Beef-in-sauce catering meals under blast-cooling have been investigated in a research project which aims at quantitative HACCP (hazard analysis critical control point). In view of its prospective coupling to a predictive microbiology model proposed in the project, zero-order spatial dependence has proved to suitably predict meal temperatures in response to temperature variations in the cooling air. This approach has modelled heat transfer rates via the a priori unknown convective coefficient hc which is allowed to vary due to uncertainty and variability in the actual modus operandi of the chosen case study hospital kitchen. Implemented in MS Excel®, the numerical procedure has successfully combined the 4 th order Runge-Kutta method, to solve the governing equation, with non-linear optimization, via the built-in Solver, to determine the coefficient hc. In this work, the coefficient hc was assessed for 119 distinct recently-cooked meal samples whose temperature-time profiles were recorded in situ after 17 technical visits to the hospital kitchen over a year. The average value and standard deviation results were hc = 12.0 ± 4.1 W m−2 K −1 , whilst the lowest values (associated with the worst cooling scenarios) were about hc ≈ 6.0 W m−2 K −1 .Convective heat transfer
Almonacid S, Simpson R, Teixeira A. Heat transfer models for predicting salmonella enteriddis in shell eggs through supply chain distribution. Journal of Food Science. 2007;(9):508-E517.
2.
Amezquita A, Weller C, Wang L, Thippareddi H, Burson D. Development of an integrated model for heat transfer and dynamic growth of clostridium perfringens during the cooling of cooked, boneless ham. International Journal of Food Microbiology. 2005;(2):123–44.
3.
Amos N, Willix J, Chadderton T, North M. A compilation of correlation parameters for predicting the enthalpy and thermal conductivity of solid foods within the temperature range of -40 oC to +40 oC. International Journal of Refrigeration-revue Internationale du Froid. 2008;(7):1293–8.
4.
Barbin D, Junior V. Comparison of the effects of air flow and product arrangement on freezing process by convective heat transfer coefficient measurement. Heat Transfer e Theoretical Analysis, Experimental Investigations and Industrial Systems, ISBN. 2011;978–953.
5.
Bellara S, Mcfarlane C, Thomas C, Fryer P. The growth of escherichia coli in a food simulant during conduction cooling: combining engineering and microbiological modelling. Chemical Engineering Science. 2000;(24):6085–95.
6.
Buchanan R, Whiting R, Damert W. nd International Conference on Predictive Microbiology. Food Microbiology. 1997;(4):313–26.
7.
Cen. European standard EN 631-1: Materials and articles in contact with foodstuffs -Catering containers -Part 1: Dimensions of containers. 1993;
8.
Corradini M, Amezquita A, Normand M, Peleg M. Modeling and predicting non-isothermal microbial growth using general purpose software. International Journal of Food Microbiol-ogy. 2006;(2):223–8.
9.
Crouch E, Golden N. A risk assessment for clostridium perfringens in readyto-eat and partially cooked meat and poultry products. USDA, Food Safety Inspection Service. 2005;
10.
De Jong A, Beumer R, Zwietering M. Modeling growth of clostridium perfringens in pea soup during cooling. Risk Analysis. 2005;(1):61–73.
11.
De Souza-Santos M. Solid fuels combustion and gasification: modeling, simulation, and equipment operations. 2004;
12.
Doyle M. Survival and growth of clostridium perfringens during the cooling step of thermal processing of meat products: a review of the scientific literature. 2002;
13.
Huang L. Dynamic computer simulation of clostridium perfringens growth in cooked ground beef. International Journal of Food Microbiology. 2003;(3):217–27.
14.
Jaloustre S, Cornu M, Morelli E, Noel V, Delignette-Muller M. Bayesian modeling of clostridium perfringens growth in beef-in-sauce products. Food Microbiology. 2011;(2):311–20.
15.
Juneja V, Marks H. Predictive model for growth of clostridium perfringens during cooling of cooked cured chickens. Food Microbiology. 2002;(4):313–27.
16.
Kalinowski R, Tompkin R, Bodnaruk P, Pruett W. Impact of cooking, cooling, and subsequent refrigeration on the growth or survival of clostridium perfringens in cooked meat and poultry products. Journal of Food Protection. 2003;(7):1227–32.
17.
Kays W, Crawford M. convective heat mass transfer (3 rd. 1993;
18.
Kreyszig E, Norminton E. Maple computer manual for advanced engineering mathematics. 1993;
19.
Marcotte M, Taherian A, Karimi Y. Thermophysical properties of processed meat and poultry products. Journal of Food Engineering. 2008;(3):315–22.
20.
Norton T, Da-Wen S. Computational fluid dynamics in food processing (contemporary food engineering. 2007;1–41.
21.
Özışık M. Heat transfer: a basic approach. 1985;
22.
Rabi J, Trezzani-Harbelot I, Morelli E, Guilpart J. Catering meals under blast-cooling: comparison between zero and first-order modeling with respect to spatial dependence of temperature. International Review of Mechanical Engineering. 2012;(2):218–27.
23.
Sepúlveda D, Barbosa-Cánovas G. Heat transfer in food products. 2003;
24.
Steele F, Wright K. Cooling rate effect on outgrowth of clostridium perfringens in cooked, ready-to-eat turkey breast roasts. Poultry Science. 2001;(6):813–6.
25.
Van Der Sman R, Zhang L, Lyng J, Brunton N, Morgan D, Mckenna B. Dielectric and thermophysical properties of meat batters over a temperature range of 5-85 oC. Journal of Food Engineering. 2003;(4):173–84.
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