基于主成分和聚类分析的优质鸡肉质评价模型的建立

发布时间：2018-07-27 20:57

【摘要】：为探讨优质鸡肉质合理评价模型,采用主成分和聚类分析方法对优质肉鸡7个新品系9个与肉质有密切联系的指标进行了分析。结果显示:9个原始指标依据主成分解释总变量和碎石图提取了4个主成分,反映原变量89.82%的信息。第一主成分主要综合了失水率、肌纤维面积、肌纤维密度和pH值信息,命名为质构因子;第二主成分主要综合了VB1、肌纤维密度、肌纤维面积和肌内脂肪(IMF)的信息,命名为作肉质因子;第三主成分综合了嫩度、肉色和肌内脂肪的信息即感官因子;第四主成分综合了肌苷酸、肌内脂肪含量和肉色的信息看作风味因子。聚类分析将9个肉质指标分为3类,聚类结果与主成分分析基本一致,综合利用主成分和聚类分析结果制定了优质鸡的肉质评价模型,为优质鸡选育和肉质评价提供了理论依据。
[Abstract]:In order to study the reasonable evaluation model of high quality chicken quality, the main component and cluster analysis methods were used to analyze the indexes of 7 new lines of high quality broiler chicken which were closely related to meat quality. The results showed that four principal components were extracted from 9 original indexes according to principal component interpretation of total variables and lithotripsy, which reflected 89.82% information of original variables. The first principal component mainly synthesizes the information of water loss rate, muscle fiber area, muscle fiber density and pH value, which is named as the texture factor, and the second principal component mainly synthesizes the information of VB1, muscle fiber density, muscle fiber area and intramuscular fat (IMF), the second principal component mainly synthesizes the information of VB1, muscle fiber density, muscle fiber area and intramuscular fat (IMF). The third principal component synthesizes the information of tenderness, meat color and intramuscular fat, that is sensory factor, and the fourth principal component combines inosine acid, the information of intramuscular fat content and meat color as flavor factor. The nine meat quality indexes were divided into 3 groups by cluster analysis. The results of cluster analysis were consistent with that of principal component analysis (PCA). The evaluation model of meat quality of high quality chickens was established by using the results of principal component analysis and cluster analysis. It provides theoretical basis for high quality chicken breeding and meat quality evaluation.
【作者单位】：中国农业科学院家禽研究所;
【基金】：江苏省科技支撑项目(BE2013386,BE2014364) 现代农业产业技术体系建设专项基金资助项目(CARS-42-Z06)
【分类号】：S831

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