<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>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/9939488 |
| Date | 17 October 2019 |
| Creators | Gabriella Mendes Candido De Oliveira (7451486) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/Modeling_Microbial_Inactivation_Subjected_to_Nonisothermal_and_Non-thermal_Food_Processing_Technologies/9939488 |
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