紫外、近红外、多源复合光谱信息的银杏叶质量快速分析

发布时间：2018-06-06 09:21

  本文选题：银杏叶 + 近红外光谱　； 参考：《光谱学与光谱分析》2017年10期

【摘要】：为考察不同类型光谱信息用于银杏叶质量快速分析的适应性,收集了58个银杏叶样品,采用高效液相色谱方法(HPLC)测定其黄酮及内酯类活性成分的含量作为定标和检验样本的因变量(y)值,测定各样品的紫外、近红外光谱及包含紫外、可见及近红外信号的多源复合光谱信息作为样本的自变量(x)值;分别采用偏最小二乘回归(PLSR),以及根据待测样本在自变量空间最近邻K个样本与待测样本间的相互关系去预测其因变量值的KNN保形映射(KNN-KSR)方法,建立银杏叶活性成分的光谱定量分析模型,比较各光谱模型下检验集样本实测值与模型值的相关系数(R)、均方根偏差(RMSEP)、平均相对误差(MRE)。结果表明PLSR方法所建立的三类光谱模型的各项指标均不及KNN-KSR方法、且其紫外光谱模型的结果极差;而采用KNN-KSR方法根据三类光谱信息预测银杏叶中黄酮、内酯类成分时,R基本能达到0.8、RMSEP分别小于0.05与0.025且其平均相对误差均在8%以下。采用KNN-KSR方法根据紫外、近红外及多源光谱信息均可实现对银杏叶中四类黄酮醇苷成分及三类内酯成分含量的快速分析,突破了现有工作只是基于PLSR方法、根据近红外光谱信息对银杏叶总黄酮醇苷进行定量分析的局限;利用紫外和多源复合光谱信息及KNN-KSR方法进行银杏叶中黄酮醇苷及内酯类成分的快速检测,为银杏叶质量分析提供了更多的方法和选择。多源复合光谱仪具有体积小、成本低,便携的优点,非常适合银杏叶药材现场采购的快速检测及后续产品的质量分析与监控。
[Abstract]:In order to investigate the adaptability of different spectral information for rapid analysis of ginkgo leaf quality, 58 samples of ginkgo biloba leaf were collected. High performance liquid chromatography (HPLC) was used to determine the contents of flavonoids and lactones as dependent variables of calibration and test samples. The multi-source composite spectral information of visible and near-infrared signals is used as the independent variable of the sample. The partial least squares regression method and the KNN shape preserving mapping KNN-KSRs method are used to predict the dependent variables of K samples in the independent variable space according to the correlation between the K samples and the samples to be tested. The spectral quantitative analysis model of the active components of Ginkgo biloba leaves was established, and the correlation coefficients between the measured values and the model values were compared under each spectral model. The root mean square deviation (RMSEPN) and the mean relative error (MREE) were compared. The results showed that the indexes of the three kinds of spectral models established by PLSR method were not as good as those of KNN-KSR method, and the results of UV spectral model were very poor, and the flavonoids in ginkgo biloba leaves were predicted by KNN-KSR method according to the three kinds of spectral information. The average relative error of RMSEP is less than 0. 05 and 0.025, respectively, and the average relative error is less than 8%. According to the ultraviolet, near infrared and multi-source spectral information, the KNN-KSR method can be used to analyze the contents of tetraflavonol glycosides and three kinds of lactones in Ginkgo biloba leaves, which breaks through the existing work only based on the PLSR method. The limitation of quantitative analysis of total flavonol glycosides in ginkgo biloba leaves based on near infrared spectrum information and the rapid detection of flavonol glycosides and lactones in ginkgo biloba leaves by using ultraviolet and multi-source complex spectral information and KNN-KSR method. It provides more methods and choices for the quality analysis of Ginkgo biloba leaves. Multi-source composite spectrometer has the advantages of small volume, low cost and portable. It is very suitable for the quick detection of field procurement of Ginkgo biloba leaves and the quality analysis and monitoring of subsequent products.
【作者单位】： 华东理工大学化学与分子工程学院;
【基金】：上海市科学技术委员会支撑项目(13401901100)资助
【分类号】：O433;S792.95
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本文编号：1986026