冻藏罗非鱼片品质变化及其货架期预测模型的建立

Predictive Modeling for Monitoring the Quality Change and Shelf-life of Frozen Tilapia Fillets in Cold-chain

摘要:

本文分析了罗非鱼片在-18 ℃、-14 ℃和-10 ℃条件下贮藏的感官、微生物和理化等指标变化，从中选择一个敏感指标用于货架期预测模型的建立。结果显示，随着贮藏时间的延长，冻罗非鱼片的菌落总数对数值缓慢增长，在120 d时分别达到4.31 log(CFU/g)、4.76 log(CFU/g)和5.47 log(CFU/g)，均未达到国家规定的冻罗非鱼片菌落总数上限值。TVB-N值呈线性上升，分别在60 d、80 d和120 d降至二级鲜度13 mg/100 g；TBA值逐渐升高，但各实验组间反应速率差异不显著。冻罗非鱼片的感官评分逐渐下降，但在贮藏末期仍保持着较好的感官品质。随贮藏温度越低，品质指标衰变越缓慢。运用Pearson相关系数对各品质指标进行分析，确定敏感指标为TVB-N，结合拟合优度分析结果判断TVB-N指标的变化符合零级反应。应用Arrhenius方程构建贮藏温度（T）与反应速率（ka）间的动力学方程，并以此为基础，推导得到恒定冷链温度条件下冻罗非鱼片货架期预测模型。在-10 ℃和-14 ℃下评价货架期预测模型可靠性，能够准确预测冻罗非鱼片的品质随温度的变化情况。

Abstract:

This study examined the quality change as well as the sensitive index of frozen tilapia fillets stored at -18 ℃, -14 ℃, -10 ℃, according to the results of sensory, microorganism and physicochemical index. The results showed that the total plate count of numerical values rising slowly with the prolongation of storage time, and reached to 4.31 log(CFU/g), 4.76 log(CFU/g), 5.47 log(CFU/g), respectively at 120 days when stored at -18℃, -14℃, -10℃. It did not reach the national limit of the total number of frozen tilapia fillets. Total volatile basic nitrogen (TVB-N) values rose linearly and reduced to 13 mg/100 g at 120 d, 80 d, 60 d, respectively. Thiobarbituric acid (TBA) value increased gradually but there were no significant differences amongst groups. The sensory quality of frozen tilapia fillets gradually decayed with storage time, but maintained a good sensory quality at the end of storage, and the lower the temperature of freezing storage had, the slower the quality decayed. According to Pearson’s correlation coefficient, TVB-N was the sensitive index and its change was accordance with zero order reaction when combining with the Goodness-of-fit analysis and Residual sum of squares (RSS). Using Arrhenius equation to build dynamic equation between storage temperature (T) and the reaction rate (ka), the shelf-life prediction model of frozen tilapia fillets in cold-chain logistics was built. It has proved that this model can accurately predict the shelf life of frozen tilapia fillets at -14 ℃ and -10 ℃.