Toward predictive food process models: A protocol for parameter estimation.
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 req...
| Publicado en: | Critical Reviews in Food Science & Nutrition Vol. 58; no. 3; pp. 436 - 450 |
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| Autores principales: | , , , , |
| Formato: | review Journal Article |
| Publicado: |
Taylor & Francis Ltd
2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=127588324&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 127588324 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10408398 1B0 jtl: Critical Reviews in Food Science & Nutrition issn: 10408398 maglogo: Y pubinfo: dt: 2018 vid: 58 iid: 3 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 127588324 127588324 NLM27246577 127588324 10.1080/10408398.2016.1186591 NLM27246577 127588324 ppf: 436 ppct: 14 formats: tig: atl: Toward predictive food process models: A protocol for parameter estimation. aug: au: Vilas, Carlos Arias-Méndez, Ana García, Míriam R. Alonso, Antonio A. Balsa-Canto, E. affil: Bioprocess Engineering Group. IIM-CSIC, Vigo, Spain sug: subj: Food Handling Methods Models, Theoretical Study Design Scales ab: 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. pubtype: Academic Journal doctype: review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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