A dynamic Bayesian Network to represent a ripening process of a soft mould cheese.

Cédric Baudrit Pierre-Henri Wuillemin 1 Mariette Sicard Nathalie Perrot
1 DECISION
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Available knowledge to describe food processes has been capitalized from different sources, is expressed under different forms and at different scales. To reconstruct the puzzle of knowledge by taking into account uncertainty, we need to combine, integrate different kinds of knowledge. Mathematical concepts such that expert systems, neural networks or mechanistic models reach operating limits. In all cases, we are faced with the limits of available data, mathematical formalism and the limits of human reasoning. Dynamical Bayesian Networks (DBNs) are practical probabilistic graphic models to represent dynamical complex systems tainted with uncertainty. This paper presents a simplified dynamic bayesian networks which allows to represent the dynamics of microorganisms in the ripening of a soft mould cheese (Camembert type) by means of an integrative sensory indicator. The aim is the understanding and modeling of the whole network of interacting entities taking place between the different levels of the process.
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Conference papers
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Cédric Baudrit, Pierre-Henri Wuillemin, Mariette Sicard, Nathalie Perrot. A dynamic Bayesian Network to represent a ripening process of a soft mould cheese.. 12th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, Sep 2008, Zagreb, Croatia. Springer, 12th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems, 5178, pp.265-272, Lecture Notes in Computer Science. 〈10.1007/978-3-540-85565-1_33〉. 〈hal-01302126〉

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