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Proceeding of the 4 rd International Symposium“NEW RESEARCH IN BIOTECHNOLOGY” USAMV Bucharest, Romania, 2011If tLis the latent period (in hours), and r is the rate of microbial growth, there arethe following relationships:Table 1. Intervals beef carcass cooling according to different values of temperatureand microbial growth rateTtLrT < 7t = 0r =07 ≤ T < 30o o 0,001T2 − 0,136T+ 2,841t L1030 ≤ T < 40o o 0,001T2 − 0,136T+ 2,841t L1040 ≤ T < 45o o 0,001T2 − 0,136T+ 2,841t L10o45 ≤ T < 47oT > 47oL= r = 0,0513⋅T− 0,17= r = 0,027 ⋅ T + 0,55= r = 2,66tL= ∞ r = 2,66tL= ∞ r = 0The meat tenderness is measured by a z value, ranging from 1 (strong meat) before coolingto 0. Z is the standard objective function, while the penalty function is g. These expressionsare combined as the objective function. The optimization is then carried out routinely usingan evolutionary program.The objective function and penalty function are formed in the normal manner. Theair temperature is gradually reduced from 15 ° C to 0.8 ° C, then increased back to 5.3 ° Cin the last hours. This system provides a nearly linear decrease in average temperature ofthe rear leg and the final temperature is 14 ° C. The tenderizers degree is normalized from0252-0194 with a significant decrease of 23%.4. CONCLUSIONSThrough this work we wanted to present in a more accessible form the general aspectsof mathematical modeling and optimization techniques that are used increasingly often inthe food industry. The two applications presented above are examples in this sense. In thefirst problem analyzed, we consider the temperature variations in the block of meat to bethawed are high because the edges and corners are heated more quickly and if we considerthe random errors of measurement due to deviations or registration, we believe thatstochastic modeling would be a more appropriate method to optimize this process. In thesecond problem studied, we wanted to model a system of temperature, given theconstraints and maximizing the tenderness of the loin muscle and reducing microbialgrowth rate.For both problems we could apply both methods in parallel: the evolutionary methodand the stochastic procedure.110
Proceeding of the 4 rd International Symposium“NEW RESEARCH IN BIOTECHNOLOGY” USAMV Bucharest, Romania, 2011We intend in the future to apply the stochastic modeling for refrigeration processes,in order to compare the results obtained by this technique with those obtained byevolutionary method.REFERENCES1. Bausic, F. (2001): Fundamentals of modeling, Bucharest, Matrix Rom, p. 22-26.2. Gill, C.O. (1993): Assessment of the hygienic efficiencies of processes for cooling meat atslaughtering plants, Agriculture Canada Technical Bulletin 1993-10E.3. Gorunescu, F., Prodan, A. (2001): Stochastic modeling and simulation, Cluj-Napoca, Ed Blue. p.33-37.4. Graafhuis, A.E., Lovatt, S.J. and Devine, C.E. (1992): A predictive model for lamb tenderness.Proc 27th New Zealand Meat Ind. Res. Conf. (Hamilton), p.143-147.5. Mihoc, Gh., Nădejde, I. (1967): Mathematical Programming, Stochastic Programming , ScientificalEd. , Bucharest, p.30-38.6. Pham, Q.T. (1995): Competitive evolution: a natural approach to operator selection. In: Progress inEvolutionary Computation, Lecture Notes in Artificial Intelligence, Springer-Verlag, Heidelberg ,Vol. 956, p.49-60.7. Varga, M., Matei, F. (2009): The application of statistical modeling tools to predict the growthevolution of foodborn moulds, Romanian Biotechnological Letters, Bucharest, Vol.14, p.4249-4259.111
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