Skip to main content
  • Original Article
  • Published:

Predictive modeling of Staphylococcus aureus growth on Gwamegi (semidry Pacific saury) as a function of temperature

Abstract

Gwamegi (semidry Pacific saury [Cololabis saira]) is a Korean food made by a traditional method of repeated freezing and de-freezing during winter. The present study aimed at developing predictive modeling of S. aureus growth on Gwamegi as a function of temperature (10–35°C). Modified Gompertz, Baranyi, and logistic primary models were fitted to experimental values. Polynomial quadratic, nonlinear Arrhenius and square root models were selected as secondary models and analyzed using specific growth rate (μmax) and lag time (λ) values obtained from the primary models. Based on the optimized models derived from the Baranyi and square root equations for μmax, its r 2 and mean square error (MSE) were 0.991 and 0.00058, and bias factor (B f) and accuracy factor (A f) were 1.0087 and 1.0801, respectively. The logistic and polynomial quadratic equations for λ, its r 2 and MSE were 0.989 and 0.22834, B f and A f were 0.9742 and 1.0271, respectively. These predictive models can provide basic information for quantitative microbial risk assessment of Gwamegi and other processed semidried seafood.

References

  • Abou-Zeid KA (2006) Development of predictive models for Listeria monocytogenes as a function of antimicrobial agents and environmental factors. Ph.D. Thesis, University of Maryland, USA.

    Google Scholar 

  • Bahk GJ, Hong CH, Oh DH, Ha SD, and Park KH (2006) Modeling the level of contamination of Staphylococcus aureus in ready-to-eat kimbab in Korea. J Food Prot 69, 1340–1346.

    Google Scholar 

  • Baranyi J and Roberts TA (1994) A dynamic approach to predicting bacterial growth in food. Int J Food Microbiol 23, 277–294.

    Article  CAS  Google Scholar 

  • Dalgaard P and Jorgensen LV (1998) Predicted and observed growth of Listeria monocytogenes in seafood challenge tests and in naturally contaminated cold-smoked salmon. Int J Food Microbiol 40, 105–115.

    Article  CAS  Google Scholar 

  • Ding T, Shim YH, Choi NJ, Ha SD, Chung MS, Hwang IG et al. (2010) Mathmatical modeling on the growth of Staphylococcus aureus in sandwich. Food Sci Biotechnol 19, 763–768.

    Article  Google Scholar 

  • Duh YH and Schaffner DW (1993) Modeling the effect of temperature on the growth rate and lag time of Listeria innocua and Listeria monocytogenes. J Food Prot 56, 205–210.

    Google Scholar 

  • Duffy LL, Vanderline PB, and Grau FH (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.

    Article  CAS  Google Scholar 

  • Fujikawa H and Morozumi S (2006) Modeling Staphylococcus aureus growth and enterotoxin production in milk. Food Microbiol 23, 260–267.

    Article  CAS  Google Scholar 

  • Gibson AM, Bratchell N, and Roberts TA (1987) The effect of sodium chloride and temperature on the rate and extent of growth of Clostridium botulinum type A in pasteurized pork slurry. J Appl Bacteriol 62, 479–490.

    Article  CAS  Google Scholar 

  • Gibson AM, Bratchell N, and Roberts TA (1988) Predicting microbial growth: Growth responses of salmonellae in a laboratory medium as affected by pH, sodium chloride and storage temperature. Int J Food Microbiol 6, 155–178.

    Article  CAS  Google Scholar 

  • Juneja VK, Melendres MV, Huang L, Gumudavelli V, Subbiah J, and Thippareddi H (2007) Modeling the effect of temperature on growth of Salmonella in chicken. Food Microbiol 34, 328–335.

    Article  Google Scholar 

  • Kaban G and Kaya M (2006) Effect of starter culture on growth of Staphylococcus aureus in sucuk. Food Control 17, 797–801.

    Article  CAS  Google Scholar 

  • Kang HS, Jeong SW, Ko JC, Jang M, and Kim JC (2011) The quality characteristics of commercial Gwmegi by product types. J Food Sci Nutr 16, 253–260.

    Article  CAS  Google Scholar 

  • Karl M and Da-Wen S (1999) Predictive food microbiology for the meat industry; a review. Int J Food Microbiol 52, 1–72.

    Article  Google Scholar 

  • Kim MW and Kim YM (2006) Isolation and identification of histamine degrading bacteria from kwamegi. J Life Science 16, 120–125.

    Article  Google Scholar 

  • Korea Food Drug Administration (2011) food and drug statistical yearbook. International Trade and Statistics Office, Korea.

    Google Scholar 

  • Le Marc Y, Valik L, and Medvedova A (2009) Modeling the effect of the starter culture on the growth of Staphylococcus aureus in milk. Int J Food Microbiol 129, 306–311.

    Article  Google Scholar 

  • Little CL, Adams MR, Anderson WA, and Cole MB (1994) Application of a log-logistic model to describe the survival of Yersinia enterocolitica at sub-optimal pH and temperature. Int J Food Microbiol 22, 63–71.

    Article  CAS  Google Scholar 

  • Mccann TL, Eifert JD, Gennings C, Schilling MW, and Carter Jr WH (2003) A predictive model with repeated measures analysis of Staphylococcus aureus growth data. Food Microbiol 20, 139–147.

    Article  Google Scholar 

  • Oh SH, Kim DJ, and Choi KH (1998) Changes in compositions of pacific saury (cololabis seira) flesh during drying for production of kwamaegi. J Korean Soc Food Sci Nutr 27, 386–392.

    CAS  Google Scholar 

  • Park HS, Bahk GJ, Park KH, Pak JY, and Ryu K (2010) Predictive model for growth of Staphylococcus aureus in suyuk. Korean J Food Sci Ani Resour 30, 487–494.

    Article  Google Scholar 

  • Ratkowsky DA, Olley J, McMeeKin TA, and Ball A (1982) Relationship between temperature and growth rate of bacterial cultures. J Bacteriol 149, 1–5.

    CAS  Google Scholar 

  • Ross T (1996) Indices for performance evaluation of predictive models in food microbiology. J Appl Bacteriol 81, 501–558.

    CAS  Google Scholar 

  • Sutherland JP, Balyliss AJ, and Roberts TA (1993) Predictive modeling of growth of Staphylococcus aureus: the effects of temperature, pH and sodium chloride. Int J Food Microbiol 21, 217–236.

    Article  Google Scholar 

  • Valero A, Pérez-Rodríguez F, Carrasco E, Fuentes-Alventosa JM, García-Gimeno RM, and Zurera G (2009) Modeling the growth boundaries of Staphylococcus aureus: effect of temperature, pH and water activity. Int J Food Microbiol 133, 186–194.

    Article  CAS  Google Scholar 

  • Yang ZQ, Jiao XA, Li P, Pan ZM, Huang JL, Gu RZ et al. (2009) Predictive model of Vibrio parahaemolyticus growth and survival on salmon meat as a function of temperature. Food Microbiol 26, 606–614.

    Article  CAS  Google Scholar 

  • Yoon KS, Min KJ, Jung YJ, Kwon KY, Lee JK, and Oh SW (2008) A model of the effect of temperature on the growth of pathogenic andnonpathogenic Vibrio parahaemolyticus isolated from oysters in Korea. Food Microbiol 25, 635–641.

    Article  CAS  Google Scholar 

  • Zhou K, Fu P, Li PL, Cheng WP, and Liang ZH (2009)Predictive modeling and validation of growth at different temperatures of Brochothrix thermosphacta. J Food safety 29, 460–473

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jong-Chan Kim.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kang, HS., Ha, SD., Jeong, SW. et al. Predictive modeling of Staphylococcus aureus growth on Gwamegi (semidry Pacific saury) as a function of temperature. J Korean Soc Appl Biol Chem 56, 731–738 (2013). https://doi.org/10.1007/s13765-013-3122-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13765-013-3122-9

Keywords