Food Science of Animal Resources (한국축산식품학회지)

Korean Society for Food Science of Animal Resources (한국축산식품학회)
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Predictive Model for Growth of Staphylococcus aureus in Suyuk

수육에서의 Staphylococcus aureus 성장 예측모델

  • Park, Hyoung-Su (Department of Food Science & Technology, Chung-Ang University) ;
  • Bahk, Gyung-Jin (Department of Food & Nutrition, Kunsan National University) ;
  • Park, Ki-Hwan (Department of Food Science & Technology, Chung-Ang University) ;
  • Pak, Ji-Yeon (Department of Food & Nutrition, Yeungnam University) ;
  • Ryu, Kyung (Department of Food & Nutrition, Yeungnam University)
  • 박형수 (중앙대학교 식품공학과) ;
  • 박경진 (군산대학교 식품영양학과) ;
  • 박기환 (중앙대학교 식품공학과) ;
  • 박지연 (영남대학교 식품영양학과) ;
  • 류경 (영남대학교 식품영양학과)
  • Received : 2009.09.14
  • Accepted : 2010.06.09
  • Published : 2010.06.30
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Abstract

Cooked pork can be easily contaminated with Staphylococcus aureus during carriage and serving after cooking. This study was performed to develop growth prediction models of S. aureus to assure the safety of cooked pork. The Baranyi and Gompertz primary predictive models were compared. These growth models for S. aureus in cooked pork were developed at storage temperatures of 5, 15, and $25^{\circ}C$. The specific growth rate (SGR) and lag time (LT) values were calculated. The Baranyi model, which displayed a $R^2$ of 0.98 and root mean square error (RMSE) of 0.27, was more compatible than the Gompertz model, which displayed 0.84 in both $R^2$ and RMSE. The Baranyi model was used to develop a response surface secondary model to indicate changes of LT and SGR values according to storage temperature. The compatibility of the developed model was confirmed by calculating $R^2$, $B_f$, $A_f$, and RMSE values as statistic parameters. At 5, 15 and $25^{\circ}C$, $R^2$ was 0.88, 0.99 and 0.99; RMSE was 0.11, 0.24 and 0.10; $B_f$ was 1.12, 1.02 and 1.03; and $A_f$ was 1.17, 1.03 and 1.03, respectively. The developed predictive growth model is suitable to predict the growth of S. aureus in cooked pork, and so has potential in the microbial risk assessment as an input value or model.

본 연구는 수육에 쉽게 오염될 수 있는 S. aureus에 대한 성장 예측모델을 적용하고, 이를 비교하여 수육을 안전하게 관리하기 위한 적절한 모델을 제시하고자 하였다. 온도에 따른 S. aureus의 성장곡선은 5, 15, $25^{\circ}C$의 보관온도에서 측정하였다. 수육에 오염된 S. aureus의 성장결과를 기초로 온도에 따라 Baranyi model과 Gompertz model을 이용하여 SGR와 LT를 산출하였다. 두 모델에 대하여 R2과 RMSE를 산출하여 통계적인 적합성을 비교하였으며 그 결과 Baranyi model에서는 각각 0.98, 0.27, Gompertz model에서는 각각 0.84, 0.84로 나타나 Baranyi model이 온도변화에 따라 S. aureus 생육을 예측하기 위한 이차모델의 변수 값으로 사용하는데 더 적합하였다. RSM을 이용한 2차 모델에서는 $R^2$이 5, 15, $25^{\circ}C$에서 각각 0.88, 0.99, 0.99로 나타나 실험값과 예측값의 상관관계가 높았다. 또한 RMSE는 온도별로 각각 0.11, 0.24, 0.10로 나타났고, $B_f$는 각각1.12, 1.02, 1.03로, $A_f$는 각각 1.17, 1.03, 1.03로 나타나 통계적 적합성이 높다고 할 수 있다. 따라서 개발된 모델을 이용할 경우 수육의 다양한 조리환경과 온도에 따른 S. auresus 성장을 추정할 수 있으며, 이를 위해 평가에서 충분히 활용할 수 있을 것으로 보인다.

Keywords

References

  1. Baranyi, J. and Roberts, T. A. (1995) Mathematics of predictive food microbiology. Int. J. Food Microbiol. 25, 61-75.
  2. Baranyi, T., Robinson, T. P., Kaloti, A., and Mackey, B. M. (1995) Predicting growth of Brochothrix thermosphacta at changing temperature. Int. J. Food Microbiol. 27, 61-75. https://doi.org/10.1016/0168-1605(94)00154-X
  3. Baranyi, J., Ross, T., Roberts, T. 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. Bean, N. H., Goulding, J. S., Matthew, T. D., and Angulo F. J. (1997) Surveillance for foodborne disease outbreaks- United States, 1988-1992. J. Food Prot. 60, 1265-1286.
  5. Bemrah, N., Sanaa, M., Cassin, M. H., Griffiths, M. W., and Cerf, O. (1998) Quantitative risk assessment of human listeriosis from consumption of soft cheese made from raw milk. Prev. Vet. Med. 37, 129-145. https://doi.org/10.1016/S0167-5877(98)00112-3
  6. Bharathi, S., Ramesh, M. N., and Varadaraj, M. C. (2001) Predicting the behavioural pattern of Escherichia coli in minimally processed vegetables. Food Control 12, 275-284. https://doi.org/10.1016/S0956-7135(01)00008-1
  7. Castillejo-Rodriguez, A. M., Gimeno, R. M. G., Cosano, G. Z., Alcala, E. B., and Perez, M. R. R. (2002) Assessment of mathematical models for predicting Staphylococcus aureus growth in cooked meat products. J. Food Prot. 65, 659-665.
  8. Chung, M. S. (2007) Study on the risk management for risk reduction of Staphylococcus aureus in ready-to-eat foods (II). The final report of Korea Food and Drug Administration research project. Korea Health Industry Development Institute pp.157-185.
  9. Dengremont, E. and Membre, J. M. (1995) Statistical approach for comparison of the growth rates of five strains of Staphylococcus aureus. Appl. Environ. Microbiol. 61, 4389- 4395.
  10. Duffy, L. L., Vanderline, P. B., and Grau, F. H. (1994) Growth of Listeria monocytogenes on vaccum-packed cooked meats: effects of pH, Aw, nitrite and sodium ascorbate. Int. J. Food Microbiol. 23, 377-390. https://doi.org/10.1016/0168-1605(94)90164-3
  11. Eifert, J. D., Gennings, C., Carter Jr, W. H., Duncan, S. E., and Hackney, C. R. (1996) Predictive model with improved statistical analysis of interactive factors affecting the growth of Staphylococcus aureus 196E. J. Food Prot. 59, 608-614.
  12. Fujikawa, H. and Morozumi, S. (2006) Modeling Staphylococcus aureus growth and enterotoxin production in milk. Food Microbiol. 23, 260-267. https://doi.org/10.1016/j.fm.2005.04.005
  13. Fujikawa, H., Yano, K., and Morozumi, S. (2006) Model comparison for Escherichia coli growth in Pouched Food. J. Food Hyg. Soc. Japan 47, 115-118. https://doi.org/10.3358/shokueishi.47.115
  14. Gibson, A.M., Bratchell, N., and Roberts, T. A. (1988) Predicting microbial growth: growth response of Salmonella in laboratory medium as affected by pH, sodium chloride and storage temperature. Int. J. Food Microbiol. 6, 155-178. https://doi.org/10.1016/0168-1605(88)90051-7
  15. Gospavic, R., Kreyenschmidt, J., Bruckner, S., Popov, V., and Haque, N. (2008) Mathematical modelling for predicting the growth of Pseudomonas spp. in poultry under variable temperature conditions. Int. J. Food Microbiol. 127, 290- 297. https://doi.org/10.1016/j.ijfoodmicro.2008.07.022
  16. Jung, I. C., Moon, Y. H., and Kang, S. J. (2004) Effects of addition of Mugwort powder on the physicochemincal and sensory characteristics of boiled pork. Korean J. Food Sci. Ani. Resour. 24, 15-22.
  17. Kang, Y. S., Yoon, S. K., Jwa, S. H., Lee, D. H., and Woo, G. J. (2002) Prevalence of Staphylococcus aureus in Kimbap. J. Fd. Hyg. Safety 17, 31-35.
  18. Karl, M. and Da-Wen, S. (1999) Predictive food microbiology for the meat industry; a review. Int. J. Food Microbiol. 52, 1-72. https://doi.org/10.1016/S0168-1605(99)00126-9
  19. Kim E. J. (2004) Analysis of microbiological hazards and quantitative microbial risk assessment of Staphylococcus aureus inoculated onto potentially hazardous foods in school foodservice operations. MS thesis, Yonsei Univ., Seoul, Korea.
  20. Korea Food and Drug Administration. Foodborne Illness Statistics. Available from: http://www.kfda.go.kr. Accessed Mar. 20, 2009.
  21. Korean Dietetic Association (2007) The Standard Recipe In: A Guideline for Foodservice Management, p. 283, Seoul, Korea.
  22. Koseki, S. and 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
  23. Lee, H. M., Lee, G. Y., Yoon, E. K., Kim, H. J., Kang, Y. S., Lee, D. H., Park, J. S., Lee, S. H., Woo, G. J., Kang, S. H., Yang, J. S., and Yang, K. H. (2004) Computation of maximum edible time using monitoring data of Staphylococcus aureus in Kimbap and Food MicroModel. J. Fd. Hyg. Safety 19, 49-51.
  24. Lindqvist, R., Sylven, S., and Vagsholm, I. (2002) Quantitative microbial risk assessment exemplified by Staphylococcus aureus in unripened cheese made from raw milk. Int. J. Food Microbiol. 78, 144-170.
  25. Park, S. Y., Choi, J. W., Chung, D. H., Kim, M. G., Lee, K. H., Kim, K. S., Bahk, G. J., Bae, D. H., Park, S. K., Kim, K. Y., Kim, C. H., and Ha, S. D. (2007) Development of a predictive mathematical model for the growth kinetics of Listeria monocytogenes in sesame leaves. Food Sci. Biotechnol. 16, 238-242.
  26. Pereira, M. L., Carmo do, L. S., Santos dos, E. J., and Bergdoll, M. S. (1994) Staphylococcus food poisoning from cream-filled cake in metropolitan area of south-eastern Brazil. Rev. Saude Publica 28, 406-409.
  27. Ross, T. (1996) Indices for performance evaluation of predictive model in food microbiology. J. Appl. Bacteriol. 81, 201-508. https://doi.org/10.1111/j.1365-2672.1996.tb04501.x
  28. Ross, T. (1999) Predictive food microbiology models in the meat industry. Meat and Livestock Australia, Sydney, Australia, p. 196.
  29. Sutherland, J. P., Bayliss, A. J., and Robert, T. A. (1994) Predictive modelling of growth Staphylococcus aureus: the effects of temperature, pH and sodium chloride. Int. J. Food Microbiol. 21, 217-236. https://doi.org/10.1016/0168-1605(94)90029-9
  30. Tatini S, R. (1973) Influence of food environments on growth of Staphylococcus aureus and production of various enterotoxins. J. Milk Food Technol. 36, 559-563.
  31. Tirado, C. and Schimdt, K. (2001) WHO surveillance program for control of food-borne infections and intoxication: preliminary results and trends across greater Europe. J. Infect. 43, 80-84. https://doi.org/10.1053/jinf.2001.0861
  32. Whiting, R. C. (1995) Microbial modelling in foods. Critical Rev. Food Sci. Nutr. 35, 467-494. https://doi.org/10.1080/10408399509527711
  33. Yang, S. E., Yu, R. C., and Chou, C. C. (2001) Influence of holding temperature on the growth and survival of Salmonella spp. and Staphylococcus aureus and the production of Staphylococcus enterotoxin in egg products. Int. J. Food Microbiol. 63, 99-107. https://doi.org/10.1016/S0168-1605(00)00416-5

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