On the design of optimal dynamic experiments for parameter estimation of aRatkowsky-type growth kinetics at suboptimal temperatures

It is generally known that accurate model building, i.e., proper model stru cture selection and reliable parameter estimation, constitutes an essential matter in the field of predictive microbiology, in particular, when integr ating these predictive models in food safety systems. In this context, Vers yck et al. (1999) have introduced the methodology of optimal experimental d esign techniques for parameter estimation within the field. Optimal experim ental design focuses on the development of optimal input profiles such that the resulting rich (i.e., highly informative) experimental data enable uni que model parameter estimation. As a case study, Versyck ct al. (1999) [Ver syck, K., Bernaerts, K., Geeraerd, A.H., Van Impe, J.F., 1999. Introducing optimal experimental design in predictive modeling. a motivating example. I nt. J. Food Microbiol., 51(1), 39-51] have elaborated the estimation of Big elow inactivation kinetics parameters tin a numerical way). Opposed to the classic (static) experimental approach in predictive modelling. an optimal dynamic experimental setup is presented. In this paper, the methodology of optimal experimental design for parameter estimation is applied to obtain u ncorrelated estimates of the square roof model parameters [Ratkowshy, D.A., Olley, J., McMeckin, T.A., Ball, A., 1982, Relationship between temperatur e and growth rate: of bacterial cultures. J. Bacteriol. 149, 1-5] describin g the effect of suboptimal growth temperatures on the maximum specific grow th rate of microorganisms. These estimates are the direct result of fitting a primary growth model to cell density measurements as a function of time. Apart from the design of an optimal time-varying temperature profile based on a sensitivity study of the model output, an important contribution of t his publication is a first experimental, validation of this innovative dyna mic experimental approach for uncorrelated parameter identification. An opt imal step temperature profile, within the range of model validity and pract ical feasibility, is developed for Escherichia coli K12 and successfully ap plied in practice. The presented experimental validation result illustrates the large potential of the dynamic experimental approach in the context of uncorrelated parameter estimation. Based on the experimental validation re sult, additional remarks are formulated related to future research in the f ield of optimal experimental design. (C) 2000 Elsevier Science B.V. All rig hts reserved.