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International audience; A conceptual model was developed to relate oral processing parameters and aroma release of cooked white rice. The conceptual model indicates that aroma release is dependent on the increase of particle surface area, the dilution effect of saliva and the diffusion of aroma from food residues that can be trapped in the buccal-pouches in the mouth. The model was validated against in vivo retro-nasal aroma release data during the consumption of rice flavoured with two aroma compounds (2-nonanone and ethyl propanoate) by five panellists. The oral processing behaviour of each subject was characterised at four different stages during oral processing by measuring bolus particle size, saliva content and the amount of residue particles that could be washed from the mouth after bolus expectoration. The results showed that aroma release for all subjects were dependent on the particle breakdown pathways used in oral processing. Subjects who reduced the rice to smaller particle sizes, higher pasted fraction and higher bolus residues had higher aroma release as expected from the conceptual model. Accounting for the physiological variables of subjects, the physicochemical parameters of aroma compounds and using a larger number of subjects in future studies will improve the model reliability.

To evaluate the effects of temperature and relative humidity (RH) on microbial and biochemical ripening kinetics, Camembert-type cheeses were prepared from pasteurized milk seeded with Kluyveromyces marxianus, Geotrichum candidum, Penicillium camemberti, and Brevibacterium aurantiacum. Microorganism growth and biochemical changes were studied under different ripening temperatures (8, 12, and 16 degrees C) and RH (88, 92, and 98%). The central point runs (12 degrees C, 92% RH) were both reproducible and repeatable, and for each microbial and biochemical parameter, 2 kinetic descriptors were defined. Temperature had significant effects on the growth of both K. marxianus and G. candidum, whereas RH did not affect it. Regardless of the temperature, at 98% RH the specific growth rate of P. camemberti spores was significantly higher [between 2 (8 degrees C) and 106 times (16 degrees C) higher]. However, at 16 degrees C, the appearance of the rind was no longer suitable because mycelia were damaged. Brevibacterium aurantiacum growth depended on both temperature and RH. At 8 degrees C under 88% RH, its growth was restricted (1.3 x 10(7) cfu/g), whereas at 16 degrees C and 98% RH, its growth was favored, reaching 7.9 x 10(9) cfu/g, but the rind had a dark brown color after d 20. Temperature had a significant effect on carbon substrate consumption rates in the core as well as in the rind. In the rind, when temperature was 16 degrees C rather than 8 degrees C, the lactate consumption rate was approximately 2.9 times higher under 88% RH. Whatever the RH, temperature significantly affected the increase in rind pH (from 4.6 to 7.7 +/- 0.2). At 8 degrees C, an increase in rind pH was observed between d 6 and 9, whereas at 16 degrees C, it was between d 2 and 3. Temperature and RH affected the increasing rate of the underrind thickness: at 16 degrees C, half of the cheese thickness appeared ripened on d 14 (wrapping day). However, at 98% RH, the underrind was runny. In conclusion, some descriptors, such as yeast growth and the pH in the rind, depended solely on temperature. However, our findings highlight the fact that the interactions between temperature and RH played a role in P. camemberti sporulation, B. aurantiacum growth, carbon substrate consumption rates, and the thickening of the cheese underrind. Moreover, the best ripening conditions to achieve an optimum between microorganism growth and biochemical kinetics were 13 degrees C and 94% RH.

A multidisciplinary approach combining physiology and physical chemistry and associating experimental measurements with in silico modelling was applied to explain the release of aroma compounds during food consumption. Experimental release kinetics obtained by inhaling gaseous samples through controlled protocols highlighted different release behaviours, depending on aroma compound properties. The associated mathematical model described mass transfer mechanisms between the different compartments of the naso-oro-pharyngeal cavities and included both physicochemical and physiological parameters. One of the main developments was notably to consider the possible retention of aroma compounds by wetted mucosa. Model sensitivity analysis confirmed the key role of interaction between aroma compounds and mucosa (air/mucosa partition coefficient) and of individual breath parameters (current breath volume and respiratory frequency) on the persistence of aroma compound in exhaled air. These achievements show that the association of an experimental approach and mechanistic modelling constitutes a powerful tool to improve the understanding of aroma release and persistence.

The aim of this study was to gain knowledge about the role of triacylglycerol (TAG) composition in fatty acids (FA) of o/w emulsions on both the pancreatic lipolysis kinetics and the bioaccessibility of released products (i.e. contained within the bile salt micellar phase). A mathematical model was developed and its predictions were compared to a set of experimental data obtained during an in vitro digestion of a whey protein stabilized emulsion. Modeling results show that FA residues of TAG were hydrolyzed at specific rates, inducing different bioaccessibility kinetics. The estimated lipolysis rate constants of the studied FA (C8:0, C10:0 >> C18:1 n-9 >> C12:0 > C14:0 > C16:0 approximate to C16:1 n-7 > C22:6 n-3) were in close agreement with the available literature on the substrate specificity of pancreatic lipase. Results also suggest that lipolysis products are very rapidly solubilized in the bile salt mixed micelles with no fractionation according to the FA carbon chain.

The aim of this work was to elaborate a predictive model of the mass transfer (water loss and solute gain) that occurs during dewatering and soaking by using neural network modelling. Two separate feedforward networks with one hidden layer were used (for water loss and solute gain respectively). Model validation was carried out on results obtained previously, which dealt with agar gel soaked in sucrose solution over a wide experimental range (temperature, 30-70 °C; solu tion concentration, 30-70 g sucrose/100 g solution; time 0-500 min; agar concentration, 2-8%). The best results were obtained with three hidden neurons, which made it possible to predict mass transfer, with an accuracy at least as good as the experimental error, over the whole experimental range. The technological interest of such a model is related to a rapidity in simulation compa rable to that of a traditional transfer function, a limited number of parameters and experimental data, and the fact that no preliminary assumption on the underlying mechanisms was needed.

In vivo aroma release during solid food consumption is a complex phenomenon that depends on food structure and composition, as well as on oral processing (combination of mastication and incorporation of saliva into the food product). The objective of this study was to understand and to predict the physicochemical properties of aroma compounds through the dynamics of flavor release during in-mouth oral processing of food before bolus swallowing. Within this context, the evolution of two aroma compounds during bolus formation was explored by studying the two main properties that account for mass transfer: air/ bolus partition and mass transfer coefficients. Four types of industrial cheese products (varying in fat and firmness) flavored with ethyl propanoate and 2-nonanone were chosen. Each matrix was mixed with various amounts of artificial saliva to mimic boluses at different stages of mastication. The air/bolus partition coefficient was determined by the static phase ratio variation method (PRV), while the mass transfer coefficient was obtained by non-linear regression from dynamic headspace experiments. Results showed that there is a dilution effect on the air/bolus partition coefficient and both a dilution and a product effect (firmness) on the mass transfer coefficient of ethyl propanoate in the bolus. These results were also validated with 2-nonanone for the low-fat cheeses.

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.

International audience; The temporal dominance of sensations method showed that four model dairy products had different dynamic profiles in terms of salt and texture perception. We investigated the physical origins of these differences, by studying the breakdown of these products and its impact on salt release. An experimental device was used for monitoring the kinetics of salt release from the food products into water -simulating saliva- after a standardized compression -simulating mastication independently of the inter and intra individual variability. A mechanistic model was developed to quantify product breakdown in terms of the area of contact between the product and the aqueous phase. Fat had a major influence on breakdown behaviour and the calculated contact area that could be accounted for by the microstructure of the product. These results provide insight into the possible origins of differences in sensory perceptions of foods. We also discussed the use of this mechanistic model for modeling salt release in the conditions of food consumption.

A model predicting the weight loss and the dry matter increase in the core and in the rind of a French smear soft cheese (Munster type) has been developed. Experimental data were collected from experiments carried out in an aseptic pilot scale ripening chamber under nine different combinations of temperature (8, 12 and 16 °C) and of relative humidity (85%, 93% and 99%) according to a complete experimental design. For each run 16 samples were analysed at various ripening stages. The model took into account the water loss by evaporation, the water transfer from the core to the surface and the mass loss due to the CO2 release. The cheese weight loss was more influenced by the relative humidity than by the temperature. The model was able to account for the experimental weight loss as well as the rind and the core dry matter, with accuracy acceptable for practical use.

A mechanistic model for Debaryomyces hansenii growth and substrate consumption, lactose conversion into lactate by lactic acid bacteria, as well as lactose and lactate transfer from the core toward the rind was established. The model described the first step (14 d) of the ripening of a smear soft cheese and included the effects of temperature and relative humidity of the ripening chamber on the kinetic parameters. Experimental data were collected from experiments carried out in an aseptic pilot scale ripening chamber under 9 different combinations of temperature (8, 12, and 16 degrees C) and relative humidity (85, 93, and 99%) according to a complete experimental design. The model considered the cheese as a system with 2 compartments (rind and core) and included 5 state evolution equations and 16 parameters. The model succeeded in predicting D. hansenii growth and lactose and lactate concentrations during the first step of ripening (curd deacidification) in core and rind. The nonlinear data-fitting method allowed the determination of tight confidence intervals for the model parameters. The residual standard error (RSE) between model predictions and experimental data was close to the experimental standard deviation between repeated experiments.

Aroma compound properties in food matrices, such as volatility and diffusivity, have to be determined to understand the effect of composition and structure on aroma release and perception. This work illustrates the use of mass transfer modeling to identify diffusion and partition properties of ethyl hexanoate in water and in carrageenan matrices with various degrees of structure. The comparison of results obtained with a diffusive model to those obtained with a convective model highlights the importance of considering the appropriate transfer mechanism. Modeling of the preliminary experimental steps ensures correct estimation of the conditions for the main aroma release step. The obtained values of partition and diffusion coefficients are in agreement with those found in the literature (either experimentally determined or predicted by theoretical equations) and demonstrate that the structure level of carrageenan matrices has little influence on diffusion properties of ethyl hexanoate (less than 20%).

Mass yield and operating time for a pork meat dehydration–impregnation–soaking (DIS) process were optimized using a coupled genetic algorithm/sequential quadratic programming method in order to obtain the optimal operating conditions: temperature and soaking solution concentrations. The DIS process was simulated by a neural network model. The non-linear optimization problem was constrained to ensure the main product characteristics: stability indicated by the water activity target and flavour characterized by the phenol gain target. The climatic conditions, the model validity region, the raw material costs and the operator working schedule were taken into account. Optimal solutions are discussed for three different batch configurations: single-stage processing under constant conditions, single-stage processing under varying temperature and two-stage processing under constant conditions. The most convenient operation resulted in a two-stage soaking process because of time, energy and cost savings, control convenience, product cooling anticipation and a reasonably high mass yield.

25 ref.; International audience; The drying of paddy rice may result in quality degradation, expressed as a head kernel yield, leading to significant commercial depreciation of the product. A mathematical model of the drying and of the quality degradation process was combined with a dynamic optimization algorithm to determine the drying conditions (air temperature and relative humidity as functions of time) that ensured the highest possible final product quality for a specified drying time and a specified final moisture content. The robustness of the optimal drying strategy with respect to the initial state of the product, to the model parameters and to the initialization of the optimization algorithm was verified. The compromise between the highest achievable final quality and the allowed total drying time was studied. The combination of simulation and optimization yielded a new insight in the rice drying process and in the quality preservation strategies.

International audience; Frying is one of the way for performing drying of food material that permits to obtain very short duration. However the control of such operation is difficult because of the immersion of product into hot oil bath that imply difficult or impossible sensors implementation. Such problems are encoutered for Dehydration Soaking Process (DISP). In the case of Batch drying using frying of food stuffs, an original approach is presented. An indirect measurement of moisture content is performed as a combination of easy to do oil bath temperature mesasurement and an estimator. Validation is provided and performance is well established. Because of the ability of moisture content real time measurements, a control strategy is studied and put into practice. An optimal predictive controller is proprosed, able to put into practice several kind of control objectives. Results are discussed and extension of the strategy is introduced

International audience; A predictive optimal control algorithm for a fruit refrigeration process is described. The future inlet air temperature profile is optimized on line, taking into account the most recent process measurements, and thus introducing feedback. The optimality criterion is directly based on economic costs. Technological and product quality constraints are included. The future control profile optimization requires long term predictions of process variables evolution, achieved using a physical model of the plant. The model is adjusted on line in order to account for unmeasured disturbances and poorly known product properties. Both the optimality criterion and the plant model are nonlinear. The approach is experimentally validated on a pilot scale refrigeration plant. Economic benefits from using an optimal, rather than a traditional constant temperature profile are discussed, revealing the importance of the process configuration. Robustness of the control algorithm with respect to modeling errors, measured and unmeasured disturbances, and temporary plant failures is demonstrated.; Un algorithme de contrôle optimal prédictif d'un procédé de réfrigération des fruits est décrit. La future courbe de température d'entrée de l'air est optimisée en ligne en tenant compte des mesures de process les plus récentes et en introduisant de ce fait un retour d'informations. Le critère d'optimalité est directement fondé sur les coûts économiques. Les contraintes de qualité du produit et les contraintes technologiques sont comprises. L'optimisation à venir de la courbe de contrôle requiert des prévisions à long terme de l'évolution des variables de process obtenue à l'aide d'un modèle physique de l'installation. Le modèle est ajusté en ligne afin de tenir compte des perturbations non mesurées et des propriétés de produit mal connues. Le critère d'optimalité et le modèle d'installation sont tous deux non-linéaires. L'approche est validée du point de vue expérimental sur une installation de réfrigération à l'échelle pilote. Les avantages économiques découlant de l'utilisation d'une courbe de température optimale plutôt que d'une courbe de température constante traditionnelle sont abordés, ce qui révèle l'importance de la configuration du process. La robustesse de l'algorithme de contrôle par rapport aux erreurs de modélisation, aux perturbations mesurées et non mesurées et les pannes temporaires de l'installation est démontrée.