Multiphysics modelling of bread baking using a stochastic porous media framework: Integration of heat, mass, structural, and quality transformations

Although baking is one of the oldest and most critical steps in the bread manufacturing process, its intricate mechanisms have not been fully comprehended. Mechanistic modelling is frequently used to understand complex mechanism of the baking process. However, prevailing models mostly employ classical porous media approach, and they are not capable of capturing the effect of pores due to its lack of geometrical representation (individual pore structures). In the current study, we applied an alternative approach that takes the porous structure into account by utilising the Poisson grain model, a random porous media approach that involves distributing pores randomly within the geometry. To describe changes in moisture and temperature, a fully coupled heat and mass transfer model was employed. For estimating expansion, solid mechanics using the conjugate-coupled Kelvin-Voigt model was used. Besides that, textural (hardness, firmness, chewiness) and browning (L* and ΔE) characteristics were predicted using data-driven empirical models, which were trained on experimental data and integrated in the mechanistic simulation framework. Sensitivity of model predictions to (i) size change and (ii) random redistribution of the pores within the model geometry was also tested. The model predictions agreed well with the experimental results for moisture, temperature, texture, colour, and expansion. Moreover, separate description of the pores (consisting of gases) and the solid part (which does not contain gas but the mixture of dry matter and water) allows better analysis of the changes that occur during the baking process, and reflection of details that are not seen in the classical methods is possible with the use of random porous media approach.