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


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.


Suyuk;Staphylococcus aureus;predictive model;Baranyi;Gompertz


Supported by : 영남대학교


  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.
  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.
  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. 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.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. Fujikawa, H. and Morozumi, S. (2006) Modeling Staphylococcus aureus growth and enterotoxin production in milk. Food Microbiol. 23, 260-267.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. Karl, M. and Da-Wen, S. (1999) Predictive food microbiology for the meat industry; a review. Int. J. Food Microbiol. 52, 1-72.
  16. Korea Food and Drug Administration. Foodborne Illness Statistics. Available from: Accessed Mar. 20, 2009.
  17. Korean Dietetic Association (2007) The Standard Recipe In: A Guideline for Foodservice Management, p. 283, Seoul, Korea.
  18. 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.
  19. 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.
  20. 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.
  21. Ross, T. (1996) Indices for performance evaluation of predictive model in food microbiology. J. Appl. Bacteriol. 81, 201-508.
  22. Ross, T. (1999) Predictive food microbiology models in the meat industry. Meat and Livestock Australia, Sydney, Australia, p. 196.
  23. 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.
  24. 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.
  25. 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.
  26. Whiting, R. C. (1995) Microbial modelling in foods. Critical Rev. Food Sci. Nutr. 35, 467-494.
  27. 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.
  28. 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.
  29. 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.
  30. 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.
  31. 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.
  32. 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.
  33. 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.

Cited by

  1. Development and Validation of Predictive Model for Foodborne Pathogens in Preprocessed Namuls and Wild Root Vegetables vol.42, pp.10, 2013,
  2. Retention Factors Influencing Hanwoo Stock (broth) and Boiled Beef vol.25, pp.2, 2014,
  3. Predictive model for the growth kinetics of Listeria monocytogenes in raw pork meat as a function of temperature vol.44, 2014,
  4. Predictive modeling of Staphylococcus aureus growth on Gwamegi (semidry Pacific saury) as a function of temperature vol.56, pp.6, 2013,
  5. Developing a Predictive Model for the Shelf-life of Fish Cake vol.42, pp.5, 2013,
  6. Predictive model for the growth kinetics of Staphylococcus aureus in raw pork developed using Integrated Pathogen Modeling Program (IPMP) 2013 vol.107, 2015,
  7. Sterilization and quality variation of dried red pepper by atmospheric pressure dielectric barrier discharge plasma vol.23, pp.7, 2016,