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Toward predictive food process models: A protocol for parameter estimation.

Carlos Vilas1, Ana Arias-Méndez1, Míriam R García1

  • 1a Bioprocess Engineering Group. IIM-CSIC , Vigo , Spain.

Critical Reviews in Food Science and Nutrition
|June 2, 2016
PubMed
Summary
This summary is machine-generated.

Accurate food process modeling requires reliable parameter estimation. This study introduces a new protocol to overcome common challenges like identifiability and multimodality, improving model predictions for food product development.

Keywords:
Model identificationexperimental designfood process engineeringidentifiabilityparameter estimation

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Area of Science:

  • Food Science and Technology
  • Process Engineering
  • Mathematical Modeling

Background:

  • Physics-based mathematical models are crucial for food product and process design, optimization, and control.
  • Model predictive capabilities depend on accurate parameter values, which are often unknown.
  • Parameter estimation from experimental data is critical for reliable food process modeling.

Purpose of the Study:

  • To address challenges in parameter estimation for food process models.
  • To introduce a novel parameter identification protocol.
  • To improve the predictive accuracy of mathematical models in the food industry.

Main Methods:

  • Examination of common pitfalls in parameter estimation, including lack of identifiability and multimodality.
  • Presentation of the theoretical framework for a new parameter identification protocol.
  • Application and illustration of the protocol using thermal processing of packaged foods.

Main Results:

  • The proposed protocol effectively addresses identifiability and multimodality issues.
  • Demonstrated improvement in parameter estimation accuracy.
  • Enhanced predictive performance of physics-based food process models.

Conclusions:

  • The developed parameter identification protocol offers a robust solution for food process modeling.
  • Accurate parameter estimation is key to unlocking the full potential of mathematical models in food science.
  • This work contributes to more reliable design and control of food processing operations.