29E-22 |
A new model to predict the inactivation and growth of microorganisms taking into account previous processing history |
C. M. CORVALAN, Dept. of Food Science, Purdue Univ., 745 Agriculture Mall Dr., West Lafayette, IN 47907-2009, O. H. Campanella, Dept. of Agricultural & Biological Engineering, Purdue Univ., 1146 Agricultural & Biological Engineering Bldg., West Lafayette, IN 47907-1146, and T. A. Haley, Food Safety & Regulatory Compliance, Bush Brothers & Co., 1016 E. Weisgarber Rd., Knoxville, TN 37909-2683. Despite remarkable progresses, the field of predictive microbiology is still an emerging one. This is in part due to the fact that metabolic changes, notably accumulated damage or adaptation, which the microorganism may experience during processing, are not taken into account. Most predictive models assume that the future evolution of the microbial population is determined solely by the present state, that is, the current microbial population and the current kinetic constant which is a function of the processing variables (e.g. temperature) at the current state. Concerning temperature, there is significant experimental evidence that the evolution of microbial population can be affected not only by the momentary temperature but also by the temperature history. This research attempts to address that issue and it is aimed to the development of a model that incorporates the effect of the previous temperature history. Thus, a model to predict the inactivation kinetics of microorganisms when the rate of inactivation depends on both the current temperature and the previously applied thermal stresses is developed. It is shown that for isothermal processes the model reduces to a model, the Weibull model, that has been recently used to describe linear and non-linear survival kinetics of microorganisms during inactivation. For process with changing temperature, standard integration techniques traditionally used for calculating sterilization process are also applied with the newly proposed model. Examples showing the suitability of the model to predict microbial inactivation with constant and variable heating profiles are presented. The model can be also extended to predict microbial growth, thus leading to new insights into the field of predictive microbiology with a new appreciation of the importance of the effects of the microbial population treatment 'history'.
Session 29E, Food Engineering: Transport processes and kinetics
|