PREDICTIVE MODEL WITH IMPROVED STATISTICAL-ANALYSIS OF INTERACTIVE FACTORS AFFECTING THE GROWTH OF STAPHYLOCOCCUS-AUREUS 196E

The growth of pathogenic bacteria in foods is affected by several fact ors which may interact to enhance or inhibit microbial growth. A model to predict the growth of Staphylococcus aureus 196E in microbiologica l 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, adjuste d to pH 5.0, 6.0, or 7.0, and incubated aerobically at 12, 20, or 28 d egrees C. Several interactive relationships between time, temperature, pH, and NaCl concentration were significant. The model adequately pre dicted the growth of S. aureus 196E, Predicted responses to multiple-f actor interactions were displayed with three-dimensional and contour p lots. A second model developed from a smaller subset of the growth dat a demonstrated that models could be produced with much less data colle ction. This methodology can provide important information to food scie ntists about the growth kinetics of microorganisms and prediction rang es or confidence intervals for growth parameters. Consequently, the ef fects of food formulations and storage conditions on the growth kineti cs of foodborne pathogens or spoilage microorganisms could be predicte d.