An overall concept for a generic model to predict the remaining shelf life of meat in different steps of the supply chain was developed. It consists of three models: a shelf life model, an inter-organisational cold chain model and a temperature mapping model that includes a heat transfer model. In this concept, shelf life is predicted based on the growth of Pseudomonas sp., taking into account organisational structure, inspection scheme, technical circumstances and temperature conditions in different supply chains. Whereas the shelf life model is almost complete, further work is required to develop the two other models.

Aims:  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. Methods and Results:  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°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 log10 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. Conclusions:  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. Significance and Impact of the Study:  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.