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Acta Agron Sin ›› 2017, Vol. 43 ›› Issue (03): 464-470.doi: 10.3724/SP.J.1006.2017.00464

• RESEARCH NOTES • Previous Articles    

Diversity and Correlation of Quality Traits in Quinoa Germplasms from North China

HU Yi-Bo,YANG Xiu-Shi,LU Ping*,REN Gui-Xing*   

  1. Institute of Crop Science, Chinese Academy of Agricultural Sciences / National R&D Center For Coarse Cereal Processing, Beijing 100081, China
  • Received:2016-08-10 Revised:2016-11-02 Online:2017-03-12 Published:2016-11-18
  • Contact: 任贵兴, E-mail: renguixing@caas.cn, Tel: 010-62156596; 陆平, E-mail: luping@caas.cn, Tel: 010-62186625 E-mail:huyibocaas@163.com
  • Supported by:

    This study was supported by Science and Technology Partnership Program, Ministry of Science and Technology of China (KY201402023), and the Agricultural Science and Technology Innovation Program of CAAS.

Abstract:

Quinoa (Chenopodium quinoa Willd.) is one of the oldest crops in the American continent. It has been recognized as a nutritious grain all over the world. In this experiment, thirteen indicators of quality in 25 quinoa germplasms were analyzed using principal component analysis (PCA) and cluster analysis. The fat had significantly positive correlation with total flavonoids (0.55), and remarkably negative correlation with lysine and histidine (–0.52 and –0.54); and the correlation among total flavonoids, polyphenols and saponins was positively significant. Thirteen traits belonged in five principal components, with a total contribution rate of 92.6%. Twenty-five quinoa germplasms were divided into four categories through cluster analysis, and there were larger different in genetic distance and quality between categories. It is an effective way to comprehensively evaluate the quinoa quality by PCA and cluster analysis, which could not only avoid the bias and the instability of single factor analysis, but also provide an important scientific basis for quinoa application and quality breeding.

Key words: Quinoa, Quality traits, Correlation, Principal component analysis, Cluster analysis

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