title: Toward predictive food process models: A protocol for parameter estimation.
abstract: Mathematical models, in particular, physics-based models, are essential tools to food product and process design, optimization and control. The success of mathematical models relies on their predictive capabilities. However, describing physical, chemical and biological changes in food processing requires the values of some, typically unknown, parameters. Therefore, parameter estimation from experimental data is critical to achieving desired model predictive properties. This work takes a new look into the parameter estimation (or identification) problem in food process modeling. First, we examine common pitfalls such as lack of identifiability and multimodality. Second, we present the theoretical background of a parameter identification protocol intended to deal with those challenges. And, to finish, we illustrate the performance of the proposed protocol with an example related to the thermal processing of packaged foods.
authors.affiliation: a Bioprocess Engineering Group. IIM-CSIC , Vigo , Spain.
authors.forename: Carlos
authors.identifier: http://orcid.org/0000-0001-6100-6252
authors.initials: C
authors.lastname: Vilas
authors.affiliation: a Bioprocess Engineering Group. IIM-CSIC , Vigo , Spain.
authors.forename: Ana
authors.identifier: http://orcid.org/0000-0003-3546-6591
authors.initials: A
authors.lastname: Arias-Méndez
authors.affiliation: a Bioprocess Engineering Group. IIM-CSIC , Vigo , Spain.
authors.forename: Míriam R
authors.identifier: http://orcid.org/0000-0001-9538-6388
authors.initials: MR
authors.lastname: García
authors.affiliation: a Bioprocess Engineering Group. IIM-CSIC , Vigo , Spain.
authors.forename: Antonio A
authors.initials: AA
authors.lastname: Alonso
authors.affiliation: a Bioprocess Engineering Group. IIM-CSIC , Vigo , Spain.
authors.forename: E
authors.identifier: http://orcid.org/0000-0002-1978-2626
authors.initials: E
authors.lastname: Balsa-Canto
mesh_terms: D005511:Food Handling / Q000379:methods*; D006801:Humans; D008962:Models, Theoretical; D012107:Research Design
keywords: Model identification; experimental design; food process engineering; identifiability; parameter estimation