The aim of the study was to determine a growth rate of Salmonella Enteritidis in cooked ham stored under different temperatures and to compare usefulness of the mathematical models for describing the microbiological data. The samples of cooked pork ham were inoculated with the mixture of three Salmonella Enteritidis strains and stored at 5°C, 10°C, 15°C for 21 d, and at 20°C and 25°C for 5 d. The number of salmonellae was determined at 10 periods of storage at each temperature. From each sample a series of decimal dilutions were prepared and plated onto Brilliant Green Agar. The plates were incubated at 37°C for 24-48 h under aerobic conditions. The colonies grown on culture media were counted, bacterial counts were multiplied by the appropriate dilutions, and number of bacteria (colony-forming units) was calculated. The bacterial counts were transformed into logarithms and analysed using IBM SPSS Statistics 20. The experiment was performed in five replicates. The obtained growth curves of bacteria were fitted to primary growth models, namely Gompertz, logistic, and Baranyi models. The goodness-of-fit test was evaluated by calculating mean square error and Akaike’s criterion. Growth kinetics values from the modified Gompertz and logistic equations were calculated. It was found that in samples of ham stored at 5°C and 10°C for 21 d, the number of bacteria remained almost at the same level during storage. In samples stored at 15°C, 20°C, and 25°C growth of salmonellae was observed. It was found that logistic model gave in most cases the best fit to obtained microbiological data describing the behaviour of S. Enteritidis in cooked ham. The growth kinetics values calculated in this study from logistic equations can be used to predict potential S. Enteritidis growth in cooked ham stored at 15°C, 20°C, and 25°C.
1. Baranyi J., Roberts T.A.: A dynamic approach to predicting bacterial growth in food. Int J Food Microbiol 1994, 23, 277-294.
2. Burnham K.P., Anderson D.R.: Model selection and multimodel inference: A practical information - theoretic approach. Springer-Verlag, New York, 2002.
3. The European Union summary report on trends and sources of zoonoses, zoonotic agents and food-borne outbreaks in 2011. EFSA J 2013. http:// www.efsa.europa.eu.
4. Gibson A.M., Bratchell N., Roberts T.A.: Predicting microbial growth: growth responses of salmonellae in a laboratory medium as affected by pH, sodium chloride and storage temperature. Int J Food Microbiol 1988, 6, 155-178.
5. Gill A.O., Holley R.A.: Inhibition of bacterial growth on ham and bologna by lysozyme, nisin and EDTA. Food Res Int 2000, 33, 83-90.
6. Hoang H.M., Flick D., Derens E., Alvarez G., Laguerre O.: Combined deterministic and stochastic approaches for modelling the evolution of food products along the cold chain. Part II: A case study. Int J Refrig 2012, 35, 915-926.
7. Hwang C., Sheen S., Juneja V.: Effects of sodium lactate on the survival of Listeria monocytogenes, Escherichia coli O157:H7, and Salmonella spp. in cooked ham at refrigerated and abuse temperatures. Food Nutr Sci 2011, 2, 464-470.
8. Jofré A., Garriga M., Aymerich T.: Inhibition of Salmonella sp. Listeria monocytogenes and Staphylococcus aureus in cooked ham by combining antimicrobials, high hydrostatic pressure and refrigeration. Meat Sci 2008, 78, 53-59.
9. Juneja V.K., Melendres M.V., Huang L., Subbiah J., Thippareddi H.: Mathematical modeling of growth of Salmonella in raw ground beef under isothermal conditions from 10°C to 45°C. Int J Food Microbiol 2009, 131, 106-111.
10. Juneja V.K., Melendres M.V., Huang L., Gumudavelli V., Subbiah J., Thippareddi H.: Modeling the effect of temperature on growth of Salmonella in chicken. Food Microbiol 2007, 24, 328-335.
11. Laguerre O., Derens E., Palagos B.: Study of domestic refrigerator temperature and analysis of factors affecting temperature: a French survey. Int J Refrig 2002, 25, 653-659.
12. Likar K., Jevsnik M.: Cold chain maintaining in food trade. Food Control 2006, 17, 108-113.
13. López S., Prieto M., Dijkstra J., Ghanoa M.S., France J.: Statistical evaluation of mathematical models for microbial growth. Int J Food Microbiol 2004, 96, 289-300.
14. McKellar R.C., Lu X.: Primary models. In: Modeling microbial responses in food, edited by R.C. McKellar, X. Lu, CRC Press LLC, Boca Raton, 2004, pp. 21-62.
15. Polish Standard: PN-A-82007 Meat products.
16. Ratkowsky D.A., Olley J., McMeekin T.A., Ball A.: Relation between temperature and growth rate of bacterial cultures. J Bacteriol 1982, 149, 1-5.
17. Szczawiński J.: Predictive microbiology: Practical applications. Med Weter 2012, 68, 540-543.
18. Szczawiński J., Klusek A., Szczawińska M. E.: Growth responses of Salmonella Enteritidis subjected to heat or high pressure treatment in a laboratory medium. High Press Res 2009, 29, 141-149.
19. Szczawiński J., Klusek A., Szczawińska M.E.. Parameters of growth curves of Salmonella Enteritidis subjected to conventional heat or microwave treatment. Bull Vet Inst Pulawy 2009, 53, 627-632.
20. The International Commission on Microbiological Specifications for foods, “Salmonellae”; in: T.A. Roberts, A.C. Baird-Parker and R.B. Tompkin, Eds., Microrganisms in Foods 5. Characteristics of Microbial Pathogens, Blackie Academic and Professional, London, 1996, p. 225.
21. Zaika L.L., Phillips J.G., Fanelli J.S., Scullen O.J.: Revised model for aerobic growth of Shigella flexneri to extend the validity of predictions at temperatures between 10 and 19°C. Int J Food Microbiol 1998, 41, 9-19.
22. Zwietering M.H., Jongenburger I., Rombouts F.M., van’t Riet K.: Modeling of the bacterial growth curve. Appl Environ Microbiol 1990, 56, 1875-1881.