近红外光谱分析技术快速检测冰温贮藏牛肉品质

摘要

In this experiment, a rapid quantitative detection method was proposed by near infrared spectroscopy (NIR) for beef quality during ice temperature storage. The calibration models of beef pH, water loss, TVB-N and color value (L*/a*) were established by NIR technique allowing the simultaneous predication of several beef quality indicators. The correlation coefficient (R2) of the calibration models were all above 0.70 and theR2 values for the predicted and actual values were all above 0.90. The calibration models had high prediction accuracy. Furthermore, cluster analysis was used to categorize the near infrared spectral data of beef stored for different durations. The results showed that based on the near infrared spectral data meat freshness was categorized well. NIR is suitable for rapid and non-invasive estimation beef quality and freshness as an alternative to the traditional detection method.%利用近红外光谱分析技术建立冰温贮藏牛肉品质的快速定量检测方法。采用近红外光谱技术建立近红外光谱的pH值、失水率、挥发性碱基总氮、色差值（L*/a*）的校正模型，能同时预测出牛肉样品的多项品质指标。结果表明：建立的校正模型相关系数都在0.70以上，校正模型的预测值与真实值决定系数均在0.90以上，具有较高的预测准确度。并且利用聚类分析的方法对不同贮藏阶段肉品近红外光谱的数据进行了分类处理，聚类分析结果证明，近红外反射光谱对牛肉的新鲜程度有着较好的分类结果。近红外光谱技术能够替代传统方法快速、非破坏地评价牛肉的肉品质及新鲜程度。