Demonstration of Inappropriate Validation Method for a Cracker Baking Process Using Predictive Modeling.

Area of Science:

• Food Microbiology
• Food Process Engineering
• Predictive Modeling

Background:

• Baking process validation for Salmonella inactivation is complex due to simultaneous heating and drying.
• Existing predictive models often rely on isothermal and single-moisture data, which may not accurately reflect dynamic baking conditions.

Purpose of the Study:

• To quantitatively evaluate a predictive modeling approach for baking processes using isothermal and single-moisture inactivation data.
• To assess the suitability of a previously disseminated model for validating Salmonella inactivation in a dynamic baking environment.

Main Methods:

• Formulated a cracker dough inoculated with a five-strain Salmonella cocktail.
• Conducted isothermal inactivation experiments at 56, 60, and 63°C to determine Salmonella kinetics (D60°C = 4.6 min, z = 4.9°C).
• Performed baking experiments in a convection oven at 177°C, measuring Salmonella survivors and product temperature/moisture profiles dynamically.

Main Results:

• Isothermal data yielded D and z values, used to predict inactivation under dynamic baking conditions.
• Baking experiments achieved an average 5-log reduction of Salmonella by 150 seconds.
• Dough-based models, using only isothermal data, overpredicted Salmonella lethality by over 100-log reductions at 150 seconds.

Conclusions:

• Single-moisture-based predictive models are inappropriate for processes with dynamic temperature and moisture, leading to fail-dangerous overestimations.
• Model-based validation of preventive controls must incorporate dynamic moisture/water activity (a_w) data.
• End-users should exercise caution with unvalidated predictive models for process validation.