Quantitative fit of a model for proving of bread dough and determination of dough properties

Bread staling prediction with a multiobjective particle swarm optimization-based bread constitutive modeling method

Aiming at the complex and cumbersome problems of current bread staling detection technology, a food constitutive modeling method based on the multiobjective particle swarm optimization (MOPSO) was proposed, which can quickly and efficiently identify the creep test parameters for bread, and predict the viscoelastic parameters of bread staling using the analyzed viscoelastic parameters, resulting in convenient and efficient detection of bread staling. Firstly, airflow-laser detection technology was used to carry out rapid, efficient, and non-destructive bread rheological tests to obtain bread creep test data. The MOPSO based on the Pareto set was then used to identify the generalized Kelvin model, and the discrimination accuracy was evaluated by using the inversion results established by the viscoelastic parameters, which resulted in efficient discrimination of creep test data of starch-based products represented by bread. Finally, using extreme learning machine regression (ELM), a prediction model between the analysis results and the moisture content of bread staling was established, and the prediction effect of the analysis results on bread staling was verified. The experimental results show that, when compared to finite element analysis (FEA) and non-linear regression (NLR) to identify creep parameters, the MOPSO overcomes the shortcomings of easy falling into the local optimal solution, is easy to implement, has strong global search ability, and is suitable for the analysis of high-dimensional viscoelastic models of complex foods. The correlation coefficient (R) of the prediction set established by the 12-membered viscoelastic parameters in the prediction model established by multi-element viscoelastic parameters and bread moisture content was 0.847, and the root mean square error (RMSE) was 0.021. This demonstrated that, when combined with MOPSO, airflow-laser detection technology could effectively identify the viscoelastic parameters of bread and establish a method suitable for monitoring bread staling in industrial production. The results of this study provide a reference for the identification of viscoelastic parameters of complex foods and to detect bread staling quickly and efficiently.

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.

Bran-induced effects on the evolution of bubbles and rheological properties in bread dough

Evolution of bubbles is the key to volume development in bread dough. The influence of wheat bran on bubble growth in bread dough through the mixing, fermentation, and proofing stages is described as a function of its level of addition. Confocal laser scanning microscopy in combination with image processing tools was used to obtain the bubble size and shape parameters. The relationship between bubble behavior and dough rheology was mapped using biaxial extension and dynamic oscillatory rheometry studies. With increase in level of bran addition, mean bubble size decreased corresponding to each stage and showed an inverse relationship with dough overpressure and elastic modulus. Addition of wheat bran was observed to suppress the bubble coarsening phenomenon in dough. Experimental observations indicated the plausibility of coalescence-mediated bubble growth in bread dough during the latter stages of fermentation and proofing, which was hindered in the presence of bran particles.

PRACTICAL APPLICATION

The results of this work provide an insight to the underlying mechanism by which wheat bran addition impacts the volume development in bread dough. The inferences presented in this research work can be used as a basis to study bubble dynamics in an opaque food system such as bread dough. This information would be of interest to industrial researchers working on the new product development of aerated bakery products with functional fibrous ingredients.