Sahar Amna, Allen Paul, Sweeney Torres, Cafferky Jamie, Downey Gerard, Cromie Andrew, Hamill Ruth M
Teagasc Food Research Centre Ashtown, Dublin D15 KN3K, Ireland.
UCD School of Veterinary Medicine, University College Dublin, Belfield, Dublin D04 W6F6, Ireland.
Foods. 2019 Oct 23;8(11):525. doi: 10.3390/foods8110525.
The potential of visible-near-infrared (Vis-NIR) spectroscopy to predict physico-chemical quality traits in 368 samples of bovine musculus longissimus thoracis et lumborum (LTL) was evaluated. A fibre-optic probe was applied on the exposed surface of the bovine carcass for the collection of spectra, including the neck and rump (1 h and 2 h post-mortem and after quartering, i.e., 24 h and 25 h post-mortem) and the boned-out LTL muscle (48 h and 49 h post-mortem). In parallel, reference analysis for physico-chemical parameters of beef quality including ultimate pH, colour (L, a*, b*), cook loss and drip loss was conducted using standard laboratory methods. Partial least-squares (PLS) regression models were used to correlate the spectral information with reference quality parameters of beef muscle. Different mathematical pre-treatments and their combinations were applied to improve the model accuracy, which was evaluated on the basis of the coefficient of determination of calibration (RC) and cross-validation (RCV) and root-mean-square error of calibration (RMSEC) and cross-validation (RMSECV). Reliable cross-validation models were achieved for ultimate pH (RCV: 0.91 (quartering, 24 h) and RCV: 0.96 (LTL muscle, 48 h)) and drip loss (RCV: 0.82 (quartering, 24 h) and RCV: 0.99 (LTL muscle, 48 h)) with lower RMSECV values. The results show the potential of Vis-NIR spectroscopy for online prediction of certain quality parameters of beef over different time periods.
评估了可见-近红外（Vis-NIR）光谱法预测368份牛胸腰段最长肌（LTL）样本理化品质性状的潜力。将光纤探头应用于牛胴体的暴露表面以采集光谱，包括颈部和臀部（宰后1小时和2小时以及四分体分割后，即宰后24小时和25小时）以及剔骨后的LTL肌肉（宰后48小时和49小时）。同时，使用标准实验室方法对牛肉品质的理化参数进行参考分析，包括最终pH值、颜色（L、a*、b*）、熟肉损失和滴水损失。采用偏最小二乘（PLS）回归模型将光谱信息与牛肉肌肉的参考品质参数相关联。应用了不同的数学预处理及其组合来提高模型准确性，并根据校准决定系数（RC）和交叉验证决定系数（RCV）以及校准均方根误差（RMSEC）和交叉验证均方根误差（RMSECV）对其进行评估。对于最终pH值（RCV：0.91（四分体分割，24小时）和RCV：0.96（LTL肌肉，48小时））和滴水损失（RCV：0.82（四分体分割，24小时）和RCV：0.99（LTL肌肉，48小时））获得了可靠的交叉验证模型，且RMSECV值较低。结果表明Vis-NIR光谱法在不同时间段在线预测牛肉某些品质参数方面具有潜力。
