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利用拉曼光谱和化学计量学评估不同成熟时间下犊牛肉食用品质的理化特性。

Assessment of physico-chemical traits related to eating quality of young dairy bull beef at different ageing times using Raman spectroscopy and chemometrics.

机构信息

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.

出版信息

Food Res Int. 2017 Sep;99(Pt 1):778-789. doi: 10.1016/j.foodres.2017.06.056. Epub 2017 Jun 27.

Abstract

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.

摘要

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。

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