Food Science of Animal Resources (한국축산식품학회지)

Korean Society for Food Science of Animal Resources (한국축산식품학회)
권호 표지
DOI QR코드

DOI QR Code

Estimation of Shelf-life of Frankfurter Using Predictive Models of Spoilage Bacterial Growth

  • Heo, Chan (Division of Animal Life Science, Konkuk University) ;
  • Choi, Yun-Sang (Division of Animal Life Science, Konkuk University) ;
  • Kim, Cheon-Jei (Division of Animal Life Science, Konkuk University) ;
  • Paik, Hyun-Dong (Division of Animal Life Science, Konkuk University)
  • Published : 2009.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

The aim of this research was to develop predictive models for the growth of spoilage bacteria (total viable cells, Pseudomonas spp., and lactic acid bacteria) on frankfurters and to estimate the shelf-life of frankfurters under aerobic conditions at various storage temperatures (5, 15, and $25^{\circ}C$). The primary models were determined using the Baranyi model equation. The secondary models for maximum specific growth rate and lag time as functions of temperature were developed by the polynomial model equation. During 21 d of storage under various temperature conditions, lactic acid bacteria showed the longest lag time and the slowest growth rate among spoilage bacteria. The growth patterns of total viable cells and Pseudomonas spp. were similar each other. These data suggest that Pseudomonas spp. might be the dominant spoilage bacteria on frankfurters. As storage temperature increased, the growth rate of spoilage bacteria also increased and the lag time decreased. Furthermore, the shelf-life of frankfurters decreased from 7.0 to 4.3 and 1.9 (d) under increased temperature conditions. These results indicate that the most significant factor for spoilage bacteria growth is storage temperature. The values of $B_f$, $A_f$, RMSE, and $R^2$ indicate that these models were reliable for identifying the point of microbiological hazard for spoilage bacteria in frankfurters.

Keywords

References

  1. BAM. (2003) Bacteriological Analytical Manual Online. Available from http://www.cfsan.fda.gov/~ebam. Accessed 10 January 2008
  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. Dainty, R. H. and Mackey, B. M. (1992) The relationship between the phenotypic properties of bacteria from chillstored meat and spoilage processes. J. Appl. Bacteriol. 73, 103-104 https://doi.org/10.1111/j.1365-2672.1992.tb03630.x
  5. Gill, C. O. and Penney, N. (1977) Penetration of bacteria into meat. Appl. Environ. Microbiol. 33, 1284-1286
  6. 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
  7. Kennedy, J., Jackson, V., Blair, I. S., McDowell, D. A., Cowan, C., and Bolton, D. J. (2005) Food safety knowledge of consumers and the microbiological and temperature status of their refrigerators. J. Food Protect. 68, 1421-1430 https://doi.org/10.4315/0362-028X-68.7.1421
  8. Koo, M. S., Kim, Y. S., Shin, D. B., Oh, S. W., and Chun, H. S. (2007) Shelf-life of prepacked kimbab and sandwiches marketed in convenience stores at refrigerated condition. J. Fd. Hyg. Safety 22, 323-331
  9. Korean Food and Drug administration. (2007) The criterion for shelf-life settlement of food (KFDA, Notification No. 2007-66)
  10. Korean Food and Drug administration. (2008) Korean food Standards Codex. pp. 73-77
  11. Korea Health Industry Development Institute (2008) Development of guideline for establishment of shelf-life of foods. The final report of Korea Food and Drug Administration research project (Project No. 2008-26). pp. 234-243
  12. Koutsoumanis, K. and Nychas, G-J. E. (2000) Application of a systematic experimental procedure to develop a microbial model for rapid fish shelf life predictions. Int. J. Food Microbiol. 60, 171-184 https://doi.org/10.1016/S0168-1605(00)00309-3
  13. Koutsoumanis, K., Stamatiou, A., Skandamis, P., and Nychas, G.-J. E. (2006) Development of a microbial model for the combined effect of temperature and pH on spoilage of ground meat and validation of the model under dynamic temperature conditions. Appl. Environ. Microbiol. 72, 124-134 https://doi.org/10.1128/AEM.72.1.124-134.2006
  14. Neumeyer, K., Ross, T., and McMeekin, T. A. (1997) Development of a predictive model to describe the effects of temperature and water activity on the growth of spoilage pseudomonads. Int. J. Food Microbiol. 38, 45-54 https://doi.org/10.1016/S0168-1605(97)00089-5
  15. Pin, C. and Baranyi, J. (1998) Predictive models means to quantify the interactions of spoilage organism. Int. J. Food Microbiol. 43, 59-72 https://doi.org/10.1016/S0168-1605(98)00035-X
  16. Lambropoulou, K. A., Drosinos, E. H., and Nychas, G-J. E. (1996) The effect of glucose supplementation on the spoilage microflora and chemical composition of minced beef stored aerobically or under a modified atmosphere at 48$^{\circ}C$. Int. J. Food Microbiol. 30, 281-291 https://doi.org/10.1016/0168-1605(96)00954-3
  17. Member, J. M. and Lambert, R. J. W. (2008) Application of predictive modeling techniques in industry: From food design up to risk assessment. Int. J. Food Microbiol. 128, 10-15 https://doi.org/10.1016/j.ijfoodmicro.2008.07.006
  18. MicroFit (1995) MicroFit version 1.0. Institute of Food Research, Norwich, UK
  19. Ross. T. (1996) Indices for performance evaluation of predictive models in food microbiology. J. Appl. Bacteriol. 81, 501-508 https://doi.org/10.1111/j.1365-2672.1996.tb03539.x
  20. SAS (2003) SAS/STAT Software. Release 9.1, SAS Institute Inc., Cary, NC, USA
  21. 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