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个体模型奶酪制造过程中不同奶酪产量测量指标及牛奶营养成分回收率的遗传参数

Genetic parameters of different measures of cheese yield and milk nutrient recovery from an individual model cheese-manufacturing process.

作者信息

Bittante G, Cipolat-Gotet C, Cecchinato A

机构信息

Department of Agronomy, Food, Natural Resources, Animals and Environment (DAFNAE), University of Padova, Viale dell'Università 16, 35020 Legnaro (PD), Italy.

出版信息

J Dairy Sci. 2013;96(12):7966-79. doi: 10.3168/jds.2012-6517. Epub 2013 Oct 4.

Abstract

Cheese yield (CY) is an important technological trait in the dairy industry, and the objective of this study was to estimate the genetic parameters of cheese yield in a dairy cattle population using an individual model-cheese production procedure. A total of 1,167 Brown Swiss cows belonging to 85 herds were sampled once (a maximum of 15 cows were sampled per herd on a single test day, 1 or 2 herds per week). From each cow, 1,500 mL of milk was processed according to the following steps: milk sampling and heating, culture addition, rennet addition, gelation-time recording, curd cutting, whey draining and sampling, wheel formation, pressing, salting in brine, weighing, and cheese sampling. The compositions of individual milk, whey, and curd samples were determined. Three measures of percentage cheese yield (%CY) were calculated: %CY(CURD), %CY(SOLIDS), and %CY(WATER), which represented the ratios between the weight of fresh curd, the total solids of the curd, and the water content of the curd, respectively, and the weight of the milk processed. In addition, 3 measures of daily cheese yield (dCY, kg/d) were defined, considering the daily milk yield. Three measures of nutrient recovery (REC) were computed: REC(FAT), REC(PROTEIN), and REC(SOLIDS), which represented the ratio between the weights of the fat, protein, and total solids in the curd, respectively, and the corresponding nutrient in the milk. Energy recovery, REC(ENERGY), represented the energy content of the cheese versus that in the milk. For statistical analysis, a Bayesian animal model was implemented via Gibbs sampling. The effects of parity (1 to ≥4), days in milk (6 classes), and laboratory vat (15 vats) were assigned flat priors; those of herd-test-date, animal, and residual were given Gaussian prior distributions. Intra-herd heritability estimates of %CY(CURD), %CY(SOLIDS), and %CY(WATER) ranged from 0.224 to 0.267; these were larger than the estimates obtained for milk yield (0.182) and milk fat content (0.122), and similar to that for protein content (0.275). Daily cheese yields showed heritability estimates similar to those of daily milk yield. The trait %CY(WATER) showed a highly positive genetic correlation with %CY(SOLIDS) (0.87), whereas their phenotypic correlation was moderate (0.37), and the fat and protein contents of milk showed high genetic correlations with %CY traits. The heritability estimates of REC(PROTEIN) and REC(FAT) were larger (0.490 and 0.208, respectively) than those obtained for the protein and fat contents of milk, and the genetic relationships between REC(PROTEIN) and REC(FAT) with milk protein and fat content were low or moderate; REC(PROTEIN) and REC(FAT) were moderately correlated with the %CY traits and highly correlated with REC(SOLIDS) and REC(ENERGY). Both REC(SOLIDS) and REC(ENERGY) were heritable (0.274 and 0.232), and showed high correlations with each other (0.96) and with the %CY traits (0.83 to 0.97). Together, these findings demonstrate the existence of economically important, genetically determined variability in cheese yield that does not depend solely upon the fat and protein contents of milk, but also relies on the ability of the coagulum to retain the highest possible proportions of the available protein, fat, and water. Exploitation of this interesting genetic variation does not seem to be feasible through direct measurement of the phenotype in cows at the population level. Instead, further research is warranted to examine possible means for indirect prediction, such as through assessing the mid-infrared spectra of milk samples.

摘要

奶酪产量(CY)是乳制品行业一项重要的技术特性,本研究的目的是使用个体模型 - 奶酪生产程序估计奶牛群体中奶酪产量的遗传参数。总共对来自85个牛群的1167头瑞士褐牛进行了一次采样(在单个测试日每个牛群最多采样15头牛,每周1或2个牛群)。从每头牛采集1500毫升牛奶,并按以下步骤进行加工:牛奶采样与加热、添加培养物、添加凝乳酶、记录凝胶化时间、切割凝乳、沥干乳清并采样、成型奶酪轮、压制、在盐水中腌制、称重以及奶酪采样。测定了个体牛奶、乳清和凝乳样品的成分。计算了三种奶酪产量百分比(%CY)的度量:%CY(凝乳)、%CY(固体)和%CY(水),它们分别代表新鲜凝乳重量、凝乳总固体以及凝乳含水量与加工牛奶重量之间的比率。此外,考虑到每日产奶量,定义了三种每日奶酪产量(dCY,千克/天)的度量。计算了三种营养回收率(REC):REC(脂肪)、REC(蛋白质)和REC(固体),它们分别代表凝乳中脂肪、蛋白质和总固体重量与牛奶中相应营养素重量之间的比率。能量回收率REC(能量)代表奶酪与牛奶中的能量含量之比。为了进行统计分析,通过吉布斯采样实施了贝叶斯动物模型。胎次(1至≥4)、产奶天数(6个类别)和实验室发酵罐(15个发酵罐)的影响被赋予平坦先验;牛群 - 测试 - 日期、动物和残差的影响被赋予高斯先验分布。%CY(凝乳)、%CY(固体)和%CY(水)的群体内遗传力估计值范围为0.224至0.267;这些值大于产奶量(0.182)和乳脂含量(0.122)的估计值,与蛋白质含量(0.275)的估计值相似。每日奶酪产量的遗传力估计值与每日产奶量的相似。%CY(水)性状与%CY(固体)性状表现出高度正遗传相关性(0.87),而它们的表型相关性为中等(0.37),并且牛奶的脂肪和蛋白质含量与%CY性状表现出高遗传相关性。REC(蛋白质)和REC(脂肪)的遗传力估计值大于牛奶蛋白质和脂肪含量的估计值(分别为0.490和0.208),并且REC(蛋白质)和REC(脂肪)与牛奶蛋白质和脂肪含量之间的遗传关系较低或中等;REC(蛋白质)和REC(脂肪)与%CY性状中等相关,与REC(固体)和REC(能量)高度相关。REC(固体)和REC(能量)都是可遗传的(0.274和0.232),并且它们彼此之间以及与%CY性状之间都表现出高相关性(0.83至0.97)。总之,这些发现表明,在奶酪产量方面存在经济上重要的、由基因决定的变异性,这种变异性不仅取决于牛奶的脂肪和蛋白质含量,还依赖于凝乳保留尽可能高比例的可用蛋白质、脂肪和水的能力。通过在群体水平直接测量奶牛的表型来利用这种有趣的遗传变异似乎不可行。相反,有必要进行进一步研究以检查间接预测的可能方法,例如通过评估牛奶样品的中红外光谱。

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