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当前位置: 首页 > 文章 > 河北坝上地区生鲜乳评价模型 安徽农业科学 2018,46 (31) 177-178+181
Position: Home > Articles > Assessment Model of Raw Milk Quality Performance in Bashang Area of Hebei Journal of Anhui Agricultural Sciences 2018,46 (31) 177-178+181

河北坝上地区生鲜乳评价模型

作  者：
李健;许翊冉;于彬;张彤;郭建军;邱殿锐;吴春梅;段宝宁
单  位：
承德市畜牧研究所;邯郸市饲料工业办公室;河北旅游职业学院;盐城市亭湖区动物卫生监督所
关键词：
乳品质;坝上;聚类分析;主成分分析
摘  要：
[目的]综合评价河北坝上地区乳品质。[方法]利用聚类分析和主成分分析的方法对坝上地区乳品质进行分析。[结果]通过主成分分析得出全脂乳固体、酸度、乳脂率对生鲜乳品质的贡献率较大,第1主成分对总方差的贡献率为64.722%,第2主成分对总方差的贡献率为19.960%,2个主成分的累积贡献率达到84.681%;坝上地区乳品质综合评价为11月份最好、8月份最差。乳品质在不同月份的聚类分析中发现,河北坝上地区乳品质10—12月份为一大类,其他月份为一大类,从聚类图中反映了当地气候与乳品质的关系;乳品质各项指标聚类分析中发现,乳脂率、乳蛋白率、乳糖含量先聚为一类,全脂乳含量、酸度与非脂乳固体含量聚为一类。[结论]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建立乳品质评价模型,为乳品质评价提供了理论依据。
译  名：
Assessment Model of Raw Milk Quality Performance in Bashang Area of Hebei
作  者：
LI Jian;XU Yi-ran;YU Bin;Hebei Tourism Vocational College;Animal Husbandry Research Institute of Chengde;
关键词：
Raw milk quality;;Bashang area;;Cluster analysis;;Principal component analysis
摘  要：
[Objective] The research aimed to comprehensively evaluate the quality of milk in the Bashang area of Hebei Province.[Method]The milk quality of Bashang area was analyzed by cluster analysis and principal component analysis.[Result]Principal component analysis showed that the whole milk solids,acidity and milk fat percentage had a greater contribution to the quality of raw milk.The contribution rate of the first principal component to the total variance was 64.722%,the contribution rate of the second principal component to the total variance was 19.960%,and the cumulative contribution rate of the two principal components reached 84.681%; the best month of raw milk quality was December,the worst was August by comprehensive evaluation. In the cluster analysis of different months,the milk quality in Hebei Bashang area could be divided into a large category from October to December,and other months were a large category,the relationship between local climate and milk quality is reflected from the cluster map; In the cluster analysis of various indicators of milk quality,it was found that milk fat percentage,milk protein rate and lactose content first clustered into one category,and whole milk content,acidity and non-fat milk solid content were grouped together. [Conclusion]The clustering results are basically consistent with the principal component analysis. The principal component and cluster analysis results are used to establish the milk quality evaluation model,which provides a theoretical basis for milk quality evaluation.

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