Optimal characterization of thermal microbial inactivation simulating non-isothermal processes

In this paper, optimal experimental design is applied for the characterization of the microbial inactivation of Listeria monocytogenes under non-isothermal conditions, modelled by the Bigelow model. These conditions simulate industrial processes, where different temperature profiles can be applied for food processing. Here, the aim was to find the best time nodes for measurements given a fixed number of observations and a final time process. Results show that, compared to a “classic” uniform time distribution for the observations, the optimal distribution of observation times results in a more accurate description of the response of the microbial population. The OED resulted in a reduction in the relative standard errors for the estimated D and z-values of 25% and 80%, respectively. Prediction intervals of the microbial counts were calculated based on the parameters estimated with both designs (i.e., uniform and optimal design). The prediction interval generated using the parameters estimated from the optimal design is notably narrower than the one obtained when the “uniform” experiment is considered, thus providing a more accurate description of the thermal resistance of the microorganism. Although the Bigelow model has been used in this particular case, the functions developed have been wrapped in an R package (bioOED), which is freely available and can be used for any other type of microorganism and/or inactivation model. [Display omitted] •Optimal Experiment Design has been applied to dynamic microbial inactivation.•The Optimal Experiment results in a more accurate characterization of the microorganism.•The functions implemented are freely available as an R package (bioOED).