Predictive model with improved statistical analysis of interactive factors affecting the growth of Staphylococcus aureus 196E

Abstract

The growth of pathogenic bacteria in foods is affected by several factors which may interact to enhance or inhibit microbial growth. A model to predict the growth of Staphylococcus aureus 196E in microbiological media was developed using a modified Gompertz function and response- surface methodology. The predictive equation required the estimation of 23 parameters which describe singular and interactive effects of the growth factors studied. S. aureus 196E was inoculated into brain heart infusion broth formulated with either 0.5, 4.5, or 8.5% NaCl, adjusted to pH 5.0, 6.0, or 7.0, and incubated aerobically at 12, 20, or 28°C. Several interactive relationships between time, temperature, pH, and NaCl concentration were significant. The model adequately predicted the growth of S. aureus 196E. Predicted responses to multiple-factor interactions were displayed with three-dimensional and contour plots. A second model developed from a smaller subset of the growth data demonstrated that models could be produced with much less data collection. This methodology can provide important information to food scientists about the growth kinetics of microorganisms and prediction ranges or confidence intervals for growth parameters. Consequently, the effects of food formulations and storage conditions on the growth kinetics of foodborne pathogens or spoilage microorganisms could be predicted.