CAJ | 학술논문

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。
이용근홍외광보분석기술건립빙온저장우육품질적쾌속정량검측방법。채용근홍외광보기술건립근홍외광보적pH치、실수솔、휘발성감기총담、색차치（L*/a*）적교정모형，능동시예측출우육양품적다항품질지표。결과표명：건립적교정모형상관계수도재0.70이상，교정모형적예측치여진실치결정계수균재0.90이상，구유교고적예측준학도。병차이용취류분석적방법대불동저장계단육품근홍외광보적수거진행료분류처리，취류분석결과증명，근홍외반사광보대우육적신선정도유착교호적분류결과。근홍외광보기술능구체대전통방법쾌속、비파배지평개우육적육품질급신선정도。
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.