Food Science of Animal Resources (한국축산식품학회지)

Korean Society for Food Science of Animal Resources (한국축산식품학회)
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The Development of Predictive Growth Models for Total Viable Cells and Escherichia coli on Chicken Breast as a Function of Temperature

  • Heo, Chan (Division of Animal Life Science, Konkuk University) ;
  • Kim, Ji-Hyun (Division of Animal Life Science, Konkuk University) ;
  • Kim, Hyoun-Wook (Division of Animal Life Science, Konkuk University) ;
  • Lee, Joo-Yeon (Korea Livestock Products HACCP Management Institute) ;
  • Hong, Wan-Soo (Department of Foodservice Management and Nutrition, Sangmyung University) ;
  • Kim, Cheon-Jei (Division of Animal Life Science, Konkuk University) ;
  • Paik, Hyun-Dong (Division of Animal Life Science, Konkuk University)
  • Published : 2010.02.28
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Abstract

The aim of this research was to estimate the effect of temperature and develop predictive models for the growth of total viable cells (TVC) and Escherichia coli (EC) on chicken breast under aerobic and various temperature conditions. The primary models were determined by Baranyi model. The secondary models for the specific growth rate (SGR) and lag time (LT), as a function of storage temperature, were developed by the polynomial model. The initial contamination level of chicken breasts was around 4.3 Log CFU/g of TVC and 1.0 Log CFU/g of E. coli. During 216 h of storage, SGR of TVC showed 0.05, 0.15, and 0.54 Log CFU/g/h at 5, 15, and $25^{\circ}C$. Also, the growth tendency of EC was similar to those of TVC. As storage temperature increased, the values of SGR of microorganisms increased dramatically and the values of LT decreased inversely. The predicted growth models with experimental data were evaluated by $B_f$, $A_f$, RMSE, and $R^2$. These values indicated that these developed models were reliable to express the growth of TVC and EC on chicken breasts. The temperature changes of distribution and showcase in markets might affect the growth of microorganisms and spoilage of chicken breast mainly.

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References

  1. BAM (2003) Bacteriological Analytical Manual Online. Available from http://www.cfsan.fda.gov/-ebam. Accessed 1 March 2009.
  2. Baranyi, J. and Roberts, T. A. (1994) A dynamic approach to predicting bacterial growth in foods. Int. J. Food Microbiol. 23, 277-294. https://doi.org/10.1016/0168-1605(94)90157-0
  3. Baranyi, J., Ross, T., Roberts, A., and McMeekin, T. A. (1996) Effects of parameterization on the performance of empirical models used in 'predictive microbiology'. Food Microbiol. 13, 83-91. https://doi.org/10.1006/fmic.1996.0011
  4. Center for Disease Control and Prevention (2005) Salmonellosis: Available from http://www.cdc.gov/nczved/dfbmd/ disease_listing/salmonellosis_gi.html. Accessed 1 March 2009.
  5. Cha, S. K., Seo, M. Y., Kim, Y. S., Kim, M. H., and Kim, Y, J. (2004) The incidence of microorganisms during the slaughtering process of chicken. Korean J. Food Sci. Ani. Resour. 24, 335-341.
  6. Fujikawa, H., Kai, A., and Morozumi, S. (2004) A new logistic model for Escherichia coli growth at constant and dynamic temperature. Food Microbiol. 21, 501-509. https://doi.org/10.1016/j.fm.2004.01.007
  7. Gill, C. O., Moza, L. F., Badoni, M., and Barbut, S. (2006) The effects on the microbiological condition of product of carcass dressing, cooling, and portioning processes at a poultry packing plant. Int. J. Food Microbiol. 110, 187-193. https://doi.org/10.1016/j.ijfoodmicro.2006.04.020
  8. Gospavic, R., Kreyenschmidt, J., Bruckner, S., Popov, V., and Haque, N. (2008) Mathematical modeling for predicting the growth of Pseudomonas spp. in poultry under variable temperature conditions. Int. J. Food Microbiol. 128, 290-297.
  9. Hong, C., Sim, W., Chun, S., Kim, Y., Oh, D., Ha, S., Choi, W., and Bahk, G. (2005) Predictive growth model for native isolated Listeria monocytogenes on raw pork as a function of temperature and time. Korean J. Food Sci. Technol. 37, 850-855.
  10. Juneja, V. K., Melendres, M. V., Huang, L., Gumudavelli, V., Subbiah, J, and Thippareddi. H. (2007) Modeling the effect of temperature on growth of Salmonella in Chicken. Food Microbiol. 24, 328-335. https://doi.org/10.1016/j.fm.2006.08.004
  11. Liu, F., Guo, Y., and Li, Y. (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
  12. Michael P. D. and Beuchat L. R. (2007) Food microbiology: Fundamentals and Frontiers. 3rd ed, ASM Press, Washington,DC, pp. 971-985.
  13. MicroFit (1995) MicroFit version 1.0. Institute of Food Research, Norwich, UK.
  14. Ministry for Food Agriculture, Forestry, and Fisheries. (1999) The application manual for HACCP (MIFAFF, Notification No. 1999-29).
  15. Oh Y. and Lee, S. (2001) Hygienic quality of beef and distribution of pathogens during cut-meat processing. J. Fd. Hyg. Safety 16, 96-102.
  16. Ross, T. (1996) Indices for performance evaluation of predictive models in food microbiology. J. Appl. Bacteriol. 81, 501-508.
  17. SAS (1999) SAS/STAT Software. Release 8.1, SAS Institute Inc., Cary, NC, USA.
  18. Seo, K., Heo, S., Lee, A., Chung, D. H., Kim, M., Lee, K., Kim, K., Bahk, G., Bae, D., Kim, K., Kim, C., and Ha, S. (2007) Development of predictive mathematical model for the growth kinetics of Staphylococcus aureus by response surface model. J. Microbiol. Biotechnol. 17, 1437-1444.
  19. Thomas, J. M. and Mattthews, K. R. (2005) Food microbiology: an introduction. ASM Press, Washington, DC, pp. 247-250.
  20. Zurera-Cosano, G., Garcia-Gimeno, R. M., Rodiguez-Perez, R., and Hervas-Martinez, C. (2006) Performance of response surface model for prediction of Leuconostoc mesenteroides growth parameters under different experimental conditions. Food Control 17, 429-438. https://doi.org/10.1016/j.foodcont.2005.02.003