A meta-analysis accounting for sources of variability to estimate heat resistance reference parameters of bacteria using hierarchical Bayesian modeling: Estimation of D at 121.1 °C and pH 7, zT and zpH of Geobacillus stearothermophilus

Predicting microbial survival requires reference parameters for each micro-organism of concern. When data are abundant and publicly available, a meta-analysis is a useful approach for assessment of these parameters, which can be performed with hierarchical Bayesian modeling. Geobacillus stearothermophilus is a major agent of microbial spoilage of canned foods and is therefore a persistent problem in the food industry. The thermal inactivation parameters of G. stearothermophilus (Dref, i.e.the decimal reduction time D at the reference temperature 121.1 °C and pH 7.0, zT and zpH) were estimated from a large set of 430 D values mainly collected from scientific literature. Between-study variability hypotheses on the inactivation parameters Dref, zT and zpH were explored, using three different hierarchical Bayesian models. Parameter estimations were made using Bayesian inference and the models were compared with a graphical and a Bayesian criterion. Results show the necessity to account for random effects associated with between-study variability. Assuming variability on Dref, zT and zpH, the resulting distributions for Dref, zT and zpH led to a mean of 3.3 min for Dref (95% Credible Interval CI = [0.8; 9.6]), to a mean of 9.1 °C for zT (CI = [5.4; 13.1]) and to a mean of 4.3 pH units for zpH (CI = [2.9; 6.3]), in the range pH 3 to pH 7.5. Results are also given separating variability and uncertainty in these distributions, as well as adjusted parametric distributions to facilitate further use of these results in aqueous canned foods such as canned vegetables.

► Thermal inactivation parameters of Geobacillus stearothermophilus are estimated.
► Estimations come from a set of mainly published 430 decimal reduction times.
► Between-study variability was confirmed, using three Bayesian hierarchical models.
► Parametric probability distributions are proposed for Dref, zT and zpH.