Modeling Microbial Inactivation Subjected to Nonisothermal and Non-Thermal Food Processing Technologies
Abstract
Modeling microbial inactivation has a great influence on the optimization, control and design of food processes. In the area of food safety, modeling is a valuable tool for characterizing survival curves and for supporting food safety decisions. The modeling of microbial behavior is based on the premise that the response of the microbial population to the environment factors is reproducible. And that from the past, it is possible to predict how these microorganisms would respond in other similar environments. Thus, the use of mathematical models has become an attractive and relevant tool in the food industry. This research provides tools to relate the inactivation of microorganisms of public health importance with processing conditions used in nonisothermal and non-thermal food processing technologies. Current models employ simple approaches that do not capture the realistic behavior of microbial inactivation. This oversight brings a number of fundamental and practical issues, such as excessive or insufficient processing, which can result in quality problems (when foods are over-processed) or safety problems (when foods are under-processed). Given these issues, there is an urgent need to develop reliable models that accurately describe the inactivation of dangerous microbial cells under more realistic processing conditions and that take into account the variability on microbial population, for instance their resistance to lethal agents. To address this urgency, this dissertation focused on mathematical models, combined mathematical tools with microbiological science to develop models that, by resembling realistic and practical processing conditions, can provide a better estimation of the efficacy of food processes. The objective of the approach is to relate the processing conditions to microbial inactivation. The development of the modeling approach went through all the phases of a modeling cycle from planning, data collection, formulation of the model approach according to the data analysis, and validation of the model under different conditions than those that the approach was developed.
Degree
Ph.D.
Advisors
Campanella, Purdue University.
Subject Area
Marketing
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