Kamal Kansou

Modelling Wheat Flour Dough Proofing Behaviour: Effects of Mixing Conditions on Porosity and Stability

Kinetics of porosity and stability of dough expansion during proofing have been fitted with Gompertz and exponential models, respectively, for 24 distinct mixing conditions and same dough composition. Data for 10 conditions were used to relate the parameters of the models to mixing variables, specific power, and texturing time, through power regression models. Interpretation of the relationships between the mixing variables and the parameters of the Gompertz and exponential models emphasises the influence of dough rheological properties on dough expansion during fermentation and likely on bubbles distribution. The prediction performances of these porosity and stability models were evaluated using the root mean square error and mean absolute percentage error, for time series of the remaining 14 mixing conditions. The results show that integrating the mixing variables into the models significantly improves the prediction accuracy compared to control models whose parameters values are arithmetic means. Finally, we present an application where the mixing variables are determined in order to obtain a dough exhibiting the desired features during proofing, such as high levels of porosity and stability. Intensive mixing yields the best result but a more interesting trade-off can be obtained with intermediary mixing processes.

How plants changed the world: Using qualitative reasoning to explain plant macroevolution's effect on the long-term carbon cycle

We present a qualitative reasoning model of how plant colonization of land during the mid Paleozoic era (450–300 million years ago) altered the long-term carbon cycle resulting in a dramatic decrease in global atmospheric carbon dioxide levels. This model is aimed at facilitating learning and communication about how interactions between biological and geological processes drove system behavior. The model is developed in three submodels of the main system components, namely how competition for limited land habitat drove natural selection for increasing adaptations to life on land; how these adaptations resulted in increased formation of organic-rich sedimentary rocks (coal); and how these adaptations altered weathering of calcium and magnesium silicate rocks, resulting in increased deposition of inorganic carbonates in oceans. These separate submodels are then assembled to derive the full dynamic model of plant macroevolution, colonization of land, and plummeting carbon dioxide levels that occurred during the mid Paleozoic. The qualitative reasoning framework supports explicit representation of causal feedbacks — as with previously developed systems analysis models — but also supports simulation of system dynamics arising from the configuration of entities in the system. The ability of qualitative reasoning to provide causal accounts (explanations) of why certain phenomena occurred and when, is a powerful advantage over numerical simulation such as the complex GEOCARB models, where explanation must be left to interpretation by experts.

Amylolysis of maize mutant starches described with a fractal-like kinetics model

Two empirical models, a conventional first-order kinetics and a fractal-like first-order kinetic model were tested for analysing the hydrolysis of 13 wild type, single and double mutants of maize starch by porcine pancreatic α-amylase (PPA). The major difference between the two models is an additional parameter, the fractal kinetics exponent h, which enables to characterise a decrease of the reaction rate coefficient over time. The fractal-like kinetic model should be preferred to characterise the amylolysis for 10 mutants out of 13 as sugary-2 and amylose-extender curves exhibit clear reaction rate retardation, unlike normal maize and waxy maize. Analysis of the model parameter values reveals two groups of kinetics for the maize mutants: amylose-extender, normal and waxy on one hand, sugary-2 on the other hand. Possible relations between the parameters of the model and granule composition and structure are discussed.

Multivariate model to characterise relations between maize mutant starches and hydrolysis kinetics

The many studies about amylolysis have collected considerable information regarding the contribution of the starch physico-chemical properties. But the inherent elaborate and variable structure of granular starch and, consequently, the multifactorial condition of the system hinders the interpretation of the experimental results. The immediate benefit of multivariate statistical analysis approaches with that regard is twofold: considering the factors, possibly interrelated, all together and not independently, providing a first estimation of the magnitude and confidence level of the relations between factors and amylolysis kinetic parameters. Based on data of amylolysis of 13 starch samples from wild type, single and double mutants of maize by porcine pancreatic α-amylase (PPA), a multivariate analysis is proposed. Amylolysis progress-curves were fitted by a Weibull function, as proposed in a previous work, to extract three kinetic parameters: the reaction rate coefficient during the first time-unit, k, the reaction rate retardation over time, h, and the final hydrolysis extent, X∞. Multivariate models relate the macromolecular composition and the fractions of crystalline polymorphic types to the kinetic parameters. h and X∞ are found to be highly related to the measured properties. Thus the amylose content appears to be significantly correlated to the hydrolysis rate retardation, which sheds some light on the probable contribution of the amylose molecules contained in the granules. The multivariate models give correct prediction performances except for k whose a part of variability remains unexplained. A further analysis points out the extent of the characterisation effort of the granule structure needed to extend the fraction of explained variability.

Basic Knowledge Models for the Processing of Bread as a Solid Foam

The breadmaking process can be defined by the succession of operations with operating conditions as input variables and dough properties as output ones, any output variable at step i being an input at step i+1. In this paper, we strive to show how the main properties of bread, density, porosity and alveolar structure (crumb), can be predicted from basic knowledge models (BKMs). So we have defined the variables of breadmaking, proposed BKMs for the two first operations, mixing and proofing, and underlined the needs to define them for shaping and baking, after a short review of existing models. The specific energy delivered during mixing is determined by a simple balance equation in order to predict gluten structuration and dough viscosity, the main output of mixing operation. Then an analysis of dough proofing at different structural scales, by rheology and imaging, allows to assess its alveolar structure, and to fit the kinetics of porosity and stability by phenomenological models. Finally we show how these BKMs could be integrated in order to help the design of baked products with target properties.

Hypothesis assessment with qualitative reasoning: Modelling the Fontestorbes fountain

Abstract This paper demonstrates the utility of the Qualitative Reasoning approach for hypothesis testing in the domain of ecology regarding the functioning of ‘black box’ systems. As a test case, we refer to the study performed by Mangin (1969) with scale models to investigate the hidden mechanism of the Fontestorbes fountain, a spring that exhibits a periodic flow situated in the south of France. In our approach, a Qualitative Reasoning method (and hence a qualitative model) is used to test the ‘siphon-hypothesis’, which traditionally explains the oscillations of the flow rate of a periodic spring by the principle of filling and emptying an underground reservoir through a siphon action. Parts of the simulation results show that the hypothesis is qualitatively accurate; in particular the model produces a cyclic behaviour that matches with the observed one. However, the qualitative model also exhibits a contradictory behaviour (true negative) that challenges the hypothesis consistency. The causal account of this true negative denotes and explains a flaw in the siphon-hypothesis. The paper concludes that, with the Qualitative Reasoning method, models can be constructed for hypothesis testing. Such models should generate the desired behaviour as a first and necessary step to support the viability of the hypothesis. However, the occurrence of unexpected behaviours provides information that challenges the hypothesis, and may lead to having to discard it.

Integration of Basic Knowledge Models for the Simulation of Cereal Foods Processing and Properties

Cereal processing (breadmaking, extrusion, pasting, etc.) covers a range of mechanisms that, despite their diversity, can be often reduced to a succession of two core phenomena: (1) the transition from a divided solid medium (the flour) to a continuous one through hydration, mechanical, biochemical, and thermal actions and (2) the expansion of a continuous matrix toward a porous structure as a result of the growth of bubble nuclei either by yeast fermentation or by water vaporization after a sudden pressure drop. Modeling them is critical for the domain, but can be quite challenging to address with mechanistic approaches relying on partial differential equations. In this chapter we present alternative approaches through basic knowledge models (BKM) that integrate scientific and expert knowledge, and possess operational interest for domain specialists. Using these BKMs, simulations of two cereal foods processes, extrusion and breadmaking, are provided by focusing on the two core phenomena. To support the use by non-specialists, these BKMs are implemented as computer tools, a Knowledge-Based System developed for the modeling of the flour mixing operation or Ludovic®, a simulation software for twin screw extrusion. They can be applied to a wide domain of compositions, provided that the data on product rheological properties are available. Finally, it is stated that the use of such systems can help food engineers to design cereal food products and predict their texture properties.

Phenomenological model of maize starches expansion by extrusion

During extrusion of starchy products, the molten material is forced through a die so that the sudden abrupt pressure drop causes part of the water to vaporize giving an expanded, cellular structure. The objective of this work was to elaborate a phenomenological model of expansion and couple it with Ludovic® mechanistic model of twin screw extrusion process. From experimental results that cover a wide range of thermomechanical conditions, a concept map of influence relationships between input and output variables was built. It took into account the phenomena of bubbles nucleation, growth, coalescence, shrinkage and setting, in a viscoelastic medium. The input variables were the moisture content MC , melt temperature T, specific mechanical energy SME , shear viscosity η at the die exit, computed by Ludovic®, and the melt storage moduli E’(at T > T g ). The outputs of the model were the macrostructure (volumetric expansion index VEI, anisotropy) and cellular structure (fineness F) of solid foams. Then a general model was established: VEI = α (η/η 0) n in which α and n depend on T, MC , SME and E’ and the link between anisotropy and fineness was established.

Multi-scale characterization of thermoplastic starch structure using Second Harmonic Generation imaging and NMR

Starch granules can be extruded to obtain a thermoplastic material. Thermoplastic starch (TPS) usually requires a significant break down of the starch granular organization to form a continuous polysaccharide matrix. In this work, we extrude potato starch with and without a plasticizer and store samples at high humidity to generate recrystallization. A multi-scale investigation of the microstructure is performed by combining different techniques: WAXS and solid-state NMR to describe macromolecule organization and Second Harmonic Generation (SHG) imaging to provide spatial information. Finally, the ability of the material to swell and remain sound in water is assessed. Glycerol-plasticized samples swell the least despite many granules with native-like structure embedded in the starch matrix. Glycerol limits the fragmentation and melting of the granules and crystallites during extrusion but also reduces the proportion of starch molecules in constrained conformations, enabling the formation of a polymer network that can sustain the penetration of water.

Testing scientific models using Qualitative Reasoning: Application to cellulose hydrolysis

With the accumulation of scientific information in natural science, even experts can find difficult to keep integrating new piece of information. It is critical to explore modelling solutions able to capture information scattered in publications as a computable representation form. Traditional modelling techniques are important in that regard, but relying on numerical information comes with limitations for integrating results from distinct studies, high-level representations can be more suited. We present an approach to stepwise construct mechanistic explanation from selected scientific papers using the Qualitative Reasoning framework. As a proof of concept, we apply the approach to modelling papers about cellulose hydrolysis mechanism, focusing on the causal explanations for the decreasing of hydrolytic rate. Two explanatory QR models are built to capture classical explanations for the phenomenon. Our results show that none of them provides sufficient explanation for a set of basic experimental observations described in the literature. Combining the two explanations into a third one allowed to get a new and sufficient explanation for the experimental results. In domains where numerical data are scarce and strongly related to the experimental conditions, this approach can aid assessing the conceptual validity of an explanation and support integration of knowledge from different sources.