School of Biosystems and Food Engineering, University College Dublin, Belfield Dublin 4, Ireland.
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.
Food Res Int. 2018 May;107:27-40. doi: 10.1016/j.foodres.2018.02.007. Epub 2018 Feb 5.
This work aims to develop a rapid analytical technique to predict beef sensory attributes using Raman spectroscopy (RS) and to investigate correlations between sensory attributes using chemometric analysis. Beef samples (n = 72) were obtained from young dairy bulls (Holstein-Friesian and Jersey×Holstein-Friesian) slaughtered at 15 and 19 months old. Trained sensory panel evaluation and Raman spectral data acquisition were both carried out on the same longissimus thoracis muscles after ageing for 21 days. The best prediction results were obtained using a Raman frequency range of 1300-2800 cm. Prediction performance of partial least squares regression (PLSR) models developed using all samples were moderate to high for all sensory attributes (RCV values of 0.50-0.84 and RMSECV values of 1.31-9.07) and were particularly high for desirable flavour attributes (RCVs of 0.80-0.84, RMSECVs of 4.21-4.65). For PLSR models developed on subsets of beef samples i.e. beef of an identical age or breed type, significant improvements on prediction performances were achieved for overall sensory attributes (RCVs of 0.63-0.89 and RMSECVs of 0.38-6.88 for each breed type; RCVs of 0.52-0.89 and RMSECVs of 0.96-6.36 for each age group). Chemometric analysis revealed strong correlations between sensory attributes. Raman spectroscopy combined with chemometric analysis was demonstrated to have high potential as a rapid and non-destructive technique to predict the sensory quality traits of young dairy bull beef.
本研究旨在开发一种快速分析技术，使用拉曼光谱（RS）预测牛肉的感官属性，并通过化学计量分析研究感官属性之间的相关性。从 15 月龄和 19 月龄的年轻奶牛（荷斯坦-弗里森和泽西牛×荷斯坦-弗里森）屠宰后获得牛肉样品（n=72）。在 21 天老化后，在同一背最长肌上同时进行经过训练的感官小组评估和拉曼光谱数据采集。使用拉曼频率范围为 1300-2800 cm 时，可获得最佳预测结果。使用所有样品开发的偏最小二乘回归（PLSR）模型对所有感官属性的预测性能均为中等至高（RCV 值为 0.50-0.84，RMSECV 值为 1.31-9.07），对理想风味属性的预测性能特别高（RCV 值为 0.80-0.84，RMSECV 值为 4.21-4.65）。对于基于牛肉样品子集（即年龄或品种相同的牛肉）开发的 PLSR 模型，对整体感官属性的预测性能有了显著提高（每个品种的 RCV 值为 0.63-0.89，RMSECV 值为 0.38-6.88；每个年龄组的 RCV 值为 0.52-0.89，RMSECV 值为 0.96-6.36）。化学计量分析表明感官属性之间存在很强的相关性。拉曼光谱结合化学计量分析被证明具有作为快速无损技术预测年轻奶牛牛肉感官质量特性的巨大潜力。
