样品中重楼皂苷Ⅰ、重楼皂苷Ⅱ及重楼皂苷Ⅶ的含量,将重楼的红外光谱数据和液相数据进行拟合,快速预测3种重楼皂苷含量。原始红外光谱经多元散射校正(multiplicative signal correction,MSC)、标准正态变量(standard normal variate,SNV)、正交信号校正(orthogonal signal correction,OSC)、一阶求导(first derivative,1st Der)、二阶求导(second derivative,2nd Der)预处理后,运用偏最小二乘回归分析建立重楼皂苷的定量预测模型。重楼皂苷Ⅰ和重楼皂苷Ⅱ的最佳预处理方法为MSC+OSC+2nd Der,重楼皂苷Ⅶ的最佳预处理方法为MSC+SNV+OSC+2nd Der;重楼皂苷Ⅰ、重楼皂苷Ⅱ和重楼皂苷Ⅶ3个指标成分定量校正模型的决定系数(R~2)分别为0.930 8,0.934 8,0.912 3 mg·g~(-1);校正均方根误差(root mean square error of estimation,RMSEE)分别为1.855 0,0.632 3,0.001 6 mg·g~(-1);验证模型的决定系数(R~2)分别为0.948 8,0.963 6,0.780 1 mg·g~(-1);预测均方根误差(root mean square error of prediction,RMSEP)分别为1.704 6,1.227 7,0.001 9 mg·g~(-1);定量模型的预测值与真实值比较接近,模型预测效果好,其中重楼皂苷Ⅰ,Ⅱ定量模型效果优于重楼皂苷Ⅶ。该方法无损、快速、准确,可用于重楼中重楼皂苷含量的快速测定。
Polyphyllin is the main active constituent in Paris which was a traditional Chinese medicine. In order to evaluate the quality of Paris rapidly and ensure the efficacy in clinical therapy,we quantified the contents of polyphyllin Ⅰ,polyphyllin Ⅱ and polyphyllin Ⅶ using infrared spectroscopy with partial least squares regression( PLSR). The method for evaluating the quality of Paris was established. Infrared spectra of 78 samples from various species in different origins were collected. The contents of polyphyllin Ⅰ,Ⅱ and Ⅶ were determined by high performance liquid chromatography( HPLC). The HPLC data were combined with the spectral data to predict the contents of three polyphyllin rapidly. Multiplicative signal correction( MSC),standard normal variate( SNV),orthogonal signal correction( OSC),first derivative and second derivative were utilized for the spectral preprocessing. Then,the optimized spectral data were used to establish the quantitative prediction model based on PLSR. The results showed that the best spectral pretreatment of polyphyllin Ⅰ and Ⅱ were MSC + OSC + 2nd Der and that of polyphyllin Ⅶ was MSC + SNV + OSC + 2nd Der. In the quantitative calibration model,the determination coefficients( R~2) of polyphyllin Ⅰ,polyphyllin Ⅱ and polyphyllin Ⅶ were 0. 930 8,0. 934 8 and0. 912 3,respectively while the Root mean square error of estimation( RMSEE) were 1. 855 0,0. 632 3 and 0. 001 6 mg·g~(-1),respectively. In the verification model,the R~2 of polyphyllin Ⅰ,polyphyllin Ⅱ and polyphyllin Ⅶ were 0. 948 8,0. 703 6 and 0. 801 7,respectively,and the root mean square error of prediction( RMSEP) were 1. 704 6,1. 227 8 and 0. 002 0 mg·g~(-1),respectively. Because of the predictive value of quantitative model was closed to the real value,the effect of the model was good. The model of polyphyllin Ⅰ and Ⅱ were better than that of polyphyllin Ⅶ. The developed method was non-destructive,fast,and accurate. It was feasible to determine the content of polyphyllin in Paris.

关键词(KeyWords)： 重楼;红外光谱;定量分析;重楼皂苷;偏最小二乘回归
Paris;infrared spectroscopy;quantitative analysis;polyphyllin;partial least squares regression

Abstract:

Keywords:

基金项目(Foundation): 国家自然科学基金项目(81460584);; 农业部公益性行业科研专项(201303117)

作者(Author): 吴喆;张霁;张金渝;徐福荣;王元忠;
WU Zhe;ZHANG Ji;ZHANG Jin-yu;XU Fu-rong;WANG Yuan-zhong;College of Traditional Chinese Medicine,Yunnan University of Traditional Chinese Medicine;Institute of Medicinal Plants,Yunnan Academy of Agricultural Sciences;

Email:

DOI: 10.19540/j.cnki.cjcmm.20170728.009

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