Predictive modeling for optimal chicken breast cooking across diverse methods and temperatures

The expansion of food service and rising consumer concerns about wellness are driving efforts to optimize cooking processes. Currently, professional ovens rely solely on temperature probes to monitor cooking and ensure safety, but these probes lack precision in positioning. This study aimed to develop a mathematical model to predict the optimal cooking time for chicken breast by matching sensory quality with safety requirements. Three different oven cooking methods at three different temperatures were considered (grill, T = 240, 260, 280 °C; forced convection, T = 150, 170, 190 °C; sous vide, T = 80, 95, 120 °C, RH = 100%) and evolution of quality indices (cooking loss, color and texture) were monitored over the cooking process, together with safety requirements. Activation energies (Ea) were computed thanks to data kinetic modeling. Predictive models based on Ea of the most sensitive quality index (i.e cooking loss) of chicken breast cooking were developed and validated. The optimal cooking time was predicted as a function of cooking loss evolution and temperature. The employment of an online sensor, i.e. a balance, inside the oven, to monitor changes in the reference quality indicator could enhance the control of the cooking process and improve food service equipment.