Faculty of Agriculture, Institute of Animal Science, University of Bonn, Bonn, Germany.
J Appl Microbiol. 2010 Feb;108(2):510-20. doi: 10.1111/j.1365-2672.2009.04451.x. Epub 2009 Jun 30.
Development of a predictive model for the determination of the shelf life of modified atmosphere-packed (MAP) cooked sliced ham in each step of the cold chain.
The growth of lactic acid bacteria (LAB), as well as the development of the total viable count and changes of sensory and pH value parameters in MAP cooked sliced ham, stored under different constant temperature conditions from 2 to 15 degrees C was investigated. As a result of the measurements, the end of the shelf life could be considered as the time when LAB reach more than 7 log(10) CFU g(-1). Different primary and secondary models were tested and analysed to find the best way to calculate the shelf life. For primary modelling, the modified Gompertz Function and the modified Logistic Function were compared. There was no substantial difference between either model. The effect of temperature on the growth rate was modelled by using the Arrhenius and the Square root model, whereas the Arrhenius equation gave a better result. A combination of the primary and secondary model was used for shelf-life prediction under dynamic conditions. This combination showed the best prediction of microbial counts using the modified Logistic model and the Arrhenius equation.
With the developed model, it is possible to predict the shelf life of MAP cooked sliced ham based on the growth of LAB under different temperature conditions.
The developed model can be used to calculate the remaining shelf life in different steps of the chain. Thus, it can deliver an important contribution to improve food quality by optimizing the storage management.
开发一种预测模型，以确定冷链中每个步骤中改良气氛包装（MAP）熟切片火腿的保质期。
研究了在 2 至 15 摄氏度的不同恒温条件下储存的 MAP 熟切片火腿中乳酸菌（LAB）的生长情况，总活菌数的发展以及感官和 pH 值参数的变化。测量结果表明，保质期结束可以视为 LAB 达到超过 7 log(10) CFU g(-1)的时间。测试并分析了不同的主模型和次模型，以找到计算保质期的最佳方法。对于主模型，比较了修正的 Gompertz 函数和修正的 Logistic 函数。两种模型之间没有实质性差异。使用 Arrhenius 和平方根模型对温度对生长速率的影响进行建模，而 Arrhenius 方程的结果更好。使用主模型和次模型的组合进行动态条件下的保质期预测。这种组合使用修正的 Logistic 模型和 Arrhenius 方程对微生物计数进行了最佳预测。
使用开发的模型，可以根据不同温度条件下 LAB 的生长情况预测 MAP 熟切片火腿的保质期。
开发的模型可用于计算链中不同步骤的剩余保质期。因此，通过优化储存管理，可以为提高食品质量做出重要贡献。
