Untitled Document
Monday, September 25, 2017
DREAM - Design and development of REAlistic food Models with well-characterised micro- and macro-structure and composition


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WP0m WP1m WP2m WP3m WP4m WP5m WP6m WP7m WP8m

WP1 — Mathematical knowledge integration for food model numeric simulation


Leader: Nathalie Perrot

Contact details:

Address: Institut National de la Recherche Agronomique (INRA)
Country: France
Email: nathalie.perrot@grignon.inra.fr
Phone: +33 1 30 81 53 79

Activity type: RTD
Participants short name: INRA, CNRS, WUR

• a generic structure for integrating knowledge that is applicable to all the objects under study;
• mathematical reconstruction and modelling of the complex systems including validation;
• reverse-engineering testing of the sequence of actions delivering relevant and robust structures determined by the global requirements targets.

Role of participants and methodology:
INRA (WP leader): expertise in food modelling and food control, knowledge integration applied to food processes: adapt the new mathematical concepts and algorithms developed by the mathematicians of CNRS as to propose validated, relevant food numerical models for DREAM.
CNRS (ISC-PIF): expertise in complex systems modelling using determinist or stochastic
mathematical approaches: develop and/or adapt original mathematical approaches and algorithms.
WUR: Expertise in modelling simultaneous reaction kinetics, mass and heat transfer in plant food processing.
Expertise with Bayesian belief network modelling. Mathematical modelling of the plant food GMFs.

Work breakdown:
Task T1.1: Generic structure for the modelling approach
Task T1.2: Construction of the numeric food model and uncertainty management
Task T1.3: Reverse engineering using the numeric food models built in T1.2.

D1.1: Cognitive map of technical knowledge of selected food models of WP2 and 4. M18.
D1.2: Validated concepts for strategy of integration of pieces of knowledge available for the food models. M24.
D1.3: Description of the validated mathematical implemented models for multistage dynamic reconstruction of food models and reverse engineering. M34.
D1.4: Final report on the results reached for the selected food models of WP1 and WP4 and the methodology to produce IKM in food science. M44.

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