CAJ | 학술논문

[目的]说明微量元素含量与清热类中药疗效之间的相关性.[方法]以微量元素含量为指标,应用主成分分析法,结合SPSS软件对10种清热类中药中的微量元素进行聚类分析.[结果]相关分析结果表明,前4个主因子含有清热类中药微量元素含量83.898%的信息.通过主成分分析建立的主因子模型分别为:F_1=-0.051X_1-0.249X_2-0.700X_3+0.594X_4+0.881X_5-0.331X_6+0.835X_7+0.796X_8+0.196X_9+0.094X_10+0.551X_11,F2=0.651X_1-0.793X_2-0.212X_3+0.656X_4-0.066X_5+0.126X_6-0.314X_7-0.124X_8+0.774X_9+0.084X_10+0.688 X_11,F_3=0.344X-1+0.418X_2-0.343X_3+0.433X_4+0.029X_5-0.208X_6-0.354X_7-0.392X_8+0.081X_9+0.830X_10+ 0.079X_11和F_4=0.331X_1-0.119X_2+0.296X_3+0.127X_4+0.124X_5+0.809X_6+0.191X_7+0.334X_8+0.083X_9+0.444X_10-0.375X_11.通过分析主因子模型可知,第1主因子与变量Mg、Ca、Cu、Zn、Mn的相关性较高,第2主因子与变量K、Na、Mg、Cr、Ni的相关性较高,第3主因子与变量Mo的相关性较高,第4主因子与变量Fe的相关性较高.在层次聚类分析中,样本聚成4类,样本3为一类,样本9为一类,样本6、7、8为一类,样本1、2、4、5、10为一类.[结论]该研究为清热类中草药的开发利用提供了科学依据和理论基础.
[목적]설명미량원소함량여청열류중약료효지간적상관성.[방법]이미량원소함량위지표,응용주성분분석법,결합SPSS연건대10충청열류중약중적미량원소진행취류분석.[결과]상관분석결과표명,전4개주인자함유청열류중약미량원소함량83.898%적신식.통과주성분분석건립적주인자모형분별위:F_1=-0.051X_1-0.249X_2-0.700X_3+0.594X_4+0.881X_5-0.331X_6+0.835X_7+0.796X_8+0.196X_9+0.094X_10+0.551X_11,F2=0.651X_1-0.793X_2-0.212X_3+0.656X_4-0.066X_5+0.126X_6-0.314X_7-0.124X_8+0.774X_9+0.084X_10+0.688 X_11,F_3=0.344X-1+0.418X_2-0.343X_3+0.433X_4+0.029X_5-0.208X_6-0.354X_7-0.392X_8+0.081X_9+0.830X_10+ 0.079X_11화F_4=0.331X_1-0.119X_2+0.296X_3+0.127X_4+0.124X_5+0.809X_6+0.191X_7+0.334X_8+0.083X_9+0.444X_10-0.375X_11.통과분석주인자모형가지,제1주인자여변량Mg、Ca、Cu、Zn、Mn적상관성교고,제2주인자여변량K、Na、Mg、Cr、Ni적상관성교고,제3주인자여변량Mo적상관성교고,제4주인자여변량Fe적상관성교고.재층차취류분석중,양본취성4류,양본3위일류,양본9위일류,양본6、7、8위일류,양본1、2、4、5、10위일류.[결론]해연구위청열류중초약적개발이용제공료과학의거화이론기출.
[Objective] The purpose of the study was to illustrate the correlation between microelement contents in heat-clearing Chinese herbal medicines and their curative effects.[Method] With microelement content as index,the microelement contents in the 10 heat-clearing Chinese herbal medicines were analyzed by principal component analysis,clustering analysis and software SPSS.[Result] The results from correlation analysis indicated that the frontal 4 principal factors contained 83.898% information about microelement contents in heat-clearing Chinese herbal medicines.The principal factor models established through principal component analysis were as follows:F_1=-0.051X_1-0.249X_2-0.700X_3+0.594X_4+0.881X_5-0.331X_6+0.835X_7+0.796X_8+ 0.196X_9+0.094X_10+0.551X_11,F_2=0.651X_1-0.793X_2-0.212X_3+0.656 X_4-0.066X_5+0.126X_6-0.314X_7-0.124X_8 +0.774X_9+0.084X_10+0.688X_11,F_3=0.344X_1+0.418X_2-0.343X_3+0.433X_4+0.029X_5-0.208X_6-0.354X_7-0.392X_8+0.081X_9+0.830X_10+0.079X_11 and F_4=0.331X_1-0.119X_2+0.296X_3+0.127X_4+0.124X_5+0.809X_6+0.191X_7 +0.334X_8+0.083X_9+0.444X_10-0.375X_11.It could be concluded through analyzing principal factor models that the 1st principal factor had better correlation with variables Mg,Ca,Cu,Zn and Mn,the 2nd principal factor had better correlation with variables K,Na,Mg,Cr and Ni,the 3rd principal factor had better correlation with variable Mo and the 4th principal factor had better correlation with variable Fe.In hierarchical clustering analysis,the samples were classified into 4 categories.The samples 3 and 9 were classified into 2 different categories,the samples 6,7 and 8 were classified into a same category and the samples 1,2,4,5 and 10 were classified into a same category.[Conclusion] This study provided scientific basis and theoretical foundation for the exploitation of heat-clearing Chinese herbal medicines.