FTIR结合化学计量学对三七产地鉴别及皂苷含量预测研究

发布时间：2018-07-08 11:48

  本文选题：傅里叶变换红外光谱 + 三七　； 参考：《光谱学与光谱分析》2017年08期

【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=0.0625,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]:The secondary metabolites of traditional Chinese medicine were significantly affected by different production areas, and the identification of origin was helpful to the scientific and rational utilization of traditional Chinese medicine. Secondly, the detection of effective component content was the main means to evaluate the quality of traditional Chinese medicine. By means of Fourier transform infrared spectroscopy (FTIR) combined with chemometrics, a rapid method for identifying the origin of Panax notoginseng and determining four major saponins in Panax notoginseng was established, which provides the basis for the scientific, rational, standardized use and rapid evaluation of the quality of Panax notoginseng. The infrared spectra of 117 panax notoginseng samples from 12 areas in 5 regions were collected. Partial high frequency signals caused by noise are removed by discrete wavelet transform. Partial least square discriminant is used to screen the data whose contribution rate is greater than 1. 117 individuals are divided into 70% training set and 30% prediction set. The training set data is used to establish the support vector machine discriminant model, the cross-validation method is used to screen the optimal parameters of the support vector machine, and the prediction set data is used to verify the result of the support vector machine discriminant model. The pretreatment data of saponin content prediction were processed by standard normal variable transform and discrete wavelet transform, the infrared data were set as X variable, and the total amount of four saponins measured by HPLC in Panax notoginseng samples was set as Y variable. Orthogonal signal correction was used to remove the interference data from infrared spectrum independent of the total amount of four saponins. The individual data are divided into 80% training set and 20% prediction set. The partial least square regression model is established in the training set and the prediction set data is used to verify the prediction results of the partial least squares regression model. The results show that: (1) the best parameter of SVM discriminant model is 2.828 43g / g 0.0625, and the optimal correct rate of training set is 91.463 ~ 4cm; (2) the parameter of SVM discriminant model is set as the optimum parameter, and the data of prediction set is added into the model. The correct rate of the prediction set is 94.285, and the correct rate is higher. (3) the correlation coefficient R _ (2) O _ (0.941) 8 of the training set is used to establish the partial least squares regression model, and the mean square deviation (RMSEEE) is 4.537. (4) the prediction data are substituted into the prediction set, and the correlation coefficient R _ (2) O _ (0.941 8) is corrected. The correlation coefficient of the prediction set was R2N 0.962 3, and the RMSEPN 3.8555 9. The predicted value of saponins was close to the value of high performance liquid phase detection, and the prediction effect was good. Fourier transform infrared spectroscopy combined with support vector function can identify the origin of Panax notoginseng. Orthogonal signal correction combined with partial least square regression can accurately predict the total amount of four main saponins in Panax notoginseng. It provides a rapid, simple, nondestructive and sensitive method for quality control of Panax notoginseng.
【作者单位】： 云南中医学院中药学院;云南省农业科学院药用植物研究所;云南省省级中药原料质量监测技术服务中心;
【基金】：国家自然科学基金项目(81460581,81260610)资助
【分类号】：O657.33;R284.1
，

本文编号：2107331