基于多元统计学方法的维药没食子药材红外光谱研究

目的:建立了不同批次的没食子药材红外光谱、二阶导数红外光谱,结合多元统计分析对25批没食子进行了研究,为没食子药材鉴别提供新的方法。方法:采用傅里叶变换红外光谱仪采集不同批次没食子药材红外光谱,利用OMNIC软件对25批没食子药材样品的红外指纹图谱进行基线校正、平滑等处理,采用Microsoft Excel2016进行正态分布分析,采用SPSS 22.0软件进行聚类分析和主成分分析,采用SIMCA 14.1软件进行正交偏最小二乘法-判别分析,以变量重要性投影(VIP)值>1为标准,筛选影响没食子药材成分质量的标志性波数。结果:对25批没食子药材样品进行了红外光谱分析,其相关系数为0.932 7~0.995 6。二阶导数图谱可以分离出原光谱中的一些相互重叠的吸收峰。正态分布曲线结果表明,广西来源与安徽来源没食子药材质量存在明显差异,中国广西、广东与伊朗哈吉阿巴德来源没食子药材样品质量相近。聚类分析表明,25批没食子药材在10~15个组间的间距下可以聚成4类。主成分分析发现,累积方差贡献率为89.565%;广西来源没食子药材的综合得分最高,安徽来源没食子药材的综合得分最低。正交偏最小二乘法-判别分析表明,25批没食子药材样品可很好地聚为4类;共筛选出5个影响没食子药材质量的关键波数。结论:不同来源的没食子样品的红外特征从峰形与峰高都存在着一定的差异性,采用红外光谱、二阶导数图谱可以初步鉴别没食子药材,与正态分布分析、聚类分析、主成分分析及正交最小二乘法-判别分析相结合,可丰富其鉴别信息,为进一步鉴定没食子药材提供参考。

Infrared Spectroscopy of Galla Turcica Herb Based on Multivariate Statistical Method

Objective This paper aims to establish the infrared spectra and second-order derivative IR spectra of different batches of Galla Turcica herbs and study 25 batches of Galla Turcica by multivariate statistical analysis, so as to provide a new method for the identification of Galla Turcica herbs.Methods The IR spectra of different batches of Galla Turcica herbs were collected by Fourier transform IR spectrometer, and the IR fingerprint profiles of 25 batches of Galla Turcica herb samples were corrected in terms of baseline and smoothed by OMNIC software. Microsoft Excel 2016 was used for normal distribution analysis. SPSS 22.0 software was used for cluster analysis and principal component analysis.SIMCA 14.1 software was used for orthogonal partial least squares-discriminant analysis. The variable importance in projection (VIP) value>1 was used as the criterion to screen the characteristic wave number affecting the quality of Galla Turcica.Results The IR spectra of 25 batches of Galla Turcica herb samples were analyzed with correlation coefficients ranging from 0.932 7 to 0.995 6, with 18 peaks. The second-order derivative spectrum could separate some overlapping absorption peaks in the original spectrum. The results of normal distribution curves showed that there was a significant difference in the quality between the Galla Turcica herbs from Guangxi and Anhui provinces, and the quality of Galla Turcica herbs samples from Guangxi and Guangdong provinces, China and Hajiabad, Iran was similar. Cluster analysis showed that 25 batches of Galla Turcica herbs could be clustered into four classes at a spacing of 10-15 groups. Principal component analysis revealed that the cumulative variance contribution value was 89.565%. the highest composite score was obtained for Galla Turcica herbs from Guangxi Province, and the lowest composite score was obtained for Galla Turcica herbs from Anhui Province. The orthogonal partial least squares-discriminant analysis showed that 25 batches of Galla Turcica herb samples could be well clustered into four categories. a total of five key wave numbers affecting the quality of Galla Turcica herbs were screened.Conclusion The IR characteristics of Galla Turcica samples from different sources showed some differences in peak shape and peak height. By using IR spectra and second-order derivative spectra, the preliminary identification of Galla Turcica herbs could be carried out, and normal distribution analysis, cluster analysis, principal component analysis, and orthogonal least squares-discriminant analysis could enrich the identification information and provide a new method for identifying Galla Turcica herbs.