peak identification
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2021 ◽  
Vol 64 (8) ◽  
Author(s):  
Kai Zhao ◽  
Yongfu Li ◽  
Guoxing Wang ◽  
Yu Pu ◽  
Yong Lian

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Jack H. Thiesen ◽  
Jeremy M. Hepker ◽  
Wenjin Yu ◽  
Keegan D. Pombier ◽  
Kimberlee J. Kearfott

2020 ◽  
Vol 215 ◽  
pp. 113014
Author(s):  
A. Mikhalychev ◽  
S. Vlasenko ◽  
T.R. Payne ◽  
D.A. Reinhard ◽  
A. Ulyanenkov

Author(s):  
Jia Xiang Liu ◽  
Jia Ju Liu ◽  
Zhi Zheng Zhu ◽  
Yong Hua Fang ◽  
Xiao Dong ◽  
...  

2019 ◽  
Vol 37 (17) ◽  
pp. 4210-4215 ◽  
Author(s):  
Zigeng Liu ◽  
Guigen Liu ◽  
Yupeng Zhu ◽  
Qiwen Sheng ◽  
Xin Wang ◽  
...  

2019 ◽  
Vol 97 (Supplement_2) ◽  
pp. 25-26
Author(s):  
Cliff Ocker

Abstract Fatty acid nutrition in ruminants, dairy cattle primarily, has increased as a point of emphasis with nutrition formulation in the past decade, as the diet fatty acid profile and metabolism has been found to impact milk fat concentration and animal health. Both the fatty acid supply and rumen degradation warrant further investigation in dairy diets for improved formulation strategies in the future. As supply and degradation are better understood, improved formulation approaches will be possible now that routine feed fatty acid measures have become more practical with the development of near-infrared reflectance spectroscopy (NIR) models at commercial feed analysis laboratories. NIR model development techniques vary; however, the general approach is to calibrate against a wide ranging database of feedstuff wet chemistry measures using a partial least squares approach. Models relate spectral observations (i.e. reflectance at a specific near-infrared light wavelength) to wet chemistry observations. NIR models should reflect both the mean and variation observed in the wet chemistry database. The NIR models developed by Rock River Laboratory, and resulting feed library database information presented in Table 1, were developed by calibrating against feedstuff chemistry performed at the Lock laboratory with Michigan State University. Wet chemistry fatty acid determination by analytical laboratories, using gas chromatography techniques against known fatty acid standards, deserves further discussion to agree upon peak identification schemes. Differences in peak identification from one laboratory to the another will result in different total fatty acid measures and NIR models. The fatty acid content and profile coefficients of variation, determined from the mean and standard deviations presented in Table 1, range from less than 10 to over 100% of the mean with an average CV of 27%. This suggests substantial variation is present in commercial feeds, and opportunities may exist to better account for variation and fatty acid supply in dairy diets.


2019 ◽  
Vol 47 (10) ◽  
pp. e58-e58 ◽  
Author(s):  
Naozumi Hiranuma ◽  
Scott M Lundberg ◽  
Su-In Lee

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