Development and validation of a tertiary simulation model for predicting the growth of the food microorganisms under dynamic and static temperature conditions

The use of predictive modeling software may markedly contribute to the better understanding of the microbial behavior in foods. In this paper, the development and validation of a tertiary model, which provides predictions of microbial growth in foods under dynamic or static temperature conditions, is presented. In particular, the UGPM (Unified Growth Prediction Model) software applies the Baranyi and Roberts (1994) primary model, coupled to a secondary temperature model, in order to simulate growth of a given microorganism during storage of a specific food or food category. The software, intended to be used by both expert and non-expert users, may be a valuable decision support tool for the food industry, by assisting in the management of foods based on their actual shelf-life and microbial safety, thereby limiting the deterministic “best-by” practice for the determination of shelf-life. The latter is commonly based solely on empirical observations and has high uncertainty. This in turn, may result in the rejection of large quantities of unspoiled or safe foods, or even in the distribution of spoiled and unsafe foods, due to ignorance of the effect of temperature abuse on the microbial spoilage and safety of products.

Research highlights
► Predictive modeling tool assisting food industries in decision making for the safety and quality of foods.
► Use in combination with traceability and temperature-monitoring systems in the chill-chain.
► Rapid assessment of the impact of temperature history on the freshness and/or safety of foods.
► Updatable by non-expert users by providing new products info and microbial growth parameters.