Preventive Nutrition and Food Science

The Korean Society of Food Science and Nutrition (한국식품영양과학회)
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Microbial Quality Change Model of Korean Pan-Fried Meat Patties Exposed to Fluctuating Temperature Conditions

  • Kim, So-Jung (Department of Food Science and Biotechnology, Kyungnam University) ;
  • An, Duck-Soon (Department of Food Science and Biotechnology, Kyungnam University) ;
  • Lee, Hyuek-Jae (Department of Information and Communication Engineering, Kyungnam University) ;
  • Lee, Dong-Sun (Department of Food Science and Biotechnology, Kyungnam University)
  • Published : 2008.12.31
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Abstract

Aerobic bacterial growth on Korean pan.fried meat patties as a primary quality deterioration factor was modeled as a function of temperature to estimate microbial spoilage on a real.time basis under dynamic storage conditions. Bacteria counts in the stretch.wrapped foods held at constant temperatures of 0, 5, 10 and $15^{\circ}C$ were measured throughout storage. The bootstrapping method was applied to generate many resampled data sets of mean microbial counts, which were then used to estimate the parameters of the microbial growth model of Baranyi & Roberts in the form of differential equations. The temperature functions of the primary model parameters were set up with confidence limits. Incorporating the temperature dependent parameters into the differential equations of bacterial growth could produce predictions closely representing the experimental data under constant and fluctuating temperature conditions.

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References

  1. Sutherland J. 2003. Modelling food spoilage. In Food Preservation Techniques. Zeuthen P, Bogh-Sorensen L, eds. Woodhead Publishing, Cambridge, UK. p 451-474
  2. McMeekin TA, Olley JN, Ross T, Ratkowsky DA. 1993. Predicted Microbiology. Research Studies Press, Somerset, UK. p 11-86
  3. Bernaerts K, Dens E, Vereecken K, Geeraerd A, Devlieghere F, Debevere J, Van Impe JF. 2004. Modeling microbial dynamics under time-varying conditions. In Modeling Microbial Responses in Food. McKellar RC, Lu X, eds. CRC Press, Boca Raton, FL, USA. p 243-261
  4. Corradini MG, Peleg M. 2005. Estimating non-isothermal bacterial growth in foods from isothermal experimental data. J Appl Microbiol 99: 187-200 https://doi.org/10.1111/j.1365-2672.2005.02570.x
  5. Van Impe JF, Nicolai BM, Schellekens M, Martens T, Baerdemaeker JD. 1995. Predictive microbiology in a dynamic environment: a system theory approach. Int J Food Microbiol 25: 227-249 https://doi.org/10.1016/0168-1605(94)00140-2
  6. Yam KL, Takhistov PT, Miltz J. 2005. Intelligent packaging: concepts and applications. J Food Sci 70: R1-10 https://doi.org/10.1111/j.1365-2621.2005.tb09052.x
  7. Lee DS, Hwang K-J, An DS, Park JP, Lee HJ. 2007. Model on the microbial quality change of seasoned soybean sprouts for on-line shelf life prediction. Int J Food Microbiol 18: 285-293 https://doi.org/10.1016/j.ijfoodmicro.2007.07.052
  8. Baranyi J, Roberts TA. 1994. A dynamic approach to predicting bacterial growth in food. Int J Food Microbiol 23: 277-294 https://doi.org/10.1016/0168-1605(94)90157-0
  9. Park JP, Lee DS. 2008. Analysis of temperature effect on microbial growth parameters and estimatation of food shelf life with confidence band. J Food Sci Nutr 13: 104-111 https://doi.org/10.3746/jfn.2008.13.2.104
  10. Zwietering MH, de Koos JT, Hasenack BE, de Wit JC, Van't Riet K. 1991. Modeling bacterial growth as a function of temperature. Appl Environ Microbiol 57: 1094-1101
  11. Koseki S, Isobe S. 2005. Prediction of pathogen growth on iceberg lettuce under real temperature history during distribution from farm to table. Int J Food Microbiol 104: 239-248 https://doi.org/10.1016/j.ijfoodmicro.2005.02.012
  12. Baranyi J, Pin C. 2001. Modelling microbiological safety. In Food Process Modelling. Tijskens LMM, Hertog MLATM, Nicolai BM, eds. Woodhead Publishing, Cambridge, UK. p 383-401
  13. Kim G-T, Ko Y-D, Lee DS. 2003. Shelf life determination of Korean seasoned side dishes. Food Sci Technol Int 9: 257-263 https://doi.org/10.1177/108201303038059
  14. Shapton DA, Shapton NF. 1994. Principles of Practices for the Safe Processing of Foods. Butterworth-Heinemann, Oxford, UK. p 388-441
  15. Motulsky H, Christopoulos A. 2004. Fitting Models to Biological Data Using Linear and Nonlinear Regression. Oxford University Press, New York, USA. p 29-137
  16. Nauta MJ. 2007. Uncertainty and variability in predictive models of microorganisms in food. In Modelling Microorganisms in Food. Brul S, Van Gerwen S, Zwietering M, eds. Woodhead Publishing, Cambridge, UK. p 44-66
  17. Liu F, Guo Y-Z, Li Y-F. 2006. Interactions of microorganisms during natural spoilage of pork at $5{\circ}C$. J Food Eng 72: 24-29 https://doi.org/10.1016/j.jfoodeng.2004.11.015
  18. Ross T, Dalgaard P. 2004. Secondary models. In Modeling Microbial Responses in Food. McKellar RC, Lu X, eds. CRC Press, Boca Raton, FL, USA. p 63-150
  19. Noriega E, Laca A, Díaz M. 2008. Modelling of diffusion-limited growth to predict Listeria distribution in structured model foods. J Food Eng 87: 247-256 https://doi.org/10.1016/j.jfoodeng.2007.11.035

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