Comparing experimental design schemes in predictive food microbiology: Optimal parameter estimation of secondary models Original Research Article

In predictive food microbiology, full factorial designs are still more the rule than the exception, despite the huge experimental workload and cost related to this method. In this study, two simulation studies for secondary square-root-type models are performed to compare several experimental designs with respect to four criteria: (i) number of experiments, (ii) goodness-of-fit statistics with respect to the original model structure, and (iii) accuracy and (iv) uncertainty of the parameter estimates. In addition, the effect of data quality, quantified as the error related to plate count measurements, is assessed on the relation between model structure and experimental design. Full factorial, reduced full factorial, central composite, Latin-square and Box-Behnken designs are evaluated and compared to randomly selected datasets. As a guideline, a full factorial design should be preferred for rather simple model structures and a limited number of levels per environmental factor. For more complex cases, a Latin-square design is an attractive alternative as it does not require a priori model knowledge and provides relatively accurate and reliable parameter estimates while keeping the experimental efforts to a minimum.