Genetic analysis of milk spectral data to develop management and selection tools to improve the nutraceutical properties of milk

牛奶实验室使用中红外（MIR）光谱法定量牛奶的主要成分，如脂肪或蛋白质。这些预测用于支付牛奶以及记录动物表现。所有这些预测都是基于相同的初始数据，即MIR谱，它代表了全球牛奶的成分。与传统的化学分析方法相比，MIR光谱法具有快速、廉价的优点。最近有研究表明，除了脂肪或蛋白质以外，还可以量化其他成分，如乳糖、酪蛋白、b-羟基burate（一种键体）丙酮、脂肪酸、乳铁蛋白或矿物质。所有这些奶成分都是动物健康和繁殖状况、奶的营养质量或两者兼而有之的潜在指标。该项目的目的是直接研究光谱数据的表型和基因型变异性，以提高奶牛的健壮性和供人类食用的牛奶的营养质量，并根据牛奶光谱数据建立牛奶脂肪酸的校准方程。本研究项目分为三个阶段：(1)光谱数据降维；(2)研究与动物健康和繁殖状况变化相关的光谱变异性；(3)根据牛奶光谱数据建立牛奶脂肪酸的校准方程。

Milk laboratories quantify major milk components such as fat or protein using mid-infrared (MIR) spectrometry. These predictions are used for milk payments as well as for animal performances recording. All these predictions are based on the same initial data, the MIR spectrum, which represents the global milk composition. The advantaes of MIR spectrometry compared to traditional chemichal analysis are its rapidity and cheap cost. Recently it has been shown that it is possible to quantify other components than fat or protein such as lactose, casein, b-hydroxyburate ( a keytone body) acetone, fatty acids, lactoferrin, or minerals. All of these milk components are potential indicators of animal health and reproduction status, nutritional quality of the milk, or both. The objective of this project is to study directly the phenotypic and genotypic variability of the spectral data in order to improve cow robustness, nutrional quality of milk for human consumption and develop calibration equations for milk fatty acids from milk spectral data. This research project involves 3 stages: (1) reduction of the dimentionality of spectral data, (2) study of the spectral variability in relation with changes of animal health and reproduction status, (3) development of calibration equations for milk fatty acids from milk spectral data.