On the use of in-silico simulations to support experimental design: A case study in microbial inactivation of foods

Abstract

The mathematical models used in predictive microbiology contain parameters that must be estimated based on experimental data. Due to experimental uncertainty and variability, they cannot be known exactly and must be reported with a measure of uncertainty (usually a standard deviation). In order to increase precision (i.e. reduce the standard deviation), it is usual to add extra sampling points. However, recent studies have shown that precision can also be increased without adding extra sampling points by using Optimal Experiment Design, which applies optimization and information theory to identify the most informative experiment under a set of constraints. Nevertheless, to date, there has been scarce contributions to know a priori whether an experimental design is likely to provide the desired precision in the parameter estimates. In this article, two complementary methodologies to predict the parameter precision for a given experimental design are proposed. Both approaches are based on in silico simulations, so they can be performed before any experimental work. The first one applies Monte Carlo simulations to estimate the standard deviation of the model parameters, whereas the second one applies the properties of the Fisher Information Matrix to estimate the volume of the confidence ellipsoids. The application of these methods to a case study of dynamic microbial inactivation, showing how they can be used to compare experimental designs and assess their precision, is illustrated. The results show that, as expected, the optimal experimental design is more accurate than the uniform design with the same number of data points. Furthermore, it is demonstrated that, for some heating profiles, the uniform design does not ensure that a higher number of sampling points increases precision. Therefore, optimal experimental designs are highly recommended in predictive microbiology.

An experimental design tool to optimize inference precision in data-driven mathematical models of bacterial infections in vivo

“…For example, despite the sustained wide application of the ITS technique, questions around optimizing experimental design and resource allocation have remained formally unaddressed, with the exception of past studies having used small-scale simulation-based approaches to determine a suitable inoculum size [4] or the distribution of ITS per host [4,12]. Limited examples of efforts to develop experimental design tools for population-level data in dynamical systems can be found in other microbiological fields like systems biology [38][39][40][41][42][43], ecology [44][45][46] and food science [47,48].…”

Multiobjective Optimization of a Frying Process Balancing Acrylamide Formation and Quality: Solution Analysis and Uncertainty Propagation

“…The Monte-Carlo method can be used to study the propagation of uncertainty in food engineering. Garre et al used this methodology in microbial inactivation of foods to select optimal experiment designs [ 33 ]. In this work, we analyzed the effects of parameter uncertainty in mathematical models describing food processes over the robustness of the Pareto set of solutions in multi-objective optimization using as a case study a frying process of potato chips where a quality parameter (yellowness) and the production of acrylamide, a potential carcinogen [ 34 , 35 ] were defined as opposed objectives.…”

Advanced mathematical modelling tools for food processing and food safety assessment

“…It is applied for three different inactivation models commonly used in food science (Bigelow, Mafart and Peleg) and three different model microorganisms (Escherichia coli, Salmonella Senftemberg and Bacillus coagulans). Functions for applying this methodology have been included in the bioOEDR package (Garre, Penalver, Fernandez, & Egea, 2017), making them available for the scientific community. This package is available on CRAN (https://CRAN.R-project.org/package=bioOED).…”