Zomeño C, Gispert M, Carabús A, Brun A, Font-I-Furnols M
IRTA-Product Quality,Finca Camps i Armet,E-17121 Monells,Catalonia,Spain.
Animal. 2016 Jan;10(1):172-81. doi: 10.1017/S1751731115001780. Epub 2015 Aug 28.
The aims of this study were (1) to evaluate the ability of computed tomography (CT) to predict the chemical composition of live pigs and carcasses, (2) to compare the chemical composition of four different sex types at a commercial slaughter weight and (3) to model and evaluate the chemical component growth of these sex types. A total of 92 pigs (24 entire males (EM), 24 surgically castrated males (CM), 20 immunocastrated males (IM) and 24 females (FE)) was used. A total of 48 pigs (12 per sex type) were scanned repeatedly in vivo using CT at 30, 70, 100 and 120 kg and slaughtered at the end of the experiment. The remaining 44 were CT scanned in vivo and slaughtered immediately: 12 pigs (4 EM, 4 CM and 4 FE) at 30 kg and 16 pigs each at 70 kg and 100 kg (4 per sex type). The left carcasses were CT scanned, and the right carcasses were minced and analysed for protein, fat, moisture, ash, Ca and P content. Prediction equations for the chemical composition were developed using Partial Least Square regression. Allometric growth equations for the chemical components were modelled. By using live animal and carcass CT images, accurate prediction equations were obtained for the fat (with a root mean square error of prediction (RMSEPCV) of 1.31 and 1.34, respectively, and R 2=0.91 for both cases) and moisture relative content (g/100 g) (RMSEPCV=1.19 and 1.38 and R 2=0.94 and 0.93, respectively) and were less accurate for the protein (RMSEPCV=0.65 and 0.67 and R 2=0.54 and 0.63, respectively) and mineral content (RMSEPCV from 0.28 to 1.83 and R 2 from 0.09 to 0.62). Better equations were developed for the absolute amounts of protein, fat, moisture and ash (kg) (RMSEPCV from 0.26 to 1.14 and R 2 from 0.91 to 0.99) as well as Ca and P (g) (RMSEPCV=144 and 71, and R 2=0.76 to 0.66, respectively). At 120 kg, CM had a higher fat and lower moisture content than EM. For protein, CM and IM had lower values than FE and EM. The ash content was higher in EM and IM than in FE and CM, while IM had a higher Ca and P content than the others. The castrated animals showed a higher allometric coefficient for fat and a lower one for moisture, with IM having intermediate values. However, for the Ca and P models, IM presented higher coefficients than EM and FE, and CM were intermediate.
(1)评估计算机断层扫描(CT)预测生猪和胴体化学成分的能力;(2)比较商业屠宰体重下四种不同性别类型的化学成分;(3)对这些性别类型的化学成分生长进行建模和评估。总共使用了92头猪(24头未阉割公猪(EM)、24头手术阉割公猪(CM)、20头免疫去势公猪(IM)和24头母猪(FE))。总共48头猪(每种性别类型12头)在体重30、70、100和120千克时使用CT进行多次活体扫描,并在实验结束时屠宰。其余44头进行活体CT扫描并立即屠宰:30千克体重时12头猪(4头EM、4头CM和4头FE),70千克和100千克体重时各16头猪(每种性别类型4头)。对左侧胴体进行CT扫描,右侧胴体切碎后分析蛋白质、脂肪、水分、灰分、钙和磷含量。使用偏最小二乘回归建立化学成分的预测方程。对化学成分的异速生长方程进行建模。通过使用活体动物和胴体CT图像,获得了脂肪(预测均方根误差(RMSEPCV)分别为1.31和1.34,两种情况下R² = 0.91)和水分相对含量(克/100克)(RMSEPCV = 1.19和1.38,R²分别为0.94和0.93)的准确预测方程,而蛋白质(RMSEPCV = 0.65和0.67,R²分别为0.54和0.63)和矿物质含量(RMSEPCV从0.28到1.83,R²从0.09到0.62)的预测方程准确性较低。针对蛋白质、脂肪、水分和灰分(千克)的绝对含量(RMSEPCV从0.26到1.14,R²从0.91到0.99)以及钙和磷(克)(RMSEPCV = 144和71,R²分别为0.76到0.66)建立了更好的方程。在120千克时,CM的脂肪含量高于EM,水分含量低于EM。对于蛋白质,CM和IM的值低于FE和EM。EM和IM的灰分含量高于FE和CM,而IM的钙和磷含量高于其他组。去势动物脂肪的异速生长系数较高,水分的异速生长系数较低,IM的值介于两者之间。然而,对于钙和磷模型,IM的系数高于EM和FE,CM介于两者之间。