Application of Mid-Infrared Spectrometry in Milk Nutrient Components Prediction and Related Traits Analysis of Dairy Cows

DONG Lifeng; YAN Tianhai; TU Yan; DIAO Qiyu

doi:10.3969/j.issn.1006-267x.2016.02.004

2016 , Vol. 28 >Issue 2: 326 - 334

DOI: https://doi.org/10.3969/j.issn.1006-267x.2016.02.004

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Application of Mid-Infrared Spectrometry in Milk Nutrient Components Prediction and Related Traits Analysis of Dairy Cows

DONG Lifeng ,
YAN Tianhai ,
TU Yan ,
DIAO Qiyu

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1. Key Laboratory of Feed Biotechnology of the Ministry of Agriculture, Feed Research Institute, Chinese Academy of Agricultural Sciences, Beijing 100081, China;
2. Agri-Food and Biosciences Institute, Hillsborough BT26 6DR, UK

Received date: 2015-08-24

Online published: 2016-02-19

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Abstract

In recent years, the mid-infrared spectrometry (MIRS) analysis of milk has been used as a potential tool for the rapid and accurate quantification of milk nutrient components, such as fatty acids, proteins and minerals, and potential harmful materials, such as adulteration and antibiotics. Furthermore, the MIRS prediction equations derived from the milk analysis can provide simultaneous detection of cows' nutrition (feed conversion rate, energy utilization efficiency and methane emission), health (mammitis and metabolic diseases), reproduction physiology and fertility conditions. These researches may provide technical insurance for optimization of production mode, development of the innovative circular economy mode and realization of low carbon, healthy and sustainable development in animal husbandry industry. This paper mainly reviewed the use of MIRS to predict milk nutrient components and related traits analysis of dairy cows worldwide in recent years.

Key words： MIRS; prediction model; milk components; dairy cows

Cite this article

DONG Lifeng , YAN Tianhai , TU Yan , DIAO Qiyu . Application of Mid-Infrared Spectrometry in Milk Nutrient Components Prediction and Related Traits Analysis of Dairy Cows[J]. Chinese Journal of Animal Nutrition, 2016 , 28(2) : 326 -334 . DOI: 10.3969/j.issn.1006-267x.2016.02.004

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