光谱学与光谱分析, 2017, 37 (8): 2418, 网络出版: 2017-08-30  

FTIR结合化学计量学对三七产地鉴别及皂苷含量预测研究

Study on the Origin Identification and Saponins Content Prediction of Panax notoginseng by FTIR Combined with Chemometrics
李运 1,2,3徐福荣 1张金渝 1,2,3王元忠 2,3
作者单位
1 云南中医学院中药学院, 云南 昆明 650500
2 云南省农业科学院药用植物研究所, 云南 昆明 650200
3 云南省省级中药原料质量监测技术服务中心, 云南 昆明 650200
摘要
不同产地对中药次生代谢产物有显著影响, 产地鉴别有助于中药的科学合理利用; 其次, 有效成分含量检测是评价中药质量的主要手段。 通过傅里叶变换红外光谱结合化学计量学建立快速鉴别三七产地及测定三七中四种主要皂苷的方法, 为三七的科学、 合理、 规范使用以及对三七质量进行快速评价提供依据。 采集5个区域12个产地117个三七样本的红外光谱。 产地鉴别预处理数据采用离散小波变换除去噪音造成的部分高频信号, 偏最小二乘判别对产地判别贡献率大于1的数据进行筛选, kennard-stone算法将117个个体分为70%训练集与30%预测集。 训练集数据用于建立支持向量机判别模型, 交叉验证法用于筛选支持向量机最优参数, 预测集数据对支持向量机判别模型结果进行验证。 皂苷含量预测预处理数据采用标准正态变量变换、 离散小波变换处理; 处理的红外数据设为X变量, 三七样品中通过高效液相色谱法测得的四种皂苷总量设为Y变量, 采用正交信号校正去除红外光谱中与四种皂苷总量无关的干扰数据。 个体数据分为80%训练集与20%预测集, 训练集建立偏最小二乘回归模型, 预测集数据对偏最小二乘回归模型的预测结果进行验证。 结果显示: (1)交叉验证法得到支持向量机判别模型的最优参数为c=2.828 43, g=0.062 5, 训练集的产地判别最优正确率为91.463 4%; (2)支持向量机判别模型参数设置为最优参数, 代入预测集数据, 预测集的产地判别正确率为94.285 7%, 判别正确率较高; (3)训练集建立偏最小二乘回归模型的相关系数R2=0.941 8, 校正均方差RMSEE=4.530 7; (4)代入预测集数据, 预测集的相关系数R2=0.962 3, 外部检验均方差RMSEP=3.855 9, 皂苷预测值与高效液相检测值接近, 预测效果良好。 傅里叶变换红外光谱结合支持向量机能对三七进行产地鉴别, 正交信号校正结合偏最小二乘回归能对三七中四种主要皂苷总量进行准确预测, 为三七质量控制提供一种快速简便、 无损、 高灵敏度的检测方法。
Abstract
Different origins have significant impact on the secondary metabolites of traditional Chinese medicine (TCM), so identification of origins is helpful for scientific and rational utilization of TCM. Additionally, the detection of active ingredient content is the main way to evaluate the quality of TCM. In this study, we established a rapidly method to identify the origins and detect active ingredient content of Panax notoginseng in order to provide some research bases for scientific, rational and specific utilization and rapid quality assessment of P. notoginseng. A total of 117 Fourier transform infrared (FTIR) spectra of P. notoginseng originated from five regions were collected. The discrete wavelet transform was used to process the original spectra in order to remove part of the high-frequency signal caused by noise while partial least squares discriminant analysis (PLS-DA) was used to screen the data with the contribution rate greater than one. Moreover, 70% of the 117 individuals were selected to form the training set by using Kennard-stone algorithm as well as the other 30% were used as prediction set. Training set data were used to establish the discriminant model of support vector machine and the cross-validation method was used for screening optimal parameters as well as the prediction data were utilized to verify the results of discriminant model. The pre-processing data to predict saponins content were processed by standard normal variable transform and discrete wavelet transform. Processed date of FTIR spectra were set as variable X and the total contents of four kinds of saponins in P. notoginseng samples measured by high performance liquid chromatography (HPLC) were set as variable Y. The orthogonal signal correction was used to remove the unrelated data of FTIR to saponins content of P. notoginseng samples. 80% of the individual data were selected as training set and the other 20% were utilized to form the prediction set. The partial least squares regression model was established by training set and the prediction set was utilized to verify the results of the model. The results showed that, (1) The optimal parameters c and g of support vector machine calculated by cross-validation was 2.828 43 and 0.062 5 respectively and the optimal accuracy of training set was 91.463 4%. (2) The support vector machine model was set as the optimal parameter and the accuracy of prediction set was 94.285 7% which showed a high accuracy. (3) The correlation coefficient (R2) and the root mean square error of estimation (RMSEE) of partial least squares regression model established by training set was 0.941 8 and 4.530 7, respectively. (4) The R2 and the root mean square error of prediction (RMSEP) of partial least squares regression model verified by prediction set was 0.962 3 and 3.855 9 respectively showing predictive value of saponins content was close to the value detected by HPLC. FTIR combined with support vector machine could effectively identify different origins of P. notoginseng. Orthogonal single collection and partial least squares regression could accurately predict the value of total four saponins content of P. notoginseng. It could provide a simple, rapid, non-destructive, high sensitive detection method for the quality control of P. notoginseng.

李运, 徐福荣, 张金渝, 王元忠. FTIR结合化学计量学对三七产地鉴别及皂苷含量预测研究[J]. 光谱学与光谱分析, 2017, 37(8): 2418. LI Yun, XU Fu-rong, ZHANG Jin-yu, WANG Yuan-zhong. Study on the Origin Identification and Saponins Content Prediction of Panax notoginseng by FTIR Combined with Chemometrics[J]. Spectroscopy and Spectral Analysis, 2017, 37(8): 2418.

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