Comparing design of experiments and optimal experimental design techniques for modelling the microbial growth rate under static environmental conditions. (December 2018)

• Record Type: Journal Article
• Authors: Akkermans, Simen; Nimmegeers, Philippe; Van Impe, Jan F.
• Is Part Of: Food microbiology. Volume 76(2018)
• Page Start: 504
• Page End: 512
• Publication Date: 2018-12
• Subjects: Predictive microbiology -- Experimental design -- D-criterion -- Model prediction uncertainty -- Secondary model; Food Microbiology -- Periodicals; Aliments -- Microbiologie -- Périodiques; Food -- Microbiology; Periodicals; Food -- Microbiology -- Periodicals; Food contamination -- Periodicals; 664.001579
• DOI: 10.1016/j.fm.2018.05.010
• Languages: English
• ISSNs: 0740-0020

Abstract

Abstract: Building secondary models that describe the growth rate as a function of multiple environmental conditions is often very labour intensive and costly. As such, the current research aims to assist in decreasing the required experimental effort by studying the efficacy of both design of experiments (DOE) and optimal experimental designs (OED) techniques. This is the first research in predictive microbiology (i) to make a comparison of these techniques based on the (relative) model prediction uncertainty of the obtained models and (ii) to compare OED criteria for the design of experiments with static (instead of dynamic) environmental conditions. A comparison of the DOE techniques demonstrated that the inscribed central composite design and full factorial design were most suitable. Five conventional and two tailor made OED criteria were tested. The commonly used D-criterion performed best out of the conventional designs and almost equally well as the best of the dedicated criteria. Moreover, the modelling results of the D-criterion were less dependent on the experimental variability and differences in the microbial response than the two selected DOE techniques. Finally, it was proven that solving the optimisation of the D-criterion can be made more efficient by considering the sensitivities of the growth rate relative to its value as Jacobian matrix instead of the sensitivities of the cell density measurements. Highlights: Comparing experimental designs for building models for the microbial growth rate. Comparison based on the obtained model prediction uncertainty. Mathematically optimised experimental designs for static experiments. The optimised design was twice as efficient as a full factorial design. Efficient method for calculating mathematically optimised designs.