Hostname: page-component-cd9895bd7-hc48f Total loading time: 0 Render date: 2024-12-22T17:06:03.442Z Has data issue: false hasContentIssue false
  • English
  • Français

Predicting beef carcass composition using tissue weights of a primal cut assessed by computed tomography

Published online by Cambridge University Press:  07 June 2010

E. A. Navajas ,
R. I. Richardson ,
A. V. Fisher ,
J. J. Hyslop ,
D. W. Ross ,
N. Prieto ,
G. Simm  and
R. Roehe
E. A. Navajas*
Affiliation:
Sustainable Livestock Systems Group, Scottish Agricultural College, King’s Buildings, Edinburgh, EH9 3JG, UK
R. I. Richardson
Affiliation:
Division of Farm Animal Science, University of Bristol, Langford, Bristol, BS40 5DU, UK
A. V. Fisher
Affiliation:
Division of Farm Animal Science, University of Bristol, Langford, Bristol, BS40 5DU, UK
J. J. Hyslop
Affiliation:
Beef and Sheep Select, Scottish Agricultural College, King’s Buildings, Edinburgh, EH9 3JG, UK
D. W. Ross
Affiliation:
Sustainable Livestock Systems Group, Scottish Agricultural College, King’s Buildings, Edinburgh, EH9 3JG, UK
N. Prieto
Affiliation:
Sustainable Livestock Systems Group, Scottish Agricultural College, King’s Buildings, Edinburgh, EH9 3JG, UK
G. Simm
Affiliation:
Sustainable Livestock Systems Group, Scottish Agricultural College, King’s Buildings, Edinburgh, EH9 3JG, UK
R. Roehe
Affiliation:
Sustainable Livestock Systems Group, Scottish Agricultural College, King’s Buildings, Edinburgh, EH9 3JG, UK
*
†E-mail: [email protected]
Get access

Abstract

The potential of the composition of the forerib measured by X-ray computed tomography (CT) as a predictor of carcass composition was evaluated using data recorded on 30 Aberdeen Angus and 43 Limousin crossbred heifers and steers. The left sides of the carcasses were split into 20 cuts, which were CT scanned and fully dissected into fat, muscle and bone. Carcass and forerib tissue weights were assessed by dissection and CT. Carcass composition was assessed very accurately by CT scanning of the primal cuts (adj-R2 = 0.97 for the three tissues). CT scanning predicted weights of fat, muscle and bone of the forerib with adj-R2 of 0.95, 0.91 and 0.75, respectively. Single regression models with the weights of fat, muscle or bone in the forerib measured by CT as the only predictors to estimate fat, muscle or bone of the left carcass obtained by CT showed adjusted coefficients of determination (adj-R2) of 0.79, 0.60 and 0.52, respectively. By additionally fitting breed and sex, accuracy increased to 0.85, 0.73 and 0.67. Using carcass and forerib weights in addition to the previous predictors improved significantly the prediction accuracy of carcass fat and muscle weights to adj-R2 values of 0.92 and 0.96, respectively, while the highest value for carcass bone weight was 0.77. In general, equations derived using CT data had lower adj-R2 values for bone, but better accuracies for fat and muscle compared to those obtained using dissection. CT scanning could be considered as an alternative very accurate and fast method to assess beef carcass composition that could be very useful for breeding programmes and research studies involving a large number of animals, including the calibration of other indirect methods (e.g. in vivo and carcass video image analysis).

Keywords

carcass compositionprimal compositioncomputed tomographydissectionbeef
Type
Full Paper
Information
animal , Volume 4 , Issue 11 , November 2010 , pp. 1810 - 1817
Copyright
Copyright © The Animal Consortium 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Arguello, A, Capote, J, Gines, R, Lopez, JL 2001. Prediction of kid carcass composition by use of joint dissection. Livestock Production Science 67, 293–295.CrossRefGoogle Scholar
Bergen, R, Miller, SP, Wilton, JW, Crews, DH, Mandell, IB 2006. Genetic correlations between live yearling bull and steer carcass traits adjusted to different slaughter end points. 1. Carcass lean percentage. Journal of Animal Science 84, 546–557.CrossRefGoogle ScholarPubMed
Cook, GL, Jones, DW, Kempster, AJ 1983. A note on a simple criterion for choosing among sample joints for use in double sampling. Animal Production 36, 493–495.Google Scholar
Crouse, JD, Dikeman, ME 1974. Methods of estimating beef carcass chemical composition. Journal of Animal Science 38, 1190–1196.CrossRefGoogle Scholar
Fisher, AV 1990. New approaches to measuring fat in the carcasses of meat animals. In Reducing fat in meat animals (ed. JD Wood and AV Fisher), pp. 155–343. Elsevier Applied Science, London.Google Scholar
Fullerton, GD 1980. Tissue imaging and characterisation. In Medical physics of CT and ultrasound Medical Physics Monograph, (ed. GD Fullerton and JA Zagzebski), pp. 125–162. American Institute of Physics, New York.Google Scholar
Glasbey, CA, Horgan, GW 1995. Image analysis for the biological sciences. Wiley, Chichester.Google Scholar
Gregory, KE, Cundiff, LV, Koch, RM, Dikeman, ME, Koohmaraie, M 1994. Breed effects and retained heterosis for growth, carcass, and meat traits in advanced generations of composite populations of beef cattle. Journal of Animal Science 72, 833–850.CrossRefGoogle ScholarPubMed
Hankins, OG, Howe, PE 1946. Estimation of the composition of beef carcasses and cuts. Technical Bulletin 926. USDA, Washington, DC.Google Scholar
Hedrick, HB 1983. Methods of estimating live animal and carcass composition. Journal of Animal Science 57, 1316–1327.CrossRefGoogle Scholar
Heyer, A, Lebret, B 2007. Compensatory growth response in pigs: effects on growth performance, composition of weight gain at carcass and muscle levels, and meat quality. Journal of Animal Science 85, 769–778.CrossRefGoogle ScholarPubMed
Hollo, G, Szues, E, Tozser, J, Hollo, I, Repa, I 2007. Application of X-ray computer tomography (CT) in cattle production. Asian-Australasian Journal of Animal Science 12, 1901–1908.CrossRefGoogle Scholar
Johnson, ER, Charles, DD 1981. The use of carcass cuts to predict beef carcass composition: a research technique. Australian Journal of Agricultural Research 32, 987–997.CrossRefGoogle Scholar
Jones, HE, Lewis, RM, Young, MJ, Wolf, BT 2002. The use of X-ray computer tomography for measuring the muscularity of live sheep. Animal Science 75, 387–399.CrossRefGoogle Scholar
Jopson, NB 1993. Physiological adaptations in two seasonal Cervids. PhD Thesis. University of New England, New South Wales, Australia.Google Scholar
Jopson, NB, Kolstad, K, Sehested, E, Vangen, O 1995. Computed tomography as an accurate and cost effective alternative to carcass dissection. Proceeding of the Australian Association of Animal Breeding and Genetics 11, 635–638.Google Scholar
Jorge, AM, Fontes, CAA, Paulino, MF, Gomes Júnior, P 2000. Use of indirect method to predict chemical body composition in Zebu cattle. Revista Brasileira de Zootecnia 29, 1862–1867.CrossRefGoogle Scholar
Kempster, AJ 1986. Correlations between indirect and direct measurements of body composition. Proceedings of the Nutrition Society 45, 55–62.CrossRefGoogle ScholarPubMed
Kempster, AJ, Jones, DW 1977. Relationships between the lean content of joints and overall lean content in steer carcasses of different breeds and crosses. Journal of Agricultural Science, Cambridge 88, 193–201.CrossRefGoogle Scholar
Kempster, AJ, Cuthbertson, A, Harrington, G 1982. Carcase evaluation in livestock breeding, production and marketing. Granada, London.Google Scholar
Kongsro, J, Røe, M, Aastveit, AH, Kvaal, K, Egelandsdal, B 2008. Virtual dissection of lamb carcasses using computer tomography (CT) and its correlation to manual dissection. Journal of Food Engineering 88, 86–93.CrossRefGoogle Scholar
Kvame, T, McEwan, JC, Amer, PR, Jopson, NB 2004. Economic benefits in selection for weight and composition of lambs cuts predicted by computer tomography. Livestock Production Science 90, 123–133.CrossRefGoogle Scholar
Macfarlane, JM, Emmans, GE, Lewis, RM, Young, MJ, Simm, G 2006. Predicting carcass composition of terminal sire sheep using X-ray computed tomography. Animal Science 82, 289–300.CrossRefGoogle Scholar
Mann, AD, Glasbey, CA, Navajas, EA, McLean, KA, Bünger, L 2008. STAR: sheep tomogram analysis routines (Version 4.9). BioSS software documentation.Google Scholar
Navajas, EA, Lambe, NR, McLean, KA, Glasbey, CA, Fisher, AV, Charteris, AJL, Bünger, L, Simm, G 2007. Accuracy of in vivo muscularity indices measured by computed tomography and their association with carcass quality in lambs. Meat Science 75, 533–542.CrossRefGoogle ScholarPubMed
Navajas, EA, Glasbey, CA, Fisher, AV, Ross, DW, Hyslop, JJ, Richardson, RI, Simm, G, Roehe, R 2009. Assessing beef carcass tissue weights using computed tomography spirals of primal cuts. Meat Science 84, 30–38.CrossRefGoogle ScholarPubMed
Neill, S, Unruh, JA, Marston, TT, Jaeger, JR, Hunt, MC, Higgins, JJ 2009. Effects of implanting and feeding zilpaterol hydrochloride on performance, carcass characteristics, and subprimal beef yields of fed cows. Journal of Animal Science 87, 704–710.CrossRefGoogle ScholarPubMed
Payne, RW, Murray, DA, Harding, SA, Baird, DB, Soutar, DM 2008. GenStat for windows (11th edition) introduction. VSN International, Hemel Hempstead.Google Scholar
Renand, G, Fisher, AV 1997. Comparison of methods for estimating carcass fat content of young Charolais bulls in performance testing stations. Livestock Production Science 51, 205–213.CrossRefGoogle Scholar
van Heelsum, AM, Lewis, RM, Davies, MH, Haresign, W 2003. Growth and carcass characteristics in wether lambs of a crossbred dam line. Animal Science 76, 45–53.CrossRefGoogle Scholar
Young, MJ, Simm, G, Glasbey, CA 2001. Computerised tomography for carcass analysis. Proceedings of the British Society of Animal Science 2001, 250–254.CrossRefGoogle Scholar