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)光谱来量化牛奶的主要成分，如脂肪或蛋白质。这些预测被用于牛奶付款和动物表演记录。所有这些预测都基于相同的初始数据，即代表全球牛奶成分的MIR光谱。与传统的化学分析方法相比，红外光谱技术具有快速、廉价等优点。最近的研究表明，除了脂肪或蛋白质外，还有可能对其他成分进行定量，如乳糖、酪蛋白、b-羟基碳酸盐(键体)丙酮、脂肪酸、乳铁蛋白或矿物质。所有这些牛奶成分都是动物健康和繁殖状况的潜在指标，或者牛奶的营养质量，或者两者兼而有之。该项目的目的是直接研究光谱数据的表型和基因变异性，以提高奶牛的健壮性，提高供人类消费的牛奶的营养质量，并根据牛奶光谱数据建立牛奶脂肪酸的校正方程。本研究项目包括3个阶段：(1)光谱数据的降维；(2)光谱变异性与动物健康和繁殖状况变化的研究；(3)从牛奶光谱数据建立牛奶脂肪酸校正方程。

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.