Neural network model for growth of Salmonella Typhimurium in brain heart infusion broth

Abstract

SummaryModels that predict growth of Salmonella as a function of variables in the current and previous environment are valuable tools for assessing the safety of food. Therefore, this study was undertaken to develop a model for growth of Salmonella Typhimurium in brain heart infusion broth as a function of previous pH (5.7–8.6), temperature (15–40 °C), pH (5.2–7.4) and time. Viable count data (log CFU mL−1) were modelled using a neural network approach. The variable impacts were 2.4% for previous pH, 29.0% for temperature, 4.9% for pH and 63.7% for time. The proportion of residuals in an acceptable prediction zone (pAPZ) from ‐1 (fail‐safe) to 0.5 log CFU mL−1 (fail‐dangerous) was 0.965 (1061/1100) for dependent data and 0.939 (386/411) for independent data for interpolation. A pAPZ ≥ 0.7 indicated that the model provided predictions with acceptable accuracy and bias. Thus, the model was successfully validated.