Principal component and hierarchical cluster analysis for discrimination and classification of mulberry tree varieties and cultivation zones

摘要：

The contents of natural active compounds extracted from mulberry leaves have been used as chemotaxonomic markers to construct chemometric models in order to discriminate and classify 61 varieties of mulberry (Morus alba L.) trees from different geoghaphical origins. Discrimination between samples as a function of the tree varieties and cultivation zone was interpreted by principal component analysis (PCA) and hierarchical cluster analysis (HCA) by contents of active compounds. According to multivariate statistics models, six principal component variables can be considered important to discriminate varieties of mulberry trees, samples of 61 samples were characterized into four groups by HCA on the basis of the PCA pattern. In conclusion, classification of varieties of mulberry trees by contents of active compounds is closely related to geographic latitude.

关键词： discrimination classification mulberry (Morus albaL.) tree natural active compound principal component analysis hierarchical cluster analysis

作者: Jun Wang Rongbin Lv Fuan Wu Yao Liang Yan Zhang Tao Wu Qiusheng Wu

作者单位: School of Biotechnology and Environmental Engineering, Jiangsu University of Science and Technology, School of Biotechnology and Environmental Engineering, Jiangsu University of Science and Technology, Sericultural Research Institute, Chinese Academy of Agricultural Sciences, Zhenjiang 212018, P.R. Ch

会议类型: 国际会议

会议名称: The Third International Conference on Modelling and Simulation(第三届国际建模、计算、仿真、优化及其应用学术会议 ICMS 2010)

会议地点: 无锡

会议语种:英文

页码: 276-280

在线出版日期: 2010-06-04（万方平台首次上网日期，不代表论文的发表时间）