Department of Food Quality and Sensory Science, Teagasc Food Research Centre, Ashtown, Dublin 15, Ireland; School of Food and Nutritional Sciences, University College Cork, Cork, Ireland.
School of Biosystems and Food Engineering, University College Dublin, Belfield, Dublin 4, Ireland.
Food Res Int. 2017 Sep;99(Pt 1):778-789. doi: 10.1016/j.foodres.2017.06.056. Epub 2017 Jun 27.
Raman spectroscopy and chemometrics were investigated for the prediction of eating quality related physico-chemical traits of Holstein-Friesian bull beef. Raman spectra were collected on the 3rd, 7th and 14th days post-mortem. A frequency range of 1300-2800cm was used for partial least squares (PLS) modelling. PLS regression (PLSR) models for the prediction of WBSF and cook loss achieved an RCV of 0.75 with RMSECV of 6.82 N and an RCV of 0.77 with RMSECV of 0.97%w/w respectively. For the prediction of intramuscular fat, moisture and crude protein content, RCV values were 0.85, 0.91 and 0.70 with RMSECV of 0.52%w/w, 0.39%w/w and 0.38%w/w respectively. An RCV of 0.79 was achieved for the prediction of both total collagen and hydroxyproline content, while for collagen solubility the RCV was 0.88. All samples (100%) from 15- and 19-month old bulls were correctly classified using PLS discriminant analysis (PLS-DA), while 86.7% of samples from different muscles (longissimus thoracis, semitendinosus and gluteus medius) were correctly classified. In general, PLSR models using Raman spectra on the 3rd day post-mortem had better prediction performance than those on the 7th and 14th days. Raman spectroscopy and chemometrics have potential to assess several beef physical and chemical quality traits.
拉曼光谱和化学计量学被用于预测荷斯坦-弗里森公牛牛肉的与食用品质相关的理化特性。拉曼光谱在宰后第 3、7 和 14 天采集。使用 1300-2800cm 的频率范围进行偏最小二乘（PLS）建模。用于预测 WBSF 和煮失率的 PLS 回归（PLSR）模型的交叉验证 RCV 为 0.75，交叉验证均方根误差（RMSECV）为 6.82N；预测肌内脂肪、水分和粗蛋白含量的 RCV 值分别为 0.85、0.91 和 0.70，交叉验证均方根误差（RMSECV）分别为 0.52%w/w、0.39%w/w 和 0.38%w/w。预测总胶原蛋白和羟脯氨酸含量的 RCV 值分别为 0.79，预测胶原蛋白溶解度的 RCV 值为 0.88。使用偏最小二乘判别分析（PLS-DA），可以正确分类 15 月龄和 19 月龄公牛的所有样本（100%），而来自不同肌肉（胸最长肌、半腱肌和臀中肌）的 86.7%的样本被正确分类。总体而言，在宰后第 3 天使用拉曼光谱建立的 PLSR 模型的预测性能优于在第 7 天和第 14 天建立的模型。拉曼光谱和化学计量学有可能评估牛肉的几个物理和化学质量特性。
