Modelling and analysis of complex food systems: State of the art and new trends

The aim of this review is twofold. Firstly, we present the state of the art in dynamic modelling and model-based design, optimisation and control of food systems. The need for nonlinear, dynamic, multi-physics and multi-scale representations of food systems is established. Current difficulties in building such models are reviewed: incomplete, piecewise available knowledge, spread out among different disciplines (physics, chemistry, biology and consumer science) and contributors (scientists, experts, process operators, process managers), scarcity, uncertainty and high cost of measured data, complexity of phenomena and intricacy of time and space scales. Secondly, we concentrate on the opportunities offered by the complex systems science to cope with the difficulties faced by food science and engineering. Newly developed techniques such as model-based viability analysis, optimisation, dynamic Bayesian networks etc. are shown to be relevant and promising for design and optimisation of foods and food processes based on consumer needs and expectations

Main individual and product characteristics influencing in-mouth flavour release during eating masticated food products with different textures: mechanistic modelling and experimental validation

Research Areas: Life Sciences & Biomedicine - Other Topics; Mathematical & Computational BiologyA mechanistic model predicting flavour release during oral processing of masticated foods was developed. The description of main physiological steps (product mastication and swallowing) and physical mechanisms (mass transfer, product breakdown and dissolution) occurring while eating allowed satisfactory simulation of in vivo release profiles of ethyl propanoate and 2-nonanone, measured by Atmospheric Pressure Chemical Ionization Mass Spectrometry on ten representative subjects during the consumption of four cheeses with different textures. Model sensitivity analysis showed that the main parameters affecting release intensity were the product dissolution rate in the mouth, the mass transfer coefficient in the bolus, the air-bolus contact area in the mouth and the respiratory frequency. Parameters furthermore affecting release dynamics were the mastication phase duration, the velopharynx opening and the rate of saliva incorporation into the bolus. Specific retention of 2-nonanone on mucosa was assumed to explain aroma release kinetics and confirmed when gaseous samples were consumed

A novel approach for studying the indoor dispersion of aroma through computational fluid dynamics

We propose a mechanistic modelling approach for studying the indoor dispersion of aroma compounds which are released from, for instance, food products. The approach combines the indoor velocity field with a release model for aroma compounds. The release mass flux is expressed as a function of key variables such as mass transfer and gas-liquid partition coefficients, and the source geometry. The transport properties of ambient air are assumed to be independent of the aroma concentration; hence release and dispersion problems can be solved separately. First, the velocity field is obtained as solution of the fluid flow problem through computational fluid dynamics (CFD). The turbulent velocity field is then used to predict the time evolution of concentration of an aroma compound released by a constant rate source, in an initially aroma-free environment. These results are interpreted in terms of a step response function. The aroma concentration as a function of time is finally estimated by convolving the possibly time-varying release mass flux and the response function associated with the position of interest. The modelling approach is flexible and computationally effective, since different release models as well as the release of distinct aroma compounds can be directly studied by taking into account a same velocity field, without any additional CFD simulation. The validity of the approach is assessed from measurements of aroma concentration in a 140m3 room, under constant release mass flux. The approach is also illustrated for a case where the release mass flux is not constant in time. © 2013 John Wiley & Sons, Ltd

Continuous optimisation theory made easy? Finite-element models of evolutionary strategies, genetic algorithms and particle swarm optimizers

Abstract. We propose a method to build discrete Markov chain models of continuous stochastic optimisers that can approximate them on arbitrary continuous problems to any precision. We discretise the objective function using a finite element method grid which produces corresponding distinct states in the search algorithm. Iterating the transition matrix gives precise information about the behaviour of the optimiser at each generation, including the probability of it finding the global optima or being deceived. The approach is tested on a (1+1)-ES, a bare bones PSO and a real-valued GA. The predictions are remarkably accurate.

Smooth trajectory planning for robot using Particle Swarm Optimization

International audienceIn this work, we deal with a class of problems of trajectory planning taking into account the smoothness of the trajectory. We assume that we have a set of positions in which the robot must pass. These positions are not assigned in the time axis. In this work, we propose a formulation of this problem, where the total length of the trajectory and the total time to move from the initial to the final position are minimized simultaneously. In order to ensure effective results and avoid abrupt movement, we should ensure the smoothness of the trajectory not only at the position level but also at the velocity and the acceleration levels. Thus, the position function must be at least two times differentiable. In our case, we use a polynomial function. We formulate this problem as a constraint optimization problem. To resolve it, we adapt the usual particle swarm algorithm to our problem and we show its efficiency by simulation