The application of hierarchical cluster analysis for clasifying horseradish genotypes (Armoracia rusticana L.) roots

L. Tomsone, Z. Kruma, I. Alsina, L. Lepse
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引用次数: 9

Abstract

Horseradish ( Armoracia rusticana L.) is a perennial herb belonging to the Brassicaceae family; it contains biologically active substances such as phenolic compounds. The aim of the present research was to clasify horseradish root genotypes, based on the total phenol content and antioxidant properties, using the hierarchical cluster analysis (HCA), and to compare them with clusters obtained from data of the molecular random amplified polymorphic DNA (RAPD) analysis. Plant phenolic compounds are among the most important primary antioxidants. The p henolic composition of plants is affected by different factors such as variety, genotype, climate, harvest time, storage, processing. Nine genotypes of horseradish roots harvested at three different times in the period from August to November 2011 were used. Several statistical methods can be used to assess differences in the horseradish genotypes. Using a univariate statistical analysis and standard deviations for each analyzed variable does not help to get a complete insight into the complex analysis. Multivariate statistical methods are appropriate tools for the analysis of a complex data matrix. The hierarchical cluster analysis (HCA) used in the current research is a simple way of grouping the set of available data by their similarities according to a set of selected variables. No similarities were found by clustering the genotypes according to the content of biologically active compounds and molecular analyses. DOI: http://dx.doi.org/10.5755/j01.ct.62.4.3410
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层次聚类分析在辣根基因型分类中的应用
辣根(Armoracia rusticana L.)是属于十字花科的多年生草本植物;它含有生物活性物质,如酚类化合物。本研究的目的是利用层次聚类分析(HCA)对辣根的总酚含量和抗氧化性能进行分类,并与分子随机扩增多态性DNA (RAPD)分析得到的聚类进行比较。植物酚类化合物是最重要的初级抗氧化剂之一。植物的对酚成分受品种、基因型、气候、采收时间、贮藏、加工等因素的影响。采用2011年8月至11月3个不同时期收获的9个基因型辣根。几种统计方法可用于评估辣根基因型的差异。使用单变量统计分析和每个分析变量的标准偏差无助于全面了解复杂分析。多元统计方法是分析复杂数据矩阵的合适工具。当前研究中使用的层次聚类分析(HCA)是一种简单的方法,根据一组选定的变量,根据数据的相似性对可用数据集进行分组。根据生物活性成分的含量和分子分析对基因型进行聚类分析,没有发现相似之处。DOI: http://dx.doi.org/10.5755/j01.ct.62.4.3410
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