Department of Primary Industries New South Wales, Elizabeth Macarthur Agricultural Institute (EMAI), Menangle, New South Wales 2568, Australia.
J Anim Sci. 2011 Dec;89(12):3935-44. doi: 10.2527/jas.2010-3728. Epub 2011 Aug 5.
Ninety hybrid (mainly Large White × Landrace) pigs from 2 experimental replicates were used to study the potential use of computed tomography (CT) as a nondestructive technology for estimating the chemical body composition of growing pigs. Body tissue components (lean, fat, and bone) of 6 live pigs from each sex (boars, gilts, and barrows) were assessed by CT imaging before slaughter at approximately 30, 60, 90, 120, and 150 kg of BW. After slaughter, the empty body components were ground and frozen until analyzed for protein, lipid, ash, and moisture content. Several growth functions were evaluated and the allometric function (Y = aBW(b)), which was evaluated as log(10)chemical component weight = b(0) + b(1)log(10)BW, provided the best fit to the data. For each sex, the allometric coefficient (b(1)) for protein (0.92 to 0.99) was close to but less than 1; for ash (1.03 to 1.12), it was close to but greater than 1; for moisture (0.82 to 0.86), it was less than 1, and for lipid (1.61 to 1.71), it was greater than 1. Deposition rates (change in component weight per unit change in BW) for each chemical component were predicted using derivatives of the function. The mean deposition rates for protein and lipid were 0.141 and 0.286 kg/kg of BW gain, respectively. The deposition rate for protein was generally stable across different BW, whereas that for lipid increased as BW increased. In addition, linear, quadratic, exponential, and logistic functions were fitted to the data to study the relationship between the CT data and chemical components. The linear function was assessed to be the best equation, based on the Bayesian information criterion. The prediction equation for protein (kg) = -1.64 + 0.28 × CT lean (kg), and for lipid (kg) = -0.69 + 1.09 × CT fat (kg), had R(2) values of 0.924 and 0.987, respectively. Sex had no effect (P > 0.05) on the prediction of protein and lipid. The effect of BW was not significant (P > 0.05) for the prediction of lipid, but it was significant (P > 0 0.05) for the prediction of protein. However, the addition of BW to the base prediction equation for protein resulted in an increase of only 0.013 in the R(2) value. It was concluded from this study that CT scanning has great potential as a nondestructive technology for estimating the physical and chemical body composition of pigs. Additional research is required to validate the utility and accuracy of the prediction equations.
本研究使用 90 头杂交（主要是大白猪和长白猪）猪，来自 2 个实验重复，旨在研究计算机断层扫描（CT）作为一种非破坏性技术，用于估计生长猪的化学体组成的潜力。在大约 30、60、90、120 和 150 公斤体重时，对每个性别（公猪、母猪和小母猪）的 6 头活猪进行 CT 成像，以评估体组织成分（瘦肉、脂肪和骨骼）。在屠宰后，将空体成分粉碎并冷冻，直到分析蛋白质、脂质、灰分和水分含量。评估了几种生长功能，以及幂函数（Y = aBW(b)），该函数被评估为log(10)化学组分重量=b(0)+b(1)log(10)BW，为数据提供了最佳拟合。对于每个性别，蛋白质的幂函数系数（b(1)）为 0.92 至 0.99，接近但小于 1；灰分的系数（b(1)）为 1.03 至 1.12，接近但大于 1；水分的系数（b(1)）为 0.82 至 0.86，小于 1；脂质的系数（b(1)）为 1.61 至 1.71，大于 1。使用该函数的导数预测每个化学组分的沉积率（BW 每单位变化时组分重量的变化）。蛋白质和脂质的平均沉积率分别为 0.141 和 0.286 公斤/公斤体重增加。蛋白质的沉积率通常在不同的 BW 下保持稳定，而脂质的沉积率随着 BW 的增加而增加。此外，还拟合了线性、二次、指数和逻辑函数来研究 CT 数据与化学组分之间的关系。基于贝叶斯信息准则，线性函数被评估为最佳方程。蛋白质（kg）的预测方程=-1.64+0.28×CT lean（kg），脂质（kg）的预测方程=-0.69+1.09×CT fat（kg），其 R(2)值分别为 0.924 和 0.987。性别对蛋白质和脂质的预测没有影响（P＞0.05）。BW 对脂质的预测没有显著影响（P＞0.05），但对蛋白质的预测有显著影响（P＞0.05）。然而，将 BW 添加到蛋白质的基本预测方程中，仅使 R(2)值增加了 0.013。本研究表明，CT 扫描作为一种非破坏性技术，具有很大的潜力，可以估计猪的物理和化学体组成。需要进一步的研究来验证预测方程的实用性和准确性。
