Department of Agronomy, University of Padova, Viale dell'Università 16, 35020 Legnaro, PD, Italy.
Meat Sci. 2013 Feb;93(2):329-35. doi: 10.1016/j.meatsci.2012.09.013. Epub 2012 Oct 2.
Visible and near infrared reflectance (Vis-NIR, 350 to 1800 nm), and near infrared transmittance (NIT, 850 to 1050 nm) spectroscopy were used to predict beef quality traits of intact and ground meat samples. Calibration equations were developed from reference data (n = 312) of pH, color traits (L*, a*, and b*), ageing loss (%), cooking loss (%), and Warner-Bratzler shear force (WBSF, N) using partial least squares regressions. Predictive ability of the models was assessed by coefficient of determination of cross-validation (R(2)(CV)) and root mean square error of cross-validation. Quality traits were better predicted on intact than on ground samples, and the best results were obtained using Vis-NIR spectroscopy. Predictions were good (R(2)(CV) = 0.62 to 0.73) for pH, L*, and a*, hardly sufficient (R(2)(CV) = 0.34 to 0.60) for b*, cooking loss, and WBSF, and unsatisfactory for ageing loss (R(2)(CV) = 0.15). Vis-NIR spectroscopy might be used to predict some physical beef quality traits on intact meat samples.
可见近红外反射光谱（Vis-NIR，350 至 1800nm）和近红外透射光谱（NIT，850 至 1050nm）用于预测完整和绞碎肉样的牛肉质量特性。使用偏最小二乘回归，从 pH 值、颜色特性（L*、a和 b）、老化损失（%）、蒸煮损失（%）和 Warner-Bratzler 剪切力（WBSF，N）的参考数据（n=312）建立校准方程。通过交叉验证的决定系数（R(2)(CV)）和交叉验证均方根误差评估模型的预测能力。与绞碎肉样相比，完整肉样的质量特性预测效果更好，且 Vis-NIR 光谱的预测结果最佳。对于 pH 值、L和 a，预测结果良好（R(2)(CV) = 0.62 至 0.73），对于 b*、蒸煮损失和 WBSF，预测结果勉强足够（R(2)(CV) = 0.34 至 0.60），而对于老化损失，预测结果不佳（R(2)(CV) = 0.15）。Vis-NIR 光谱可能用于预测完整肉样的一些物理牛肉质量特性。
