Optimal experimental design for discriminating between microbial growth models as function of suboptimal temperature

• Optimal experiment design is used to discriminate between dynamic microbial growth models.
• Optimal dynamic temperature profiles are computed based on two discrimination criteria.
• To have the same experimental load an equal number of experiments is imposed in all cases.
• Discrimination between CTMI and aCTMI models at suboptimal temperatures is realized in silico.
• The criterion by Schwaab performs best when the real model’s parameters have to be estimated.

In the field of predictive microbiology, mathematical models play an important role for describing microbial growth, survival and inactivation. Often different models are available for describing the microbial dynamics in a similar way. However, the model that describes the system in the best way is desired. Optimal experimental design for model discrimination (OED-MD) is an efficient tool for discriminating among rival models.In this work the T12T12-criterion proposed by Atkinson and Fedorov (1975) [1] and applied efficiently by Ucinski and Bogacka (2005) [2] and the Schwaab-approach proposed by Schwaab et al. (2008) [3] and Donckels et al. (2009) [4] will be applied for discriminating among rival models for the microbial growth rate as a function of temperature. The two methods will be tested in silico and their performances will be compared.Results from a simulation study indicate that it is possible to validate the case that one of the proposed models is more accurate for describing the temperature effect on the microbial growth rate. Both methods are able to design inputs with a sufficient discrimination potential. However, it has been observed that the Schwaab-approach provides inputs with a higher discrimination potential in combination with more accurate parameter estimates.