In predictive food microbiology, cardinal parameter models are often applied to describe the effect of
temperature, pH and/or water activity on the microbial growth rate. To identify the model parameters,
full factorial designs are often used, in spite of the high experimental burden and cost related to this
method.
In this work, the impact of the selected experimental scheme on the estimation of the parameters of
the cardinal model describing the effect of temperature, pH and/or water activity has been evaluated. In
a first step, identification of a simple model describing only the effect of temperature, pH or water
activity was considered. The comparison of an equidistant design and a D-optimal (based) design showed
that the latter, which is based on the model’s sensitivity functions, yields more realistic parameter
estimates than the typical equidistant design. By selecting the experimental levels based on the sensi-
tivity functions, a more realistic description of the behavior around optimal conditions can be obtained.
In the second step, focus was on the efficient and accurate estimation of the ten parameters of the
extended cardinal model that describes the combined effect of temperature, pH and water activity on the
microbial growth rate. Again, equidistant level selection is compared to a D-optimal (based) experi-
mental design. In addition, a full factorial and a Latin-square approach are evaluated. From the simulation
case studies presented, it can be stated that all parameters can be equally well defined from an equi-
distant design as from a D-optimal-based design. In addition, reducing the experimental load by con-
structing a Latin-square design does not hamper the parameter estimation procedure. This work
confirms the observation of a previous study, i.e., for complex cases a Latin-square design is an attractive
alternative for a full factorial design as it yields equally accurate and reliable parameter estimates while
reducing the experimental workload.
In predictive food microbiology, cardinal parameter models are often applied to describe the effect of
temperature, pH and/or water activity on the microbial growth rate. To identify the model parameters,
full factorial designs are often used, in spite of the high experimental burden and cost related to this
method.
In this work, the impact of the selected experimental scheme on the estimation of the parameters of
the cardinal model describing the effect of temperature, pH and/or water activity has been evaluated. In
a first step, identification of a simple model describing only the effect of temperature, pH or water
activity was considered. The comparison of an equidistant design and a D-optimal (based) design showed
that the latter, which is based on the model’s sensitivity functions, yields more realistic parameter
estimates than the typical equidistant design. By selecting the experimental levels based on the sensi-
tivity functions, a more realistic description of the behavior around optimal conditions can be obtained.
In the second step, focus was on the efficient and accurate estimation of the ten parameters of the
extended cardinal model that describes the combined effect of temperature, pH and water activity on the
microbial growth rate. Again, equidistant level selection is compared to a D-optimal (based) experi-
mental design. In addition, a full factorial and a Latin-square approach are evaluated. From the simulation
case studies presented, it can be stated that all parameters can be equally well defined from an equi-
distant design as from a D-optimal-based design. In addition, reducing the experimental load by con-
structing a Latin-square design does not hamper the parameter estimation procedure. This work
confirms the observation of a previous study, i.e., for complex cases a Latin-square design is an attractive
alternative for a full factorial design as it yields equally accurate and reliable parameter estimates while
reducing the experimental workload.
การแปล กรุณารอสักครู่..
