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Dual energy X-ray absorptiometry precisely and accurately predicts lamb carcass composition at abattoir chain speed across a range of phenotypic and genotypic variables

Published online by Cambridge University Press:  13 May 2020

S. L. Connaughton Open the ORCID record for S. L. Connaughton [Opens in a new window] ,
A. Williams ,
F. Anderson ,
K. R. Kelman  and
G. E. Gardner
S. L. Connaughton*
Affiliation:
School of Veterinary and Life Sciences, Murdoch University, South Street, Murdoch6150, Western Australia, Australia
A. Williams
Affiliation:
School of Veterinary and Life Sciences, Murdoch University, South Street, Murdoch6150, Western Australia, Australia
F. Anderson
Affiliation:
School of Veterinary and Life Sciences, Murdoch University, South Street, Murdoch6150, Western Australia, Australia
K. R. Kelman
Affiliation:
School of Veterinary and Life Sciences, Murdoch University, South Street, Murdoch6150, Western Australia, Australia
G. E. Gardner
Affiliation:
School of Veterinary and Life Sciences, Murdoch University, South Street, Murdoch6150, Western Australia, Australia
*
†E-mail: s.connaughton@murdoch.edu.au
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Abstract

Dual energy X-ray absorptiometry (DEXA) is an imaging modality that has been used to predict the computed tomography (CT)-determined carcass composition of multiple species, including sheep and pigs, with minimal inaccuracies, using medical grade DEXA scanners. An online DEXA scanner in an Australian abattoir has shown that a high level of precision can be achieved when predicting lamb carcass composition in real time. This study investigated the accuracy of that same online DEXA when predicting fat and lean percentages as determined by CT over a wide range of phenotypic and genotypic variables across 454 lambs over 6 kill groups and contrasted these results against the current Australian industry standard of grade-rule (GR) measurements to grade carcasses. Lamb carcasses were DEXA scanned and then CT scanned to determine CT Fat % and CT Lean %. All phenotypic traits and genotypic information, including Australian Sheep Breeding Values, were recorded for each carcass. Residuals of the DEXA predicted CT Fat % and Lean %, and the actual CT Fat % and Lean % were calculated and tested against all phenotypic and genotypic variables. Excellent overall precision was recorded when predicting CT Fat % (R2 = 0.91, RMSE = 1.19%). Small biases present for sire breed, sire type, dam breed, hot carcass weight and c-site eye muscle area could be explained by a regression paradox; however, biases among kill group (−0.73% to 1.01% for CT Fat %, −1.48% to 0.76% for CT Lean %) and the Merino sire type (0.36% for CT Fat %, −0.73% for CT Lean %) could not be explained by this effect. Over the large range of phenotypic and genotypic variation, there was excellent precision when predicting CT Fat % and CT Lean % by an online DEXA, with only minor biases, showing superiority to the existing Australian standard of GR measurements.

Keywords

sheepobjective measurementbiaslean meat yieldcomputed tomography
Type
Research Article
Information
animal , Volume 14 , Issue 10 , October 2020 , pp. 2194 - 2202
Copyright
© The Author(s), 2020. Published by Cambridge University Press on behalf of The Animal Consortium

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References

Anderson, F, Williams, A, Pannier, L, Pethick, D and Gardner, G 2015. Sire carcass breeding values affect body composition in lambs–1. Effects on lean weight and its distribution within the carcass as measured by computed tomography. Meat Science 108, 145–154.CrossRefGoogle ScholarPubMed
Anderson, F, Williams, A, Pannier, L, Pethick, D and Gardner, G 2016. Sire carcass breeding values affect body composition in lambs–2. Effects on fat and bone weight and their distribution within the carcass as measured by computed tomography. Meat Science 116, 243–252.CrossRefGoogle ScholarPubMed
Bredella, MA, Ghomi, RH, Thomas, BJ, Torriani, M, Brick, DJ, Gerweck, AV, Misra, M, Klibanski, A and Miller, KK 2010. Comparison of DXA and CT in the assessment of body composition in premenopausal women with obesity and anorexia nervosa. Obesity 18, 2227–2233.CrossRefGoogle ScholarPubMed
Brown, D, Huisman, A, Swan, A, Graser, H, Woolaston, R, Ball, A, Atkins, K, Banks, R 2007. Genetic evaluation for the Australian sheep industry. In Proceedings of the 17th Biennial Conference of the Association for the Advancement of Animal Breeding and Genetics, 23–26 September 2007, Armidale, Australia, pp. 326–329.Google Scholar
Camacho, A, Torres, A, Capote, J, Mata, J, Viera, J, Bermejo, LA and Argüello, A 2017. Meat quality of lambs (hair and wool) slaughtered at different live weights. Journal of Applied Animal Research 45, 400–408.CrossRefGoogle Scholar
Chen, A, Bengtsson, T and Ho, TK 2009. A regression paradox for linear models: sufficient conditions and relation to Simpson’s paradox. The American Statistician 63, 218–225.CrossRefGoogle Scholar
Fogarty, N, Banks, R, Van Der Werf, J, Ball, A and Gibson, J 2007. The information nucleus–a new concept to enhance sheep industry genetic improvement. In Proceedings of the 17th Biennial Conference of the Association for the Advancement of Animal Breeding and Genetics, 23–26 September 2007, Armidale, Australia, pp. 29–32.Google Scholar
Gardner, G, Glendenning, R, Brumby, O, Starling, S and Williams, A 2015. The development and calibration of a dual X-ray absorptiometer for estimating carcass composition at abattoir chain-speed. In Proceedings of the Fourth Annual Conference on Body and Carcass Evaluation, Meat Quality, Software and Traceability, 22–23 September 2015, Edinburgh, UK, pp. 22–25.Google Scholar
Gardner, G, Starling, S, Charnley, J, Hocking-Edwards, J, Peterse, J and Williams, A 2018. Calibration of an on-line dual energy X-ray absorptiometer for estimating carcase composition in lamb at abattoir chain-speed. Meat Science 144, 91–99.CrossRefGoogle ScholarPubMed
Gundersen, H and Jensen, E 1987. The efficiency of systematic sampling in stereology and its prediction. Journal of Microscopy 147, 229–263.CrossRefGoogle ScholarPubMed
Hopkins, D, Safari, E, Thompson, J and Smith, C 2004. Video image analysis in the Australian meat industry–precision and accuracy of predicting lean meat yield in lamb carcasses. Meat Science 67, 269–274.CrossRefGoogle ScholarPubMed
Hopkins, D, Stanley, D, Martin, L, Ponnampalam, E and van de Ven, R 2007. Sire and growth path effects on sheep meat production 1. Growth and carcass characteristics. Australian Journal of Experimental Agriculture 47, 1208–1218.CrossRefGoogle Scholar
Hopkins, DL, Anderson, MA, Morgan, JE and Hall, DG 1995. A probe to measure GR in lamb carcasses at chain speed. Meat Science 39, 159–165.CrossRefGoogle ScholarPubMed
Horber, F, Thomi, F, Casez, J, Fonteille, J and Jaeger, P 1992. Impact of hydration status on body composition as measured by dual energy X-ray absorptiometry in normal volunteers and patients on haemodialysis. The British Journal of Radiology 65, 895–900.CrossRefGoogle ScholarPubMed
Hunter, TE, Suster, D, Dunshea, FR, Cummins, LJ, Egan, AR and Leury, BJ 2011. Dual energy X-ray absorptiometry (DXA) can be used to predict live animal and whole carcass composition of sheep. Small Ruminant Research 100, 143–152.CrossRefGoogle Scholar
Jandasek, J, Milerski, M and Lichovnikova, M 2014. Effect of sire breed on physico-chemical and sensory characteristics of lamb meat. Meat Science 96, 88–93.CrossRefGoogle ScholarPubMed
Juárez, M, Horcada, A, Alcalde, MJ, Valera, M, Polvillo, O and Molina, A 2009. Meat and fat quality of unweaned lambs as affected by slaughter weight and breed. Meat Science 83, 308–313.CrossRefGoogle ScholarPubMed
Kipper, M, Marcoux, M, Andretta, I and Pomar, C 2018. Repeatability and reproducibility of measurements obtained by dual-energy X-ray absorptiometry on pig carcasses1. Journal of Animal Science 96, 2027–2037.CrossRefGoogle Scholar
Kirton, A, Duganzich, D, Feist, C, Bennett, G and Woods, E 1985. Prediction of lamb carcass composition from GR and carcass weight. In Proceedings of the New Zealand Society of Animal Production, 1985, New Zealand, pp. 63–66.Google Scholar
Koo, WWK, Hammami, M and Hockman, EM 2002. Use of fan beam dual energy X-ray absorptiometry to measure body composition of piglets. The Journal of Nutrition 132, 1380–1383.CrossRefGoogle ScholarPubMed
Kremer, R, Barbato, G, Castro, L, Rista, L, Rosés, L, Herrera, V and Neirotti, V 2004. Effect of sire breed, year, sex and weight on carcass characteristics of lambs. Small Ruminant Research 53, 117–124.CrossRefGoogle Scholar
Lukaski, HC, Marchello, MJ, Hall, CB, Schafer, DM and Siders, WA 1999. Soft tissue composition of pigs measured with dual X-ray absorptiometry: comparison with chemical analyses and effects of carcass thicknesses. Nutrition 15, 697–703.CrossRefGoogle ScholarPubMed
Macfarlane, J, Lewis, R, Emmans, G, Young, M and Simm, G 2006. Predicting carcass composition of terminal sire sheep using X-ray computed tomography. Animal Science 82, 289–300.CrossRefGoogle Scholar
Marcoux, M, Bernier, J and Pomar, C 2003. Estimation of Canadian and European lean yields and composition of pig carcasses by dual-energy X-ray absorptiometry. Meat Science 63, 359–365.CrossRefGoogle ScholarPubMed
Martínez-Cerezo, S, Sañudo, C, Panea, B, Medel, I, Delfa, R, Sierra, I, Beltrán, JA, Cepero, R and Olleta, JL 2005. Breed, slaughter weight and ageing time effects on physico-chemical characteristics of lamb meat. Meat Science 69, 325–333.CrossRefGoogle ScholarPubMed
Mercier, J, Pomar, C, Marcoux, M, Goulet, F, Thériault, M and Castonguay, F 2006. The use of dual-energy X-ray absorptiometry to estimate the dissected composition of lamb carcasses. Meat Science 73, 249–257.CrossRefGoogle ScholarPubMed
Moore, K, McLean, K and Bunger, L 2011. The benefits of Computed Tomography (CT) scanning in UK sheep flocks for improving carcase composition. In Proceedings of the Annual BSAS Meeting, 4–6 April 2011, Nottingham, UK.Google Scholar
Mull, RT 1984. Mass estimates by computed tomography: physical density from CT numbers. American Journal of Roentgenology 143, 1101–1104.CrossRefGoogle ScholarPubMed
Pannier, L, Pethick, D, Geesink, G, Ball, A, Jacob, R and Gardner, G 2014a. Intramuscular fat in the longissimus muscle is reduced in lambs from sires selected for leanness. Meat Science 96, 1068–1075.CrossRefGoogle ScholarPubMed
Pannier, L, Pethick, DW, Boyce, MD, Ball, AJ, Jacob, RH and Gardner, GE 2014b. Associations of genetic and non-genetic factors with concentrations of iron and zinc in the longissimus muscle of lamb. Meat Science 96, 1111–1119.CrossRefGoogle ScholarPubMed
Pearce, K, Ferguson, M, Gardner, G, Smith, N, Greef, J and Pethick, D 2009. Dual X-ray absorptiometry accurately predicts carcass composition from live sheep and chemical composition of live and dead sheep. Meat Science 81, 285–293.CrossRefGoogle ScholarPubMed
Pietrobelli, A, Formica, C, Wang, Z and Heymsfield, SB 1996. Dual-energy X-ray absorptiometry body composition model: review of physical concepts. American Journal of Physiology-Endocrinology and Metabolism 271, E941–E951.CrossRefGoogle ScholarPubMed
Pietrobelli, A, Wang, Z, Formica, C and Heymsfield, SB 1998. Dual-energy X-ray absorptiometry: fat estimation errors due to variation in soft tissue hydration. American Journal of Physiology-Endocrinology and Metabolism 274, E808–E816.CrossRefGoogle ScholarPubMed
Ponnampalam, E, Hopkins, D, Dunshea, F, Pethick, D, Butler, K and Warner, R 2007. Genotype and age effects on sheep meat production 4. Carcass composition predicted by dual energy X-ray absorptiometry. Australian Journal of Experimental Agriculture 47, 1172–1179.CrossRefGoogle Scholar
Soladoye, O, Campos, ÓL, Aalhus, J, Gariépy, C, Shand, P and Juárez, M 2016. Accuracy of dual energy X-ray absorptiometry (DXA) in assessing carcass composition from different pig populations. Meat Science 121, 310–316.CrossRefGoogle ScholarPubMed
St-Onge, M-P, Wang, Z, Horlick, M, Wang, J and Heymsfield, SB 2004. Dual-energy X-ray absorptiometry lean soft tissue hydration: independent contributions of intra- and extracellular water. American Journal of Physiology-Endocrinology and Metabolism 287, E842–E847.CrossRefGoogle ScholarPubMed
Suster, D, Leury, B, Ostrowska, E, Butler, K, Kerton, D, Wark, J and Dunshea, F 2003. Accuracy of dual energy X-ray absorptiometry (DXA), weight and P2 back fat to predict whole body and carcass composition in pigs within and across experiments. Livestock Production Science 84, 231–242.CrossRefGoogle Scholar
Suster, D, Leury, BJ, Kerton, DJ and Dunshea, FR 2006. Repeatability of pig body composition measurements using dual energy X-ray absorptiometry and influence of animal size and subregional analyses. Australian Journal of Experimental Agriculture 46, 1447–1454.CrossRefGoogle Scholar
Toomey, CM, McCormack, WG and Jakeman, P 2017. The effect of hydration status on the measurement of lean tissue mass by dual-energy X-ray absorptiometry. European Journal of Applied Physiology 117, 567–574.CrossRefGoogle ScholarPubMed
Van der Werf, J, Kinghorn, B and Banks, R 2010. Design and role of an information nucleus in sheep breeding programs. Animal Production Science 50, 998–1003.CrossRefGoogle Scholar
Williams, A, Anderson, F, Siddell, J, Pethick, D, Hocking Edwards, J and Gardner, G 2017. Predicting lamb carcase composition from carcase weight and GR tissue depth. In 63rd International Congress of Meat Science and Technology, 13–18 August 2017, Cork, Ireland, pp. 729–732.Google Scholar
Wiseman, J 2013. Fat deposition and the quality of fat tissue in meat animals. Fats in Animal Nutrition (Wood J), 1st Edition, 420–421.Google Scholar