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Pattern Recognition of the Herbal Drug, Magnoliae Flos According to their Essential Oil Components

机译:根据其精油成分对中药木兰的模式识别

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This paper describes a pattern recognition method of Magnoliae flos based on a gas chromatographic/mass spectrometric (GC/MS) analysis of the essential oil components. The botanical drug is mainly comprised of the four magnolia species (M. denudata, M. biondii, M. kobus, and M. liliflora) in Korea, although some other species are also being dealt with the drug. The GC/MS separation of the volatile components, which was extracted by the simultaneous distillation and extraction (SDE), was performed on a carbowax column (supelcowax 10; 30 m x 0.25 mm x 0.25 μm) using temperature programming. Variance in the retention times for all peaks of interests was within RSD 2% for repeated analyses (n = 9). Of the 74 essential oil components identified from the magnolia species, approximately 10 major components, which is α-pinene, β-pinene, sabinene, myrcene, d-limonene, eucarlyptol (1,8-cineol), γ-terpinene,p-cymene, linalool, α-terpineol, were commonly present in the four species. For statistical analysis, the original dataset was reduced to the 13 variables by Fisher criterion and factor analysis (FA). The essential oil patterns were processed by means of the multivariate statistical analysis including hierarchical cluster analysis (HCA), principal component analysis (PCA) and discriminant analysis (DA). All samples were divided into four groups with three principal components by PCA and according to the plant origins by HCA. Thirty-three samples (23 training sets and 10 test samples to be assessed) were correctly classified into the four groups predicted by PCA. This method would provide a practical strategy for assessing the authenticity or quality of the well-known herbal drug, Magnoliae flos.
机译:本文介绍了一种基于精油成分的气相色谱/质谱(GC / MS)分析的木兰模式识别方法。在韩国,该植物药主要由四种木兰属(M. denudata,M。biondii,M。kobus和M. liliflora)组成,尽管该药还涉及其他一些物种。使用温度程序设计,在碳纤维色谱柱(supelcowax 10; 30 m x 0.25 mm x 0.25μm)上,通过同时蒸馏和萃取(SDE)萃取的挥发性成分进行GC / MS分离。对于重复分析,所有关注峰的保留时间差异均在RSD 2%以内(n = 9)。在从玉兰属植物中鉴定出的74种精油成分中,约有10种主要成分,它们是α-pine烯,β-pine烯,sa烯,月桂烯,d-柠檬烯,真紫杉醇(1,8-cineol),γ-松油烯,p-在这四个物种中,常存在异丙苯,芳樟醇,α-松油醇。为了进行统计分析,通过Fisher准则和因子分析(FA)将原始数据集缩减为13个变量。通过多元统计分析,包括层次聚类分析(HCA),主成分分析(PCA)和判别分析(DA),对精油模式进行了处理。 PCA将所有样品分为四个组,具有三个主要成分,HCA根据植物来源将其分为四个组。将33个样本(23个训练集和10个待测样本)正确分类为PCA预测的四组。该方法将为评估著名草药木兰的真实性或质量提供实用的策略。

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