Bayesian modeling of Clostridium perfringens growth in beef-in-sauce products

Models on Clostridium perfringens growth which have been published to date have all been deterministic. A probabilistic model describing growth under non-isothermal conditions was thus proposed for predicting C. perfringens growth in beef-in-sauce products cooked and distributed in a French hospital. Model parameters were estimated from different types of data from various studies. A Bayesian approach was proposed to model the overall uncertainty regarding parameters and potential variability on the ‘work to be done’ (h0) during the germination, outgrowth and lag phase. Three models which differed according to their description of this parameter h0 were tested. The model with inter-curve variability on h0 was found to be the best one, on the basis of goodness-of-fit assessment and validation with literature data on results obtained under non-isothermal conditions. This model was used in two-dimensional Monte Carlo simulations to predict C. perfringens growth throughout the preparation of beef-in-sauce products, using temperature profiles recorded in a hospital kitchen. The median predicted growth was 7.8.10−2 log10 cfuċg−1 (95% credibility interval [2.4.10−2,0.8]) despite the fact that for more than 50% of the registered temperature profiles cooling steps were longer than those required by French regulations.