Relationship of total bacterial and somatic cell counts with milk production and composition - multivariate analysis
Relationship of total bacterial and somatic cell counts with milk production and composition - multivariate analysis
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DOI：
10.4025/actascianimsci.v41i1.42568
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发表时间：
2019-01-01
期刊：
影响因子：
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通讯作者：
Haygert-Velho, Ione Maria Pereira
中科院分区：
文献类型：
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作者：
Botton, Fernanda Scolari;Alessio, Dileta Regina Moro;Haygert-Velho, Ione Maria Pereira
本研究旨在利用多变量统计分析评估牛奶的每月产量、成分和质量（总和脱脂干提取物、乳糖、脂肪和蛋白质）及其与体细胞计数（SCC）和细菌总数（TBC）的关系。数据来自某奶牛场两年（2015年1月至2016年12月）。 SCC 和 TBC 变量转化为体细胞评分 (SCS) 和 log10 (LogTBC)。使用因子分析、判别分析和聚类分析。通过因子分析，发现两个因子共同解释了总数据变异的69.5%。第一个因素代表乳糖与脂肪和蛋白质含量之间的反比关系，而第二个因素代表月产奶量与 SCS 和 LogTBC 之间的反比关系。判别分析发现，乳糖和蛋白质含量以及 SCS 是对聚类分析形成的组的分离影响最大的变量。各组的差异主要在于每月产奶量、成分和 SCS。最后，牛奶产量、成分和质量变量之间存在重要的多元关系。
This study was realized to evaluate the monthly production, composition and quality of milk (total and defatted dry extract, lactose, fat and protein) and their relation to somatic cell count (SCC) and total bacterial count (TBC) using multivariate statistical analyses. The data are from a dairy farm for the period of two years (from January 2015 to December 2016). The SCC and TBC variables were transformed to somatic cell score (SCS) and log10 (LogTBC). Factor analysis, discriminant analysis and cluster analysis were used. Through factor analysis, it was found two factors that together explained 69.5% of the total data variation. The first factor represented the inverse relationship between lactose versus fat and protein content, while the second factor represented the inverse relationship among monthly milk yield versus SCS and LogTBC. The discriminant analysis identified that lactose and protein contents and SCS were the variables that had the greatest participation in the separation of the groups formed by the cluster analysis. The groups differed mainly by the monthly production of milk, composition and SCS. Finally, there are important multivariate relations between the variables milk production, composition and quality.