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野三七紫外可见指纹图谱结合多元统计方法的鉴别分析 被引量:5

Authentication of Panax Vietnamensis Ha et Grushv.by UV-Vis Fingerprint Spectra and Multivariate Statistical Analysis
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摘要 目的为确保野三七的安全、有效使用,建立野三七紫外可见(UV-Vis)指纹图谱结合多元统计方法的鉴别分析方法。方法采集越南产野三七的根部(n=10)与叶部(n=5)、云南产野三七的根部(n=9),提取其有效成分,利用UV-Vis指纹图谱结合多元统计方法对其进行鉴别分析,多元统计方法包括主成分分析法(PCA)和偏最小二乘法判别分析法(PLS-DA)。结果越南产野三七根部与叶部、云南产野三七根部的UV-Vis指纹图谱特征吸收峰分别在206~310nm、206~800nm、206~400nm,存在明显的差异。PLS-DA和PCA分析可使不同产地和部位的野三七样品的鉴别结果可视化。结论 UV-Vis指纹图谱结合多元统计方法能有效地鉴别不同产地和部位的野三七样品,为中药材的质量控制提供参考。 Objective To authenticate Panax Vietnamensis Ha et Grushv.using UV-Vis fingerprint spectra and multivariate statistical analysis,and to ensure the safe and effective use of Panax Vietnamensis Ha et Grushv.Methods The active ingredients of Panax Vietnamensis Ha et Grushv.were extracted from the roots collected in Vietnam(n=10),leaves collected in Vietnam(n=5),and roots collected in Yunnan Province of China(n=9).The Panax Vietnamensis Ha et Grushv.was identified by using UV-Vis fingerprint spectra and multivariate statistical analysis(principal component analysis(PCA)and partial least squares discriminant analysis(PLS-DA)).Results There were significant differences in UV-Vis absorption peak among Vietnamese Panax vietnamensis roots(206-310nm),Vietnamese Panax vietnamensis leaves(206-800nm)and Yunnan Panax vietnamensis roots(206-400nm).The discrimination among different sample parts and geographical locations were visualized by PCA and PLS-DA.Conclusion UV-Vis fingerprint spectra combined with multivariate statistical analysis can effectively identify Panax Vietnamensis Ha et Grushv.samples obtained from different parts and regions,and provide reference for the quality control of traditional Chinese herbs.
出处 《南昌大学学报(医学版)》 CAS 2017年第2期11-16,95,共7页 Journal of Nanchang University:Medical Sciences
基金 国家自然科学基金(81260610 81260608) 科技部"十二五"国家科技支撑计划项目(2011BAI13B01-02 2011BAI13B02-04) 农业部公益性行业科研专项(201303117) 云南省科技计划项目(2012CG024)
关键词 野三七 紫外可见指纹图谱 主成分分析法 偏最小二乘法判别分析法 鉴别 Panax Vietnamensis Ha et Grishv UV-Vis fingerprint spectra principal component analysis partial least squares discriminant analysis identification
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