ORIGINAL PAPER
Prediction of intramuscular fat in live bulls using real-time ultrasound and image analysis
 
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1
Slovak Agricultural Research Center - Research Institute for Animal Production, Hlohovská 2, 949 92 Nitra, Slovakia
2
Public University of Navarra, Agricultural Production Engineering Department, Campus de Arrosadía, 31006 Pamplona, Spain
CORRESPONDING AUTHOR
P. Polák   

Slovak Agricultural Research Center - Research Institute for Animal Production, Hlohovská 2, 949 92 Nitra, Slovakia
Publication date: 2008-01-15
 
J. Anim. Feed Sci. 2008;17(1):30–40
 
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ABSTRACT
Sonograms of Longissimus dorsi muscle (LDM) area were taken before slaughter on the last thoracic vertebra by an Aloka SSD-500. Three sonograms of each bull were taken with 60, 65 and 70% ultrasound intensity. A digital image of the LDM area was obtained on the last thoracic vertebra. Intramuscular fat content was detected by an Infratec 1265 - Meat Analyzer. The grey value of the sonogram measured by computer image analysis significantly correlated with the content of IMF, r=0.78 for Slovak Simmental, and r=0.60 for Holstein bulls. Area proportion of marbling determined on images obtained by computer image analysis (MARB) correlated significantly with IMF, r=0.65. This correlation was only significant for Slovak Simmental bulls. The obtained results suggest that prediction of intramuscular fat in meat is possible using the technique of ultrasound and computer image analyses of live animals. Further investigation is needed to improve measurement precision and to validate applicability among a wide variety of cattle breeds.
 
CITATIONS (2):
1.
Use of image analysis of cross-sectional cuts to estimate the composition of the 10th–11th–12th rib-cut of European lean beef bulls
R. Santos, F. Peña, M. Juárez, C. Avilés, A. Horcada, A. Molina
Meat Science
 
2.
Application of texture analysis of b-mode ultrasound images for the quantification and prediction of intramuscular fat in living beef cattle: A methodological study
Enrico Fiore, Giorgia Fabbri, Luigi Gallo, Massimo Morgante, Michele Muraro, Matteo Boso, Matteo Gianesella
Research in Veterinary Science
 
ISSN:1230-1388