Meat cooking: simulation of process times and weight loss

GOÑI, S. M.; SALVADORI, V. O.

Viña del Mar, Chile

Congreso; Tenth International Congress on Engineering and Food; 2008

An adequate design and control of industrial cooking is vital to ensure safety and high quality processed foods. Various aspects related to global quality determine the consumer acceptability. Our interest in this field is to study the heat transfer aspects of cooking, looking for simple relationships between the processing times and the operative conditions. The use of complete mathematical models represents a useful tool for the analysis, design, simulation and optimization of this technology. The aim of this work is to simulate the cooking of a meat piece (semitendinosus muscle), under different operative conditions, using finite element method, solved in a commercial software (COMSOL Multiphysics). For the numerical results, simplified prediction equations of cooking time and weight loss are proposed.  Both microscopic heat and mass transfer balances describe the behaviour of food during its cooking, i.e. increasing its temperature until approximately 100ºC, and reducing its size due to significant loss of water. The developed model was validated through experimental cooking performed in a convective oven (results shown in previous works). Physical properties from literature or ad-hoc developed correlations were used, average experimental heat and mass transfer coefficients were used. A representation of the meat piece, obtained by means of nuclear magnetic resonance, digital image processing and computational vision, was used as simulation domain. Several simulations were performed, scaling the domain with a geometric scale factor from 0.8 to 1.1, ambient temperature from 172 to 223ºC and initial sample temperature from 5 to 20ºC. The results of these simulations were used as the basis of simple equations that predict cooking time and weight loss respectively, knowing the values of oven temperature, and sample initial weight and temperature. Using validated mathematical models provides a way of obtaining simple prediction equations. Besides, the results highlight the goodness of the image processing methodology, associated with computational vision, to obtain high fidelity geometrical representations of the real sample. This eliminates the error derived from using approximate geometries.