On the use of in-silico simulations to support experimental design: A case study in microbial inactivation of foods
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AuthorGarre Pérez, Alberto; Peñalver Soto, José Lucas; Esnoz Nicuesa, Arturo; Iguaz Gainza, Asunción; Fernández Escámez, Pablo Salvador; [et al.]
Knowledge AreaTecnología de los Alimentos
SponsorsThe financial support of this research work was provided by the Ministry of Science, Innovation and Universities of the Spanish Government and European Regional Development Fund (ERDF) through project AGL2017-86840-C2-1-R, as well as the Seneca Foundation through project (20900/PD/18). AG is grateful to the MINECO for awarding him a pre-doctoral grant (Ref: BES-2014-070946). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
PublisherEd. Public Library of Science (PLoS)
Bibliographic CitationGarre A, Peñalver-Soto JL, Esnoz A, Iguaz A, Fernandez PS, Egea JA (2019) On the use of insilico simulations to support experimental design: A case study in microbial inactivation of foods. PLoS ONE 14(8): e0220683. https://doi.org/ 10.1371/journal.pone.0220683
KeywordsTecnología de los alimentos
Monte Carlo Method
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 precisión 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 ...
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