A predictive shelf life model as a tool for the improvement of quality management in pork and poultry chains

The aim of the present study was the development of a common predictive shelf life model, which is applicable for fresh pork as well as fresh poultry meat. Based on the growth of Pseudomonas sp., the model was developed by combining the Gompertz model as primary and the Arrhenius model as secondary model. Relevant microbial growth parameters for fresh poultry (growth rate, time at which maximum growth rate is obtained = reversal point) were related to the corresponding parameters for fresh pork which enabled the development of a common shelf life model. The model predictions for Pseudomonas sp. growth as well as the shelf lives at dynamic temperature conditions were in good agreement with the observations for fresh pork and fresh poultry 'even if only short temperature abuses were performed (with a duration of less than 5% of the total storage time). Whereas for pork a slight overestimation of shelf life occurred (mean difference between observed and predicted shelf life: −2.7%), the shelf life times for poultry were mostly underestimated (mean difference: 11.1%).For the implementation in real meat chains, it is necessary to adapt the model to the specific product and supply chain characteristics. Then the model can be considered to be an effective tool (in combination with adequate temperature monitoring solutions) for the improvement of quality management within the meat supply and distribution chain. The predictive information of the model can be used in specific situations of decision making, for example by optimising the storage management from the FIFO concept (First In First Out) to the LSFO concept (Least Shelf life, First Out).

► Development of a common predictive shelf life model for different meat types. ► Shelf life prediction based on Pseudomonas sp. growth for both meat types. ► Relevant microbial growth parameters for pork related to parameters for poultry. ► Model predictions at dynamic temperatures in good agreement with observations. ► Reliable predictions also for short temperature abuses (<5% of total storage time).