A Response Surface Model Based on Absorbance Data for the Growth Rates of Salmonella enterica Serovar Typhimurium as a Function of Temperature, NaCl, and pH

  • Park, Shin-Young (Institute of Biomedical Science, Hanyang University) ;
  • Seo, Kyo-Young (Department of Food Science and Technology, Chung-Ang University) ;
  • Ha, Sang-Do (Department of Food Science and Technology, Chung-Ang University)
  • Published : 2007.04.30

Abstract

Response surface model was developed for predicting the growth rates of Salmonella enterica sv. Typhimurium in tryptic soy broth (TSB) medium as a function of combined effects of temperature, pH, and NaCl. The TSB containing six different concentrations of NaCl (0, 2, 4, 6, 8, and 10%) was adjusted to an initial of six different pH levels (pH 4, 5, 6, 7, 8, 9, and 10) and incubated at 10 or $20^{\circ}C$. In all experimental variables, the primary growth curves were well $(r^2=0.900\;to\;0.996)$ fitted to a Gompertz equation to obtain growth rates. The secondary response surface model for natural logarithm transformations of growth rates as a function of combined effects of temperature, pH, and NaCl was obtained by SAS's general linear analysis. The predicted growth rates of the S. Typhimurium were generally decreased by basic (9, 10) or acidic (5, 6) pH levels or increase of NaCl concentrations (0-8%). Response surface model was identified as an appropriate secondary model for growth rates on the basis of coefficient determination $(r^2=0.960)$, mean square error (MSE=0.022), bias factor $(B_f=1.023)$, and accuracy factor $(A_f=1.164)$. Therefore, the developed secondary model proved reliable predictions of the combined effect of temperature, NaCl, and pH on growth rates for S. Typhimurium in TSB medium.

References

  1. Adair, C., D. C. Kilsby, and P. T. Whittall. 1989. Comparison of the School field (non-linear Arrhenius) model and the square root model for predicting bacterial growth in foods. Food Microbiol. 6: 7-18 https://doi.org/10.1016/S0740-0020(89)80033-4
  2. Bhaduri, S., C. O. Turner-Jones, R. L. Buchanan, and J. G. Phillips. 1994. Response surface models of the effect of pH, sodium chloride and sodium nitrite on growth of Yersinia enterocolitica at low temperatures. Int. J. Food Microbiol. 23: 333-343 https://doi.org/10.1016/0168-1605(94)90161-9
  3. Baumler, A. J., B. M. Hargis, and R. M. Tsoils. 2000. Tracing the origins of Salmonella outbreaks. Science 287: 50-52 https://doi.org/10.1126/science.287.5450.50
  4. Bovill, R., J. Bew, N. Cook, M. D'Agostino, N. Wilkinson, and J. Baranyi. 2000. Predictions of growth for Listeria monocytogenes and Salmonella during fluctuating temperature. Int. J. Food Microbiol. 59: 157-165 https://doi.org/10.1016/S0168-1605(00)00292-0
  5. Buchanan, R. L. 1993. Predictive food microbiology. Trends Food Sci. Technol. 4: 6-11 https://doi.org/10.1016/S0924-2244(05)80004-4
  6. Buchanan, R. L., L. K. Bagi, R. V. Goins, and J. G. Phillips. 1993. Response surface model for the growth kinetics of Escherichia coli O157:H7. Food Microbiol. 10: 303-315 https://doi.org/10.1006/fmic.1993.1035
  7. Buchanan, R. L. and J. G. Phillips. 1990. Response surface models for predicting the effects of temperature, pH, sodium chloride content, sodium nitrite concentration and atmosphere on the growth of Listeria monocytogenes. J. Food Prot. 53: 370-376 https://doi.org/10.4315/0362-028X-53.5.370
  8. Cho, S.-A., I. S. Lee, J. H. Park, S. H. Seok, H. Y. Lee, D. J. Kim, M. W. Back, S. H. Lee, S. J. Hur, S. J. Ban, Y. K. Lee, and J. H. Park. 2005. Safety and immunogenicity of Salmonella enterica serovar Typhimurium IIaB in mice. J. Microbiol. Biotechnol. 15: 609-615
  9. Choi, J. H., J. I. Choi, and S. Y. Lee. 2005. Display of proteins on the surface of Escherichia coli by C-terminal deletion fusion to the Salmonella typhimurium Omp C. J. Microbiol. Biotechnol. 15: 141-146
  10. Dalgaard, P., T. Ross, L. Kamperman, K. Neumeyer, and T. A. McMeekin. 1994. Estimation of bacterial growth rates from turbidimetric and viable count data. Int. J. Food Microbiol. 23: 391-404 https://doi.org/10.1016/0168-1605(94)90165-1
  11. Dlgaard, P., O. Mejlholm, and H. H. Huss. 1997. Application of an iterative approach for development of a microbial model predicting the shelf-life of packed fish. Int. J. Food Microbiol. 38: 169-179 https://doi.org/10.1016/S0168-1605(97)00101-3
  12. D'Aoust, J. Y. 1997. Salmonella species, pp. 129-158. In M. P. Doyle, L. R. Beuchat, and T. J. Montville (eds.), Food Microbiology: Fundamentals and Frontiers. ASM Press, Washington, D.C
  13. Duffy, L. L., P. B. Vanderlinde, and F. H. Grau. 1994. Growth of Listeria monocytogenes on vacuum-packed cooked meats: Effects of pH, $a_{w}$, nitrite and ascorbate. Int. J. Food Microbiol. 23: 377-390 https://doi.org/10.1016/0168-1605(94)90164-3
  14. El-Gazzar, F. E. and E. H. Marth. 1975. Salmonellae, salmonellosis, and dairy foods: A review. J. Dairy Sci. 75: 2327-2343
  15. Gibson, A. M., N. Bratchell, and T. A. Roberts. 1988. Predicting microbial growth: Growth responses of Salmonella in a 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
  16. GraphPad Software Inc. 2003. User's Guide. San Diego, California, U.S.A
  17. Grau, F. H. and P. B. Vanderlinede. 1993. Aerobic growth of Listeria monocytogenes on beef lean and fatty tissue: Equations describing the effects of temperature and pH. J. Food Prot. 56: 96-101 https://doi.org/10.4315/0362-028X-56.2.96
  18. Grimont, P. A., D. F. Grimont, and P. Bouvet. 2000. Taxonomy of the genus Salmonella, pp. 1-17. In C. Wray and A. Wray (eds.), Salmonella in Domestic Animals. CAB Int., Wallingford, U.K
  19. Hedberg, C. W., M. J. David, K. E. White, K. L. MacDonald, and M. T. Osterholm. 1993. Role of egg consumption in sporadic Salmonella enteritidis and Salmonella typhimurium infections in Minnesota. J. Infect. Dis. 167: 107-111 https://doi.org/10.1093/infdis/167.1.107
  20. Jung, S. J., H. J. Kim, and H. Y. Kim. 2005. Quantitative detection of Salmonella typhimurium contamination in milk, using real-time PCR. J. Microbiol. Biotechnol. 15: 1353-1358
  21. Lee, M.-J., D. H. Bae, D. H. Lee, K. H. Jang, D. H. Oh, and S. D. Ha. 2006. Reduction of Bacillus cereus in cooked rice treated with sanitizers and disinfectants. J. Microbiol. Biotechnol. 16: 639-642
  22. McClure, P. J., C. D. Blackburn, M. B. Cole, P. S. Curtis, J. E. Jones, J. D. Legan, I. D. Ogden, M. W. Peck, T. A. Roberts, J. P. Sutherland, and S. J. Walker. 1994. Modelling the growth, survival and death of microorganisms in foods: The UK Food Micromodel approach. Int. J. Food Microbiol. 23: 265-275 https://doi.org/10.1016/0168-1605(94)90156-2
  23. Mead, P. S., L. Slutsker, V Dietz, L. F. McCaig, J. S. Bresee, C. Shapiro, P. M Griffin, and R. V. Tauxe. 1999. Food-related illness and death in the United States. Emerg. Infect. Dis. 5: 607-625 https://doi.org/10.3201/eid0505.990502
  24. Mishu, B., J. Koehler, L. A. Lee, D. Rodrigue, F. H. Berenner, P. Blake, and R. V. Tauxe. 1994. Outbreaks of Salmonella enteritidis infections in the United States, 1985-1991. J. Infect. Dis. 169: 547-552
  25. Nerbrink, E., E. Borch, H. Blom, and T. Nesbakken. 1999. A model based on absorbance data on the growth rate of Listeria monocytogenes and including the effects of pH, NaCl, Na-lactate and Na-acetate. Int. J. Food Microbiol. 47: 99-109 https://doi.org/10.1016/S0168-1605(99)00021-5
  26. Neumeyer, K., T. Ross, and T. A. McMeekin. 1997. Development of a predictive model to describe the effects of temperature and water activity on the growth of spoilage Pseudomonas. Int. J. Food Microbiol. 38: 45-54 https://doi.org/10.1016/S0168-1605(97)00089-5
  27. Oscar, T. P. 1999. Response surface models for effects of temperature and previous growth sodium chloride on growth kinetics of Salmonella typhimurium on cooked chicken breast. J. Food Prot. 62: 1470-1474 https://doi.org/10.4315/0362-028X-62.12.1470
  28. Oscar, T. P. 2002. Development and validation of a tertiary simulation model for predicting growth of Salmonella typhimurium on cooked chicken. Int. J. Food Microbiol. 76: 177-190 https://doi.org/10.1016/S0168-1605(02)00025-9
  29. Oscar, T. P. 2004. A quantitative risk assessment model for Salmonella and whole chicken. Int. J. Food Microbiol. 93: 231-247 https://doi.org/10.1016/j.ijfoodmicro.2003.12.002
  30. Palwnbo, S. A., A. C. Williams, R. L. Buchanan, and J. G. Phillips. 1991. Model for the aerobic growth of Aeromonas hydrophila K144. J. Food Prot. 54: 429-435 https://doi.org/10.4315/0362-028X-54.6.429
  31. Park, S. Y., J. W. Choi, J. H. Yeon, M. J. Lee, D. H. Chung, M. G. Kim, K. H. Lee, K. S. Kim, D. H. Lee, G. J. Bahk, D. H. Bae, K. Y. Kim, C. H. Kim, and S. D. Ha. 2005. Predictive modeling for the growth of Listeria monocytogenes as a function of temperature, NaCl, and pH. J. Microbiol. Biotechnol.15: 1323-1329
  32. Ross, T. 1996. Indices for performance evaluation of predictive models in food microbiology. J. Appl. Bacteriol. 81: 501-508
  33. Ross, T. 1999. Meat and Livestock Australia, Sydney, Australia. Predictive Food Microbiology Models in the Meat Industry
  34. Ross, T., P. Dalgaard, and S. Tienungoon, 2000. Predictive modelling of the growth and survival of Listeria in fishery products. Int. J. Food Microbiol. 62: 231-245 https://doi.org/10.1016/S0168-1605(00)00340-8
  35. SAS Institute Inc. 2002. SAS User's Guide. Statistical Analysis Systems Institute, Cary, NC, U.S.A
  36. Schaffuer, D. W. and T. P. Labuza. 1997. Predictive microbiology: Analyzing the present and the future. Food Technol. 51: 95-99
  37. Skinner, G. E., J. W. Larkin, and E. J. Rhodehamel. 1994. Mathematical modeling of microbial growth: A review. J. Food Safety 14: 175-217 https://doi.org/10.1111/j.1745-4565.1994.tb00594.x
  38. Soboleva, T. K., A. B. Pleasants, and G. le Roux. 2000. Predictive microbiology and food safety. Int. J. Food Microbiol. 57: 183-192 https://doi.org/10.1016/S0168-1605(00)00265-8
  39. Sutherland, J. P., A. J. Bayliss, and T. A. Roberts. 1994. Predictive modeling of growth of 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
  40. Tietjen, M. and D. Y. C. Fung. 1995. Salmonellae and food safety. Crit. Rev. Microbiol. 21: 53-83 https://doi.org/10.3109/10408419509113534
  41. Whiting, R. C. and R. L. Buchanan. 1997. Predictive modeling, pp. 728-739. In M. P. Doyle, L. R. Beuchat, and T. J. Montville (eds.), Food Microbiology: Fundamentals and Frontiers. ASM Press, Washington, D.C
  42. Wilson, P. D. G., D. R. Wilson, T. F. Brocklehurst, H. P. Coleman, G. Mitchell, C. R. Waspe, S. A. Jukes, and M. M. Robins. 2003. Batch growth of Salmonella typhimurium LT2: Stoichiometry and factors leading to cessation of growth. Int. J. Food Microbiol. 89: 195-203 https://doi.org/10.1016/S0168-1605(03)00142-9
  43. Zurera-Cosano, G., A. M. Castillejo-Rodriguez, R. M. Garcia-Gimeno, and F. Rincon-Leon. 2004. Performance of response surface and Davey model for prediction of Staphylococcus aureus growth parameters under different experimental conditions. J. Food Prot. 67: 1138-1145 https://doi.org/10.4315/0362-028X-67.6.1138
  44. Ziprin, R. L., D. E. Corrier, A. Hinton Jr., R. C. Beier, G.. E. Spates, J. R. DeLoach, and M. H. Elissadle. 1990. Intracloacal Salmonella Typhimurium infection of broiler chickens: Reduction of colonization with anaerobic organisms and dietary lactose. Avian Dis. 34: 749-753 https://doi.org/10.2307/1591274