Department of Agricultural Sciences, Texas A&M University-Commerce, Commerce 75429-3011, USA.
J Anim Sci. 2012 Sep;90(9):3259-65. doi: 10.2527/jas.2011-4697. Epub 2012 May 14.
The objective of this study was to re-evaluate our previously published technique of estimating total physically separable internal fat (IFAT) in beef cattle using real-time ultrasound (RTU) and carcass measurements from live animals by including more breed types and genders under different management scenarios. We expanded the original database and performed additional analyses. The database was gathered from 4 studies and contained 110 animals (16 bulls, 16 heifers, and 78 steers), being Angus (n = 56), Angus× 5/8 Angus × 3/8 Nellore (n = 18), and Angus crossbreds (n = 36). Ultrasound measurements were obtained 7 d before slaughter, including the 12th to 13th rib fat thickness (uBF) and ultrasound kidney fat depth (uKFd). The uKFd was measured in a cross-sectional image collected between the first lumbar and 13th rib as previously published. Carcass data were collected 48 h post-mortem and consisted of backfat thickness (cBF), kidney fat depth (cKFd) and KPH weight, live BW, and HCW. Whole gastrointestinal tracts were removed and dissected to obtain IFAT weights. Weight of IFAT was highly correlated with KPH weight (0.88) and cKFd (0.81) and moderately correlated with uKFd (0.71). Prediction equations were developed for estimating IFAT, KPH weight, and cKFd with the PROC REG of SAS using the stepwise statement. The best predictors of IFAT were KPH weight or cKFd and cBF (r(2) = 0.84 and 0.83 and root mean square errors (RMSE) of 4.23 and 4.33 kg, respectively). Ultrasound measurements of uKFd and uBF had an r(2) of 0.65 and RMSE of 6.07 kg when both were used to predict IFAT. The results of cross-validation analyses indicated that equations developed either with KPH weight or cKFd weight and cBF had greater precision than the equation developed with uKFd and uBF. Most of the errors associated with the mean square error of prediction were due to random, uncontrolled variation. These results were consistent with previously published evaluation of this technique. These findings confirm that this RTU technique allows the measurement of IFAT in a non-invasive way that may improve our ability to estimate IFAT in beef cattle, be used to more accurately formulate rations, and be applied in sorting cattle at feedyard.
本研究的目的是通过包括更多的品种和性别以及不同的管理场景,重新评估我们之前发表的使用实时超声(RTU)和活体动物胴体测量来估计牛肉中总可分离体脂(IFAT)的技术。我们扩展了原始数据库并进行了额外的分析。该数据库来自 4 项研究,包含 110 头动物(16 头公牛、16 头小母牛和 78 头阉牛),包括安格斯牛(n=56)、安格斯牛×5/8 安格斯牛×3/8 尼里-拉菲牛(n=18)和安格斯杂交牛(n=36)。超声测量在屠宰前 7 天进行,包括第 12 到 13 肋骨的脂肪厚度(uBF)和超声肾脂深度(uKFd)。uKFd 的测量方法与之前发表的方法相同,即在第 1 腰椎和第 13 肋骨之间采集横截面图像进行测量。胴体数据在死后 48 小时收集,包括背膘厚度(cBF)、肾脂深度(cKFd)和 KPH 重量、活体 BW 和 HCW。整个胃肠道被取出并解剖以获得 IFAT 重量。IFAT 重量与 KPH 重量(0.88)和 cKFd(0.81)高度相关,与 uKFd(0.71)中度相关。使用 SAS 的 PROC REG 程序使用逐步语句为估计 IFAT、KPH 重量和 cKFd 开发预测方程。IFAT 的最佳预测因子是 KPH 重量或 cKFd 和 cBF(r(2)分别为 0.84 和 0.83,均方根误差(RMSE)分别为 4.23 和 4.33 千克)。当同时使用 uKFd 和 uBF 预测 IFAT 时,uKFd 和 uBF 的超声测量的 r(2)为 0.65,RMSE 为 6.07 千克。交叉验证分析的结果表明,使用 KPH 重量或 cKFd 重量和 cBF 开发的方程比使用 uKFd 和 uBF 开发的方程具有更高的精度。预测误差的大部分与均方根误差有关,是由于随机、不可控的变化造成的。这些结果与之前对该技术的评估一致。这些发现证实,这种 RTU 技术允许以非侵入性的方式测量 IFAT,这可能提高我们在牛肉中估计 IFAT 的能力,有助于更准确地制定配方,并应用于饲养场的牛群分类。