Purpose: To develop a predictive model and software application that will estimate the growth of Listeria monocytogenes in Ready-to-Eat meats with different formulations and anti-microbial concentrations.
Methods: 18 experimental data sets describing microbial growth with different levels of moisture, NaCl, pH, and the antimicrobial e(Lm)inate LAD were used to develop the model, including controls. In experiments where growth was observed the Baranyi Roberts model was used, and a linear model was used where inactivation due to the use of antimicrobial was observed. Secondary modelling to examine the influence of formulation parameters involved the use of Locally Weighted Polynomial Regression (LOESS), and the final results were integrated into a web-based software application.
Results: The LOESS method enabled the growth rate and lag time to be modelled simultaneously as functions of moisture, NaCl, pH and e(Lm)inate LAD concentration. Percentage accuracy and bias factors to assess model performance ranged from 0.01 - 4% for the growth rate and lag time, indicating good agreement with experimental data. The full range of experimental conditions was used as options in the final software application.
Significance: The work demonstrates the utility of predictive microbiology specifically for antimicrobial use in foods. By housing the model in a web-based software application, a food manufacturer can quickly assess the impact of different product formulations before carrying out any experimental work, saving on time and money and increasing product safety.