<p>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.</p> <p>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.</p> <p>A non-linear ordinary differential equation was used to describe the inactivation curves with the hypothesis that the momentary inactivation rate is not constant and depends on the instantaneous processing conditions. The inactivation rate was related to key process parameters to describe the inactivation kinetics under more realistic processing conditions. From the solution of the non-linear ordinary differential equation and the optimization algorithm, safety inferences in the microbial response can be retrieved, such as the critical lethal variable that increases microbial inactivation. For example, for nonisothermal processes such as microwave heating, time-temperature profiles were modeled and incorporated into the inactivation rate equation. The critical temperature required to increase the microbial inactivation was obtained from the optimization analysis. For non-thermal processes, such as cold plasma, the time-varying concentration of reactive gas species was incorporated into the inactivation rate equation. The approach allowed the estimation of the critical gas concentration above which microbial inactivation becomes effective. For Pulsed Electric Fields (PEF), the energy density is the integral parameter that groups the wide range of parameters of the PEF process, such as the electric field strength, the treatment time and the electrical conductivity of the sample. The literature has shown that all of these parameters impact microbial inactivation. It has been hyphothesized that the inactivation rate is a function of the energy density and that above a threshold value significant microbial inactivation begins. </p> <p>The differential equation was solved numerically using the Runge-Kutta method (<i>ode45</i> in MATLAB ®). The<i> lsqcurvefit</i> function in MATLAB ® estimated the kinetic parameters. The approach to model microbial inactivation, whether when samples were subjected to nonisothermal or to non-thermal food processes, was validated using data published in the literature and/or in other samples and treatment conditions. The modeling approaches developed by this dissertation are expected to assist the food industry in the development and validation process to achieve the level of microbial reduction required by regulatory agencies. In addition, it is expected to assist the food industry in managing food safety systems through support food safety decision-making, such as the designation of the minimal critical parameter that may increase microbial inactivation. Finally, this dissertation will contribute in depth to the field of food safety and engineering, with the ultimate outcome of having a broad and highly positive impact on human health by ensuring the consumption of safe food products.</p>