Analysis and Evaluation of Quality Traits of Peanut Varieties with Near Infra-Red Spectroscopy Technology
文献类型： 外文期刊
作者： Liu, Hong 1 ; Pandey, Manish K. 3 ; Xu, Zhenjiang 1 ; Rao, Dehua 1 ; Huang, Zhanquan 1 ; Chen, Mengqiang 1 ; Feng, Def 1 ;
作者机构： 1.South China Agr Univ, Coll Agr, Guangzhou 510642, Guangdong, Peoples R China
2.Guangdong Acad Agr Sci, Crops Res Inst, Guangzhou 510640, Guangdong, Peoples R China
3.Int Crops Res Inst Semi Arid Trop, Hyderabad 500324, India
4.Guangdong Prov Key Lab Crops Genet & Improvement, Guangzhou 510640, Guangdong, Peoples R China
关键词： Peanut; Quality traits; Principal components analysis; Cluster analysis
期刊名称：INTERNATIONAL JOURNAL OF AGRICULTURE AND BIOLOGY （ 影响因子：0.822； 五年影响因子：0.906 ）
ISSN： 1560-8530
年卷期： 2019 年 21 卷 3 期
页码：
收录情况： SCI
摘要： Peanut kernel and oil quality are the important features which decide the market value of the produce. In order to identify better source with good kernel and oil quality for use in breeding program, 21 quality traits of 100 peanut varieties were phenotyped under national official field tests in South China. Some of these traits included were contents of crude fat, protein, fatty acids and amino acids using near infra-red spectroscopy technology. The average contents of crude fat, protein, amino acids, oleic and linoleic in these varieties were found to be 51.37, 26.31, 22.611, 44.84 and 34.05%, respectively. The principal component analysis (PCA) identified three component factors representing 74% variation with the clear-cut grouping of 21 quality traits into these component factors i.e., protein and amino acid (PC1), unsaturated fatty acid (PC2) and crude fat (PC3). Furthermore, the cluster analysis divided these 100 peanut varieties into 4 groups with some differences in the quality traits between groups. It is an effective way to comprehensively evaluate the peanut quality by principal component analysis and cluster analysis, which could not only avoid the bias and the instability of single factor analysis, but also explore a practical distinction way for the peanut quality analysis and the quality breeding. (C) 2019 Friends Science Publishers
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