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近红外光谱技术在金银花和山银花判别中的应用研究 被引量:20

Application of Near Infrared Spectroscopy in Identification of Lonicerae Japonicae Flos and Lonicerae Flos
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摘要 目的:通过近红外光谱技术研究金银花和山银花在真伪鉴别中的应用。方法:本研究利用近红外光谱方法识别金银花和山银花的差异性,通过收集来自不同产地的101种金银花和山银花样品,采用近红外光谱选择5300~5550、6900~7500、8200~12000 cm^-1处光谱波段进行标准化预处理后,在主成分分析的聚类基础上通过SIMCA模式识别原理对金银花和山银花分别建立了类模型。结果:模型基本能正确识别金银花和山银花,所建立的方法可靠、快速简单,可作为金银花真伪判别的一种有效控制方法。结论:近红外光谱结合SIMCA模式识别在金银花和山银花分类识别具有可行性。 Objective:To study the application of Lonicerae Japonicae Flos and Lonicerae Flos in the identification of authenticity by near-infrared spectroscopy.Methods:The differences between Lonicerae Japonicae Flos and Lonicerae Flos were detected identified by near-infrared spectroscopy at 5300-5550,6900-7500,8200-12000 cm^-1,101 samples collected from different regions were detected,and after standardization preprocessing in the spectral band,based on the clustering of principal component analysis,a class model was established for Lonicerae Japonicae Flos and Lonicerae Flos by SIMCA pattern recognition principle.Results:The model correctly identified Lonicerae Japonicae Flos and Lonicerae Flos,the established method is reliable,fast and simple,and can be used as the control method for the authenticity of Lonicerae Japonicae Flos.Conclusion:Near-infrared spectroscopy combined with SIMCA pattern recognition method is feasible in the classification and identification of Lonicerae Japonicae Flos and Lonicerae Flos.
作者 刘征辉 魏静娜 赵琳琳 赵云平 薛天凯 黄迪 郭永泽 程奕 LIU Zheng-hui;WEI Jing-na;ZHAO Lin-lin;ZHAO Yun-ping;XUE Tian-kai;HUANG Di;GUO Yong-ze;CHENG Yi(Tianjin Agricultural Quality Standards and Testing Technology Research Institute,Tianjin 300081,China;Tianjin High-standard Quality Testing Lab.,Tianjin 300081,China;Tianjin Center for Quality Testing of Traditional Chinese Medicine,Tianjin 300081,China)
出处 《中国现代中药》 CAS 2020年第1期58-64,共7页 Modern Chinese Medicine
基金 2018年天津市重点研发专项京津冀三地协同创新研发项目(18YFSDZC00020)。
关键词 近红外光谱 金银花 山银花 主成分分析 SIMCA模式识别 真伪判别 Near-infrared spectroscopy Lonicerae Japonicae Flos Lonicerae Flos principal component analysis SIMCA pattern recognition authenticity discrimination
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