Department of Animal Science, University of Padova, Viale dell'Università 16, 35020 Legnaro, Padua, Italy.
J Anim Sci. 2011 Sep;89(9):2687-95. doi: 10.2527/jas.2010-3740. Epub 2011 Mar 31.
The aims of this study were 1) to investigate the potential application of near-infrared spectroscopy (NIRS) to predict beef quality (BQ) traits, 2) to assess genetic variations of BQ measures and their predictions obtained by NIRS, and 3) to infer the genetic relationship between measures of BQ and their predictions. Young Piedmontese bulls (n = 1,230) were raised and fattened on 124 farms and slaughtered at the same commercial abattoir. The BQ traits evaluated were shear force (SF, kg), cooking loss (CL, %), drip loss (DL, %), lightness (L*), redness (a*), yellowness (b*), saturation index (SI), and hue angle. Near-infrared spectra were collected using a Foss NIRSystems 5000 instrument over a spectral range of 1,100 to 2,498 nm every 2 nm, in reflectance mode. After editing, prediction models were developed on a calibration subset (n = 268) using partial least squares regressions, followed by application of these models to the validation subset (n = 940). Estimations of (co)variance for measures of BQ and NIRS-based predictions were obtained through a set of bivariate Bayesian analyses on the validation subset. Near-infrared predictions were satisfactory for measurements of L* (R(2) = 0.64), a* (R(2) = 0.68), hue angle (R(2) = 0.81), and saturation index (R(2) = 0.59), but not for b*, DL, CL, and SF. The loss of additive genetic variance of predicted vs. measured L*, a*, DL, CL, and SF was generally high and was similar to the loss of residual variance, being a function of the calibration parameter R(2). As a consequence, estimated heritabilities of measures and predictions of BQ were similar for traits with high calibration R(2) values. Genetic correlations between BQ measures and predictions were high for all color traits and DL, and were greater than the corresponding phenotypic correlations, whereas both the phenotypic and genetic correlations for SF and CL were nil. Results suggest that NIRS-based predictions for color features and DL may be used as indicator traits to improve meat quality of the Piedmontese breed.
1）探索近红外光谱（NIRS）在预测牛肉品质（BQ）特性方面的潜在应用，2）评估 BQ 测量值的遗传变异及其通过 NIRS 获得的预测值，3）推断 BQ 测量值与其预测值之间的遗传关系。年轻的皮埃蒙特公牛（n = 1,230）在 124 个农场饲养和育肥，并在同一商业屠宰场屠宰。评估的 BQ 特性包括剪切力（SF，kg）、蒸煮损失（CL，%）、滴水损失（DL，%）、亮度（L*）、红色度（a*）、黄色度（b*）、饱和度指数（SI）和色调角。使用 Foss NIRSystems 5000 仪器以反射模式在 1,100 至 2,498nm 的光谱范围内每隔 2nm 采集近红外光谱。在编辑后，使用偏最小二乘回归在校准子集（n = 268）上开发预测模型，然后将这些模型应用于验证子集（n = 940）。通过对验证子集进行一系列二元贝叶斯分析，获得了 BQ 测量值和基于 NIRS 的预测值的（协）方差估计值。近红外预测对 L*（R²=0.64）、a*（R²=0.68）、色调角（R²=0.81）和饱和度指数（R²=0.59）的测量值是令人满意的，但对 b*、DL、CL 和 SF 则不然。与测量值相比，预测值的加性遗传方差损失通常较高，且与剩余方差损失相似，这是校准参数 R²的函数。因此，对于校准 R²值较高的性状，测量值和预测值的估计遗传力相似。所有颜色性状和 DL 的 BQ 测量值和预测值之间的遗传相关性较高，且大于相应的表型相关性，而 SF 和 CL 的表型和遗传相关性均为零。结果表明，基于 NIRS 的颜色特征和 DL 预测值可作为指示性状，以提高皮埃蒙特品种的肉质。
