›› 2010, Vol. 37 ›› Issue (11): 94-99.

• 遗传繁育 • 上一篇    下一篇

云南地方猪种肉质NIR指纹主成分分析和聚类分析研究

吴金亮 ,高新,殷云飞,陈吉红,杨国明   

  1. (云南省畜牧兽医科学院,昆明 650224)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2010-11-20 发布日期:2010-11-20
  • 通讯作者: 高新

Study on the Diversity of Yunnan Pig in Meat characteristics Using NIR Spectroscopy Based on Principal Component Analysis and Cluster Analysis

WU Jin-liang,GAO Xin,YIN Yun-fei,CHEN Ji-hong,YANG Guo-ming   

  1. (Yunnan Animal Science and Veterinary Institute,Kunming 650224,China)
  • Received:1900-01-01 Revised:1900-01-01 Online:2010-11-20 Published:2010-11-20

摘要: 试验旨在探讨近红外(near-infrared,NIR)光谱技术应用于云南地方猪种肉质特性分析的可行性。在云南省8个县采集7个云南地方猪种腰背最长肌共计102份样品,利用近红外光谱分析技术,对各猪种近红外指纹图谱进行主成分分析和聚类分析。结果表明,7个猪种肉质的近红外光谱具有共性和差异性,其主成分空间分布位于不同的区域,大河乌猪与大河猪光谱欧氏距离最近,迪庆藏猪肉质特异性明显。利用近红外光谱技术可以准确、快速地对地方猪种进行肉质特性分析,揭示出地方猪种肉质方面的特色资源。

关键词: 猪; 地方品种; 肉质; 近红外; 指纹图谱; 主成分分析; 聚类分

Abstract: The aim of the present study was to investigate the feasibility of identifying the relationship between the Yunnan pig breeds in meat characteristics with near-infrared (NIR) spectroscopy. One hundred and two pork samples were from eight countries of Yunnan province in China. Based on the NIR spectra of the preprocessing pork,the samples were subjected to principal component analysis (PCA) and cluster analysis (CA). The results showed that there were the common features and some differences in NIR spectra from different Yunnan pig breeds,seven pig breeds were scattered different regions of the principal components. According to the euclidean distance of NIR spectra, the relationship of the different breeds could be identified by cluster analysis,the distance of spectra was the shortest between the samples from Dahe wu pig and Dahe pig,Diqing zang pig was special significantly in meat characteristics. So applying near-infrared fingerprint spectroscopy can accurately and quickly analysis meat characteristics of local swine breeds,and find out the special resource of swine breeds in meat.

Key words: pig; breed; pork; near-infrared; principal component analysis; cluster analysis

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