Predicting Bacillus coagulans spores inactivation in tomato pulp under nonisothermal heat treatments.

UNLABELLED: The knowledge and understanding of Bacillus coagulans inactivation during a thermal treatment in tomato pulp, as well as the influence of temperature variation during thermal processes are essential for design, calculation, and optimization of the process. The aims of this work were to predict B. coagulans spores inactivation in tomato pulp under varying time-temperature profiles with Gompertz-inspired inactivation model and to validate the model's predictions by comparing the predicted values with experimental data. B. coagulans spores in pH 4.3 tomato pulp at 4 °Brix were sealed in capillary glass tubes and heated in thermostatically controlled circulating oil baths. Seven different nonisothermal profiles in the range from 95 to 105 °C were studied. Predicted inactivation kinetics showed similar behavior to experimentally observed inactivation curves when the samples were exposed to temperatures in the upper range of this study (99 to 105 °C). Profiles that resulted in less accurate predictions were those where the range of temperatures analyzed were comparatively lower (inactivation profiles starting at 95 °C). The link between fail prediction and both lower starting temperature and magnitude of the temperature shift suggests some chemical or biological mechanism at work. Statistical analysis showed that overall model predictions were acceptable, with bias factors from 0.781 to 1.012, and accuracy factors from 1.049 to 1.351, and confirm that the models used were adequate to estimate B. coagulans spores inactivation under fluctuating temperature conditions in the range from 95 to 105 °C. PRACTICAL APPLICATION: How can we estimate Bacillus coagulans inactivation during sudden temperature shifts in heat processing? This article provides a validated model that can be used to predict B. coagulans under changing temperature conditions. B. coagulans is a spore-forming bacillus that spoils acidified food products. The mathematical model developed here can be used to predict the spoilage risk following thermal process deviations for tomato products.