Abstract
In this study, the methodology of optimal experiment design for parameter estimation is exploited in the field of predictive microbiology. Optimal estimates for the Square Root model parameters-modeling microbial growth in the sub-optimal growth temperature range-are obtained by practical implementation of optimal (3-step) temperature input profiles. Due to the model non-linearity, the effectiveness of the experiment design will be determined by the selection of nominal parameter values mimicking the true parameter vector p*. Convergence to p* is commonly not guaranteed after a first optimal experiment design. Here, the latter problem is overcome by an iterative design procedure.
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