02140naa a2200337 a 450000100080000000500110000800800410001902400520006010000190011224501180013126000090024952011690025865000210142765000110144865300080145965300180146765300160148565300120150170000230151370000130153670000220154970000220157170000140159370000190160770000210162670000230164770000170167070000210168770000210170877300730172921508562024-02-16       2023    bl uuuu     u00u1 u    #d7 ahttps://doi.org/10.1016/j.jfca.2022.1051042DOI1 aLOBO, A. A. G.  aThe use of dual energy x-ray absorptiometry (DXA) to predict the veal carcass composition.h[electronic resource]  c2023  aThe objective of this study was to evaluate the potential of dual energy X-ray absorptiometry (DXA) to estimate the carcass composition of veal calves. A total of 15 newborn Holstein × Gyr male calves were randomly assigned to two dietary treatments: calves fed exclusively milk or fed milk + concentrate ad libitum. Calves were assigned to four slaughter groups (7, 28, 49 and 63 days of age) and after 24 h of chilling carcasses were analyzed by DXA using the enCORE software to obtain fat, lean tissue, and bone mineral content and density. Afterward, carcasses were dissected into lean, fat, and bone tissues that were ground and lyophilized to determine the carcasses chemical composition. The DXA predicted the protein (P &lt 0.0001, R2 = 0.7290), mineral (P = 0.002, R2 = 0.6119) contents, proportions of muscle (P &lt 0.001, R2 = 0.6986) and bone of veal carcasses (P &lt 0.0001, R2 = 0.6982), but not fat (P = 0.9557, R2 = 0.002). In conclusion, the DXA measured values were able to predict veal carcass protein, mineral contents, lean and bones amount but was not precise enough to detect the fat tissue on lean carcasses such as those from veal calves.  aBody composition  aCalves  aAsh  aEther extract  aLean tissue  aProtein1 aCÔNSOLO, N. R. B.1 aDIAS, J.1 aMENEZES, A. C. B.1 aMARTINS, T. DA S.1 aSILVA, J.1 aMACHADO, F. S.1 aMARCONDES, M. I.1 aPFLANZER JR, S. B.1 aNASSU, R. T.1 aSCHEFFLER, T. L.1 aCHIZZOTTI, M. L.  tJournal of Food Composition and Analysisgv. 117, apr. 2023, 105104.