The growth of Escherichiacoli in a food simulant during conduction cooling: combining engineering and microbiological modelling

Developments of mathematical models for simulating vacuum cooling processes for food products - a review

Vacuum cooling is a rapid cooling method widely used in cooling some food products. Simulating the vacuum cooling process with mathematical models helps to acquire a more intuitive understanding and optimize the whole cooling process. However, there is no review summarizing the mathematical models of vacuum cooling. In this review, heat and mass transfer process during vacuum cooling, types of mathematical models for vacuum cooling, and numerical methods including finite difference method, finite element method and finite volume method used for process simulation are introduced in details. The food products used in numerical simulation study of vacuum cooling generally include liquid food, vegetables and cooked meat. The ranges of application of various numerical methods are also discussed. Moreover, heat and mass transfer coefficients have a great influence on the accuracy of the model, and are generally provided by the literature. The investigations presented in this review invariably demonstrate that mathematical modeling can provide good prediction of key information of vacuum cooling process, and has a great potential to improve vacuum cooling process in the food industry. However, more efforts are still needed to realize the industrial translation of laboratory results.

Control Paramethers for Sous-vide Cook-chill Processing of Swiss Chard (Beta vulgaris) Stems

Within the various stages involved in the preparation of swiss chart stems (Beta vulgaris) as a `V gamme' product, this work concentrated on heating and subsequent cooling, since these are the stages which cause the most drastic changes in the quality parameters of the product. For the detailed analysis, a 3D finite-difference conductive heat transfer model has been developed. This model takes into account vegetal physico-chemical properties (humidity, density, thermal conductivity) as well as their thermoresistance. The texture is one of the parameters that most influences the organoleptic quality of this product, both as an indicator of the extent of cooking and for reflecting the level of homogeneity. Its temperature dependence has been determined by means of a texturometer, confirming a first-order Arrhenius type behavior. Assuming a pasteurization level corresponding to the absence of Listeria monocytogenes in its vegetative form with a margin of 7 logarithmic units as appropriate, the solution of the model enables the establishment of the time necessary for each processing temperature (from 65 to 95 °C). Given the marked temperature gradients observed, various geometric configurations of the stems have been simulated in order to find the most homogenous product.

Development of an integrated model for heat transfer and dynamic growth of Clostridium perfringens during the cooling of cooked boneless ham

Numerous small meat processors in the United States have difficulties complying with the stabilization performance standards for preventing growth of Clostridium perfringens by 1 log10 cycle during cooling of ready-to-eat (RTE) products. These standards were established by the Food Safety and Inspection Service (FSIS) of the US Department of Agriculture in 1999. In recent years, several attempts have been made to develop predictive models for growth of C. perfringens within the range of cooling temperatures included in the FSIS standards. Those studies mainly focused on microbiological aspects, using hypothesized cooling rates. Conversely, studies dealing with heat transfer models to predict cooling rates in meat products do not address microbial growth. Integration of heat transfer relationships with C. perfringens growth relationships during cooling of meat products has been very limited. Therefore, a computer simulation scheme was developed to analyze heat transfer phenomena and temperature-dependent C. perfringens growth during cooling of cooked boneless cured ham. The temperature history of ham was predicted using a finite element heat diffusion model. Validation of heat transfer predictions used experimental data collected in commercial meat-processing facilities. For C. perfringens growth, a dynamic model was developed using Baranyi's nonautonomous differential equation. The bacterium's growth model was integrated into the computer program using predicted temperature histories as input values. For cooling cooked hams from 66.6 degrees C to 4.4 degrees C using forced air, the maximum deviation between predicted and experimental core temperature data was 2.54 degrees C. Predicted C. perfringens growth curves obtained from dynamic modeling showed good agreement with validated results for three different cooling scenarios. Mean absolute values of relative errors were below 6%, and deviations between predicted and experimental cell counts were within 0.37 log10 CFU/g. For a cooling process which was in exact compliance with the FSIS stabilization performance standards, a mean net growth of 1.37 log10 CFU/g was predicted. This study introduced the combination of engineering modeling and microbiological modeling as a useful quantitative tool for general food safety applications, such as risk assessment and hazard analysis and critical control points (HACCP) plans.