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' (h(0)) during the germination, outgrowth and lag phase. Three models which differed according to their description of this parameter h(0) were tested. The model with inter-curve variability on h(0) 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) log(10) cfu·g(-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.

Modeling of Clostridium perfringens vegetative cell inactivation in beef-in-sauce products: a meta-analysis using mixed linear models.

The aim of the present study was to predict Clostridium perfringens vegetative cell inactivation during the final reheating step of two beef-in-sauce products prepared and distributed in a French hospital for exposure in risk assessment. In order to account for variability according to experts and international organization recommendations, published data were used to estimate the thermal inactivation parameters of a probabilistic model. Mixed effects models were proposed to describe variability on D(ref) the decimal reduction time at temperature T(ref). Many models differing by their description of variability on D(ref) were tested. Based on goodness-of-fit and parsimony of the model, the one including three random effects was chosen. That model describes random effects of vegetative cell culture conditions, strains and other uncontrolled experimental factors. In order to check the ability of the model to predict inactivation under dynamic thermal conditions, model validation was carried out on published non isothermal data. This model was then used to predict C. perfringens vegetative cell inactivation using temperature profiles inside beef-in-sauce products registered in a French hospital and to explore control measures easier to apply than French regulations.