Performance of Response Surface and Davey Model for Prediction of Staphylococcus aureus Growth Parameters under Different Experimental Conditions

Abstract

The combined effect of different temperatures (7 to 19 degrees C), pH levels (4.5 to 8.5), sodium chloride levels (0 to 8%), and sodium nitrite levels (0 to 200 ppm) on the predicted growth rate and lag time of Staphylococcus aureus under aerobic and anaerobic conditions was studied. The two predictive models developed, response surface (RS) and the Davey model, provided reliable estimates of the two kinetic parameters studied. The RS provided better predictions of maximum specific growth rate, with bias factors of 1.06 and 1.31 and accuracy factors of 1.17 and 1.37, respectively, in aerobic and anaerobic conditions. The Davey model performed more accurately for lag time, with a bias factor of 1.12 and an accuracy factor of 1.49, for both aerobic and anaerobic conditions. Predictive growth models are a valuable tool, enabling swift determination of Staphylococcus aureus growth rate and lag time. These data are essential for ensuring staphylococcus-related quality and safety of food products.

Performance of response surface model for prediction of Leuconostoc mesenteroides growth parameters under different experimental conditions

“…They also show the statistical factors that indicate the average deviations between observed and predicted values for each of the models, in aerobic and anaerobic conditions. In both cases, the polynomial equations produced a high value for the multiple regression coefficient (R 2 ) and a low value for the RMSE statistic, which indicates a good fit of the experimental data, and better values than those observed in other scientific studies (García-Gimeno et al, 2003;Juneja, Eblen, & Marks, 2001;Lou & Nakai, 2001;Zurera-Cosano et al, 2004). The standard error of prediction, SEP, produced low values for the three parameters, less than 11% in aerobic conditions and 16% in anaerobic conditions, thus confirming the concordance between the observed and predicted values (Tables 1 and 2).…”

Unveiling Staphylococcus aureus enterotoxin production in dairy products: a review of recent advances to face new challenges

“…In the future, a better knowledge of the mechanisms involved in the inhibition of S. aureus and SE production will hopefully provide sufficient data to implement predictive models. The evolution in the population of S. aureus (lag, growth, survival and death) and the effect of food-related parameters (a w and pH) and process or storage conditions (temperature, atmosphere) have been modelled in mathematical predictive models derived from experimental data on microbial populations Stewart et al 2002;Zurera-Cosano et al 2004). Some modelling studies have taken account of the dairy environment and notably of the interactions with LAB (Lindqvist et al 2002).…”

Dynamics of staphylococcal enterotoxin production in model experiments simulating the fresh cheese environment

“…This program is based on experimental results obtained in culture media such as Brain Heart Infusion Broth (Buchanan et al 1993) and should predict the characteristics of bacterial growth in food. Several kinetic predictive growth models of S. aureus can be found in related literature (Sutherland et al 1994;Zurera-Cosano et al 2004).…”