Modeling and estimation of rheological properties of food products for manufacturing simulations

The rheological properties of food products related to their engineering and manufacturing were modeled and estimated. A 2D/3D dynamic FE model for the simulation of uniform objects was formulated based on a five-element physical model and was further extended to deal with nonuniform layered objects. Three kinds of food materials (Japanese sweets) were tested to assess their deformation and force behaviors. An approach based on inverse FE optimization was then proposed to estimate the rheological properties of these objects. Two different sets of rheological parameters were estimated for describing the deformation and force respectively. The estimated parameters were then employed to simulate three-layered food products (consisting of two different materials) in two kinds of compressive operations. The validation results showed that the FE model and property-estimation method accurately reproduced both rheological force and deformation. The FE model can be used to predict the rheological behaviors of food products during their manufacture.     

An improved technique for determining transport parameters in cooling processes

In order to describe cooling processes, the thermal diffusivity α and the convective transfer coefficient h must be known. To this end, in general the analytical solution of the diffusion equation with only the first term is fitted to an experimental dataset of the temperature versus time, in which the temperature is measured in a known position. Despite this technique to describe well the major part of the cooling kinetics, this procedure contains a flaw for Fourier numbers less than 0.2 (lag factor). This article presents an algorithm based on optimal removal of experimental points to minimize errors in the determination of α and h. The algorithm was validated for several physical situations, and applied in cucumber cooling. The whole cooling kinetics was simulated with success (chi-square = 2.8756 × 10⁻³ and determination coefficient = 0.9991), including the region responsible by the lag factor.     

Toward predictive food process models: A protocol for parameter estimation

Mathematical models, in particular, physics-based models, are essential tools to food product and process design, optimization and control.

The success of mathematical models relies on their predictive capabilities. However, describing physical, chemical and biological changes in food processing requires the values of some, typically unknown, parameters. Therefore, parameter estimation from experimental data is critical to achieving desired model predictive properties.

This work takes a new look into the parameter estimation (or identification) problem in food process modeling. First, we examine common pitfalls such as lack of identifiability and multimodality. Second, we present the theoretical background of a parameter identification protocol intended to deal with those challenges. And, to finish, we illustrate the performance of the proposed protocol with an example related to the thermal processing of packaged foods.