Purpose: The objective of this study was to develop software program to predict bacterial growth, to calculate growth probability, and to validate developed models with experimental data.
Methods: Foodborne bacteria Animal product Modeling Equipment (FAME) was programmed with Java script and Html programming languages. Kinetic models developed with the modified Gompertz model with 5,400 samples of experimental data (packaging condition × temperature × NaCl × NaNO2) on frankfurters for Pseudomonas spp., Listeria monocytogenes, and Salmonella were loaded in FAME, and probabilistic models developed with 345,600 samples of experimental data for L. monocytogenes, Staphylococcus aureus, and Salmonella for combinations of the fixed effects were also loaded in FAME. In addition, validation function was added in the software.
Results: Using FAME, cell counts of foodborne bacteria at various conditions (packaging condition, storage temperature, NaCl concentration, and NaNO2 residual) can be calculated in part of kinetic model, and growth probability (P = 0.1, 0.5, and 0.9) of foodborne bacteria can also be estimated by probabilistic models. At the same time, automatic validation function by calculating bias factor, accuracy factor, and root mean square error can be processed with experimental data. In addition, there is a function which users can load and edit own equations.
Significance: FAME should be useful in predicting foodborne pathogen growth and growth probability, especially for non-specialists in predictive models.