云南水牛乳成分分析及乳能量预测模型的建立

李清, 毛华明, 李文, 李建华, 鲁琼芬, 何鸿源, 刘琴

doi:10.3969/j.issn.1006-267x.2017.08.032

动物营养学报 >

2017 , Vol. 29 >Issue 8: 2875 - 2883

DOI: https://doi.org/10.3969/j.issn.1006-267x.2017.08.032

研究简报 Short communications

云南水牛乳成分分析及乳能量预测模型的建立

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1. 云南农业大学动物科学技术学院, 昆明 650201;
2. 云南省动物营养与饲料重点实验室, 昆明 650201;
3. 湖南工业大学生命科学与化学学院, 株洲 412007;
4. 云南省安宁市畜牧兽医站, 昆明 650300;
5. 云南省昆明市奶牛生产性能测定中心, 昆明 650041

李清(1971—),女,广东台山人,副教授,博士,主要从事动物营养研究。E-mail:814318012@qq.com

收稿日期: 2017-01-01

网络出版日期: 2017-08-02

基金资助

云南省生物重大专项——奶业专项（2014ZA002）；湖南省科技计划项目（2016NK2096）；中国博士后面上项目（2016M592456）；长沙市科技局重点项目（kh16011067）

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Establishment of Prediction Model of Milk Energy and Milk Composition Analysis of Yunnan Buffalo

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1. College of Animal Science and Technology, Yunnan Agricultural University, Kunming 650201, China;
2. Yunnan Provincial Key Laboratory of Animal Nutrition and Feed Science, Kunming 650201, China;
3. College of Life Science and Chemistry, Hunan University of Technology, Zhuzhou 412007, China;
4. Animal Husbandry and Veterinary Station of Anning, Kunming 650300, China;
5. Dairy Production Performance Measurement Center of Kunming, Kunming 650041, China

Received date: 2017-01-01

Online published: 2017-08-02

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摘要

本试验旨在分析水牛乳成分中乳脂肪、乳蛋白、乳糖和乳尿素氮含量及其与乳能量的相关性，建立产奶净能的预测模型，用于奶水牛泌乳期科学的饲养管理。试验采取云南省主要奶水牛养殖场和养殖小区的2014—2015年9—12月份送达昆明市奶牛生产性能测定中心的304个原料乳样本，水牛乳的乳成分由昆明市奶牛生产性能测定中心用MilkoScan FT+FC乳成分体细胞联用仪和MilkoScan FT 120乳成分分析仪测定，鲜乳经真空干燥箱恒温烘干后用氧弹式热量计测定乳能量。结果表明：乳脂肪、乳蛋白、乳糖、乳尿素氮、乳总固形物含量和乳能量分别为6.45%、4.55%、5.31%、13.60 mg/dL、18.77%和4.02 MJ/kg；乳能量分别与乳脂肪和乳蛋白含量呈现极显著正相关（r=0.896 0、r=0.563 0，P<0.01），乳能量受乳脂肪和乳蛋白含量的影响较大。分别以乳脂肪（F），乳脂肪和乳蛋白（F和P），乳脂肪、乳蛋白和乳糖（F、P和La），乳脂肪、乳蛋白、乳糖和乳尿素氮含量（F、P、La和MUN）为预测因子，建立的预测乳能量（E）的一元、二元、三元及四元回归方程的拟合度均在0.90以上，方程分别为：E=0.388F+1.540 （R²=0.933 6，P<0.01）；E=0.373F+0.221P+0.460 （R²=0.926 7，P<0.01）；E=0.396F+0.186P+0.105La-0.104 （R²=0.954 0，P<0.01）；E=0.397F+0.187P+0.106La+0.002MUN-0.146 （R²=0.958 0，P<0.01）。由此可见，可通过水牛乳中的乳脂肪、乳蛋白、乳糖及乳尿素氮含量预测水牛乳产奶净能。

关键词： 水牛乳; 乳成分; 乳能量; 预测模型

本文引用格式

李清, 毛华明, 李文, 李建华, 鲁琼芬, 何鸿源, 刘琴 . 云南水牛乳成分分析及乳能量预测模型的建立[J]. 动物营养学报, 2017 , 29(8) : 2875 -2883 . DOI: 10.3969/j.issn.1006-267x.2017.08.032

Abstract

This experiment was conducted to analysis the contents of milk fat, milk protein, milk lactose and milk urea nitrogen of buffalo milk and those correlation with milk energy, and to established the prediction model of milk production net energy for scientific feeding management of lactating dairy buffalo. A total of 304 milk samples were collected from the main milk buffalo farms and farming communities in Yunnan province at 2014 to 2015 years 9 to 12 months, which were sent to Kunming Milk Production Center. The milk composition of the buffalo milk was detected by the MilkoScan FT+FC and MilkoScan FT 120 analyzer in Kunming Dairy Production Center, and milk energy was determined by oxygen bomb calorimeter after constant temperature drying in vacuum drying oven. The results showed that milk fat, milk protein, milk lactose, milk urea nitrogen, milk total solids and milk energy were 6.45%, 4.55%, 5.31%, 13.60 mg/dL, 18.77% and 4.02 MJ/kg, respectively. And there was significant positive correlation between milk energy and milk fat, milk protein (r=0.896 0, r=0.563 0, P<0.01). The milk energy was greater impacted by milk fat and milk protein. Moreover, the fitting degrees of the one, two, three and four regression equations were above 0.90 to predict the net energy (E) of buffalo milk by using milk fat (F), milk fat and milk protein (F and P), milk fat, milk protein and milk lactose (F, P and La), milk fat, milk protein, milk lactose and milk urea nitrogen contents (F, P, La and MUN) as predictors, and regression equations were as followed: E=0.388F+1.540 (R²=0.933 6, P<0.01); E=0.373F+0.221P+0.460 (R²=0.926 7, P<0.01); E=0.396F+0.186P+0.105La-0.104 (R²=0.954 0, P<0.01); E=0.397F+0.187P+0.106La+0.002MUN-0.146 (R²=0.958 0, P<0.01). In conclusion, the milk production net energy can be predicted by the contents of milk fat, milk protein, milk lactose and milk urea nitrogen of buffalo milk.

Key words： buffalo milk; milk composition; milk energy; prediction model

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