Demonstration of Inappropriate Validation Method for a Cracker Baking Process Using Predictive Modeling
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EmailThe goal of this study was to quantitatively evaluate a previously disseminated approach to validating baking processes utilizing a predictive model developed using only isothermal and single-moisture inactivation data for the initially formulated dough.
Abstract
Validation of baking processes for the inactivation of Salmonella is complicated by the combined effects of product heating and drying. The goal of this study was to quantitatively evaluate a previously disseminated approach to validating baking processes utilizing a predictive model developed using only isothermal and single-moisture inactivation data for the initially formulated dough. A simple cracker dough was formulated using flour inoculated with a five-strain cocktail of Salmonella. Side-by-side isothermal and baking experiments were performed to estimate Salmonella inactivation kinetics and to quantify survivors in a dynamic environment, respectively. Isothermal, single-moisture inactivation experiments were performed with cracker dough (water activity, aw = 0.956 ± 0.002; moisture content = 0.50 ± 0.01 dry basis) at three temperatures (56, 60, or 63°C) with ≥6 time intervals. Baking experiments were performed in a convection oven at 177°C with samples pulled every 30 s up to 360 s, with an endpoint product aw (25°C) of 0.45. The Salmonella isothermal, single-moisture inactivation kinetics in cracker dough resulted in D60°C and z−values of 4.6 min and 4.9°C, respectively; this model was then integrated over the dynamic product temperature profiles from the baking experiments. In the baking experiments, an average of 5-log reductions of Salmonella was achieved by 150 s of treatment; however, >100-log reductions were predicted by the dough-based models at that time point. This fail-dangerous overestimation of Salmonella lethality in crackers explicitly demonstrated that single-level moisture-based prediction models are inappropriate for describing inactivation in a process with both dynamic temperature and moisture, and that model-based validations must incorporate moisture/aw. Furthermore, end-users should exercise caution when utilizing unvalidated models to validate preventive control processes.
