Differentiation of perirenal and omental fat quality of suckling lambs according to the rearing system from Fourier transforms mid-infrared spectra using partial least squares and artificial neural networks analysis-Reference-Cited by-同舟云学术

Differentiation of perirenal and omental fat quality of suckling lambs according to the rearing system from Fourier transforms mid-infrared spectra using partial least squares and artificial neural networks analysis

Published:2009-09 Issue:1 Volume:83 Page:140-147
ISSN:0309-1740
Container-title:Meat Science
language:en
Short-container-title:Meat Science

Author:

Osorio M.T.,Zumalacárregui J.M.,Alaiz-Rodríguez R.,Guzman-Martínez R.,Engelsen S.B.,Mateo J.

Publisher

Elsevier BV

Subject

Food Science

Reference26 articles.

1. Effect of nutritional factors on fatty acid composition of lamb fat deposits;Bas;Livestock Production Science,2000

2. Effect of rearing system on growth, body composition and development of digestive system in young lambs;De la Fuente;Journal of Animal Physiology and Animal Nutrition,1998

3. Neural network toolbox user’s guide for use with MATLAB;Demuth,2002

4. Classification of edible fats and oils by principal component analysis of Fourier transform infrared spectra;Dupuy;Food Chemistry,1995

5. Linearization and scatter correction for near-infrared reflectance spectra of meat;Geladi;Applied Spectroscopy,1985

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