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Coupling deterministic and random sequential approaches for structure and texture prediction of a dairy oil-in-water emulsion

Abstract : Dairy products made of concentrated milk protein powder and milk fat have been experimentally shown to behave like complex systems: The resulting textures depend on various factors, including concentration and type of proteins, nature of heat treatment and homogenisation process. The aim of this paper is to combine two models in order to predict the composition of the interface of a homogenised oil-in-water emulsion, and the resulting bridge structure between the fat droplets. This structure is then correlated to the texture of the emulsion. Free unknown parameters of both models have been estimated from experimental data using an evolutionary optimisation algorithm. The resulting model fits the experimental data, and is coherent with the macroscopic texture measurements. Industrial relevance: Sustainability is nowadays at the heart of industrial requirements. The development of mathematical approaches should facilitate common approaches to risk/benefit assessment and nutritional quality in food research and industry. These models will enhance knowledge on process-structure-property relationships from molecular to macroscopic level, and facilitate creation of in-silico simulators with functional and nutritional properties. The stochastic optimisation techniques (evolutionary algorithms) employed in these works allow the users to thoroughly explore the systems and optimise it. With regard to the complexity of the food systems and dynamics, the challenge of the mathematical approaches is to realise a complete dynamic description of food processing. In order to reach this objective, it is mandatory to use innovative strategies, exploiting the most recent advances in cognitive and complex system sciences. (C) 2013 Elsevier Ltd. All rights reserved.
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Contributor : Migration Prodinra <>
Submitted on : Tuesday, June 2, 2020 - 10:08:28 PM
Last modification on : Thursday, July 22, 2021 - 10:03:28 AM


  • HAL Id : hal-02739639, version 1
  • PRODINRA : 331591



Etienne Descamps, Nathalie Perrot, Sébastien Gaucel, Ioan-Cristian Trelea, Alain Riaublanc, et al.. Coupling deterministic and random sequential approaches for structure and texture prediction of a dairy oil-in-water emulsion. The DREAM Project International Conference From Model Foods to Food Models, Institut National de Recherche Agronomique (INRA). UAR Département Caractérisation et Elaboration des Produits Issus de l'Agriculture (1008)., Jun 2013, Nantes, France. 108 p. ⟨hal-02739639⟩



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