%O This research was funded by grants from 2017 variety resources protection project (4100-C17051), Natural Science Foundation of Guangdong (2017A030311007; 2015A030313565), International Science & Technology Cooperation Program of Guangdong Province (2013B050800021), Agricultural Science and Technology Program of Guangdong (2013B020301014), Provincial modern agricultural science and technology innovation alliance construction project (2016LM3161). The founders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We declare no conflict of interests.
%K Peanut, Quality traits, Principal components analysis, Cluster analysis, Infra-red Spectroscopy Technology
%A H Liu
%A M K Pandey
%A Z Xu
%A D Rao
%A Z Huang
%A M Chen
%A D Feng
%A R K Varshney
%A Y Hong
%I Friends Science Publishers
%V 21
%L icrisat11305
%J INTERNATIONAL JOURNAL OF AGRICULTURE & BIOLOGY
%N 3
%P 491-498
%R 10.17957/IJAB/15.0920
%D 2019
%X 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.
%T Analysis and Evaluation of Quality Traits of Peanut Varieties with Near Infra-red Spectroscopy Technology