基于低场核磁共振的预包装即食牛肉保质期预测模型研究

Research on Shelf Life Predicting Model of Prepackaged Instant Beef Based on Low-field Nuclear Magnetic Resonance

目的:开展预包装即食牛肉保质期快速、准确和无损的预测方法研究,对于保障产品货架期安全具有重要意义。方法:以预包装即食牛肉为分析对象,采集样品低场核磁H质子的T2弛豫时间,建立即食牛肉水分含量定量分析模型,结合样品感官接受性等建立预包装即食牛肉保质期预测模型。结果:预包装即食牛肉低场核磁谱图横向弛豫时间可较好地反映出随着贮存时间的延长航天即食牛肉的品质变化。建立了即食牛肉含水量预测模型,模型的预测误差小于4%;建模集相关系数(r)为0.9405,校正标准差(RMSECV)为34.5,相对分析误差(RPD)为3.1,对10个分别贮存一定时间但未参与建模的样本分别进行距离货架期终点的预测,预测结果与实测值的的相关性达0.99,预测结果的误差范围为0.7%~9.9%,RMSEP为13.6,预测模型的精确度满足货架期预测的精度要求。结论:低场核磁共振技术在预包装即食牛肉产品保质期预测方面具有应用潜力。

Objective:Conducting research on fast,accurate,and non-destructive prediction methods for the shelf life of prepackaged instant beef had great significance for ensuring the safety of the shelf life of product.Methods:Using prepackaged instant beef as the analysis object,the T2 relaxation time of low field nuclear magnetic resonance(NMR)proton was collected from the sample,and a quantitative analysis model for the moisture content of instant beef was established.A prediction model for the shelf life of prepackaged instant beef was established based on the sensory acceptance of the sample.Results:The lateral relaxation time of the low-field nuclear magnetic resonance spectrum of prepackaged instant beef could better reflect the quality changes of spaceflight instant beef with the extension of storage time.A water content prediction model for instant beef was established,with a prediction error of less than 4%.The correlation coefficient(r)of the modeling set was 0.9405,the corrected standard deviation(RMSECV)was 34.5,and the relative analysis error(RPD)was 3.1.Ten samples stored for a certain period of time but not involved in modeling were predicted to be close to the end of shelf life.The correlation between the predicted results and the measured values was 0.99,and the error range of the predicted results was 0.7%to 9.9%.The RMSEP was 13.6.The accuracy of the prediction model met the accuracy requirements of shelf life prediction.Conclusion:Low field nuclear magnetic resonance technology would have potential application in predicting the shelf life of prepackaged instant beef products.