The estimation of model parameters with high precision is of major importance in mathematical predictions. If a mathematical model is properly chosen and if the primary objective is to improve parameter estimation, underlying statistical theories can be applied. Precision increases with the number of experimental points. However, and in many situations, maximum precision is attained when sampling consists of replicates of specific experimental points. Experimental conditions can be optimized using the D-optimal design concept based on minimization of the generalized variance of the parameter estimates. The objective of this work was to use this methodology for the design of experiments for microbial inactivation processes described by a Gompertz-based model under isothermal and non-isothermal conditions. The application of D-optimal design concept considerably improved parameters precision, when compared to the commonly used heuristic designs.
- D-optimal experimental design
- Isothermal and non-isothermal conditions
- Maximum inactivation rate
- Microbial thermal inactivation
- Shoulder parameter