Advertisement

Predictive Microbiology

  • F. Devlieghere
  • K. Francois
  • A. Vermeulen
  • J. Debevere
Part of the Integrating Safety and Environmental Knowledge Into Food Studies towards European Sustainable Development book series (ISEKI-Food, volume 4)

Recent crises in the food industry have increased the awareness of the public of the food they eat. In the last few decades, dioxins and polychlorinated biphenyls (PCBs), but also microbial hazards such as Listeria monocytogenes or Bacillus cereus have reached the news headlines. The consumer has become more critical towards foods, demanding fresher, healthy, safe, and nutrition-rich food products.

One way to prove the microbial safety of a food product is by using laborious and time-consuming challenge tests. In these tests the shelf life of a specific food product can be assessed regarding spoilage and pathogenic microorganisms for a specific set of storage conditions. These methods were criticized for their expensive, time-consuming and noncumulative character (McDonald and Sun, 1999). As most food companies have an increasing number of different products, and storage conditions are different at each stage in the food chain, it is almost impossible to cover all these product/condition combinations using the classic challenge tests.

Keywords

Lactic Acid Bacterium Listeria Monocytogenes Primary Model Yersinia Enterocolitica Gompertz Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Adams, M.R., Little, C.L. and Easter, M.C. Modeling the effect of pH, acidulant and temperature on the growth rate of Yersinia enterocolitica, J. Appl. Bacteriol., 71, 65–71, 1991.Google Scholar
  2. Baranyi, J., Comparison of statistic and deterministic concepts of bacterial lag, J. Theor. Biol., 192, 403–408, 1998.CrossRefGoogle Scholar
  3. Baranyi, J. and Roberts, T.A., A dynamic approach to predicting bacterial growth in food, Int. J. Food Microbiol., 23, 277–294, 1994.CrossRefGoogle Scholar
  4. Baranyi, J., Pin, C. and Ross, T., Validating and comparing predictive models, Int. J. Food Microbiol., 48, 159–166, 1999.CrossRefGoogle Scholar
  5. Begot, C., Lebert, I. and Lebert, A., Variability of the response of 66 Listeria monocytogenes and Listeria innocua strains to different growth conditions, Food Microbiol., 14, 403–412, 1997.CrossRefGoogle Scholar
  6. Bratchell, N., Gibson, A.M., Truman, M., Kelly, T.M. and Roberts, T.A., Predicting microbial growth: the consequences of quantity of data, Int. J. Food Microbiol., 8, 47–58, 1989.CrossRefGoogle Scholar
  7. Buchanan, R.L., Whiting, R.C. and Damert, W.C., When simple is good enough: a comparison of the Gompertz, Baranyi, and three-phase linear models for fitting bacterial growth curves, Food Microbiol., 14, 313–326, 1997a.CrossRefGoogle Scholar
  8. Buchanan, R.L., Golden, M.H. and Phillips, J.G., Expanded models for the non-thermal inactiva-tion of Listeria monocytogenes, J. Appl. Microbiol., 82, 567–577, 1997b.Google Scholar
  9. Davey, K.R., A predictive model for combined temperature and water activity on the microbial growth during the growth phase, J. Appl. Bacteriol., 67, 483–488, 1989.Google Scholar
  10. Dens, E.J. and Van Impe, J.F., On the importance of taking space into account when modeling microbial competition in structured foods. Math. Comput. Simulat., 53 (4–6), 443–448, 2000.CrossRefGoogle Scholar
  11. Dens, E.J. and Van Impe, J.F., On the need for another type of predictive model in structured foods, Int. J. Food Microbiol., 64, 247–260, 2001.CrossRefGoogle Scholar
  12. Devlieghere, F., Van Belle, B. and Debevere, J., Shelf life of modified atmosphere packed cooked meat products: a predictive model, Int. J. Food Microbiol., 46, 57–70, 1999.CrossRefGoogle Scholar
  13. Devlieghere, F., Geeraerd, A.H., Versyck, K.J., Bernaert, H., Van Impe, J. and Debevere, J., Shelf life of modified atmosphere packaged cooked meat products: addition of Na-lactate as a fourth shelf life determinative factor in a model and product validation. Int. J. Food Microbiol., 58, 93–106, 2000.CrossRefGoogle Scholar
  14. Geeraerd, A.H., Valdramidis, V.P., Devlieghere, F., Bernaert, H., Debevere, J. and Van Impe, J.F., Development of a novel approach for secondary modeling in predictive microbiology: incorporation of microbiological knowledge in black box polynomial modeling, Int. J. Food Microbiol., 91 (3), 229–244, 2004.CrossRefGoogle Scholar
  15. Gibson, A.M. and Hocking, A.D., Advances in the predictive modeling of fungal growth, Trends Food Sci. Technol., 8 (11), 353–358, 1997.CrossRefGoogle Scholar
  16. Gibson, A.M., Bratchell, N. and Roberts, T.A., 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, 1987.Google Scholar
  17. Le Marc, Y., Huchet, V., Bourgeois, C.M., Guyonnet, J.P., Mafart, P. and Thuault, D., Modeling the growth kinetics of Listeria monocytogenes as a function of temperature, pH and organic acid concentration, Int. J. Food Microbiol., 73, 219–237, 2002.CrossRefGoogle Scholar
  18. Masana, M.O. and Baranyi, J., Growth/no growth interface of Brochotrix thermosphacta as a function of pH and water activity, Food Microbiol., 17, 485–493, 2000.CrossRefGoogle Scholar
  19. McDonald, K. and Sun, D.-W., Predictive food microbiology for the meat industry: a review, Int. J. Food Microbiol., 53, 1–27, 1999.CrossRefGoogle Scholar
  20. McMeekin, T.A., Chandler, R.E., Doe, P.E., Garland, C.D., Olley, J., Putro, S. and Ratkowsky, D.A., Model for the combined effect of temperature and water activity on the growth rate of Staphylococcus xylosus, J. Appl. Bacteriol., 62, 543–550, 1987.Google Scholar
  21. McMeekin, T.A., Ross, T. and Olley, J., Application of predictive microbiology to assure the quality and safety of fish and fish products, Int. J. Food Microbiol., 15, 13–32, 1992.CrossRefGoogle Scholar
  22. McMeekin, T.A., Olley, J.N., Ross, T. and Ratkowsky, D.A., Predictive microbiology — theory and application, Wiley, New York, 340 p, 1993.Google Scholar
  23. Peleg, M. and Penchina, C.M., Modeling microbial survival during exposure to a lethal agent with varying intensity, Crit. Rev. Food Sci. Nutr., 40 (2), 159–172, 2000.CrossRefGoogle Scholar
  24. Poschet, F. and Van Impe, J.F., Quantifying the uncertainty of model outputs in predictive microbiology: a Monte Carlo analysis, Med. Fac. Landbouwwet. Universiteit Gent., 64 (5), 499–506, 1999.Google Scholar
  25. Ratkowsky, D.A. and Ross, T., Modeling the bacterial growth/no growth interface, Lett. Appl. Microbiol., 20, 29–33, 1995.CrossRefGoogle Scholar
  26. Ratkowsky, D.A., Olley, J., McMeekin, T.A. and Ball, A., Relationship between temperature and growth rate of bacterial cultures, J. Bacteriol., 149 (1), 1–5, 1982.Google Scholar
  27. Ratkowsky, D.A., Lowry, R.K., McMeekin, T.A., Stokes, A.N. and Chandler, R.E., Model for bacterial culture growth rate throughout the entire biokinetic temperature range, J. Bacteriol., 154 (3), 1222–1226, 1983.Google Scholar
  28. Robinson, T.P., Aboaba, O.O., Ocio, M.J., Baranyi, J. and Mackey, B.M., The effect of inoculum size on the lag phase of Listeria monocytogenes, Int. J. Food Microbiol., 70, 163–173, 2001.CrossRefGoogle Scholar
  29. Ross, T., Indices for performance evaluation of predictive models in food microbiology, J. Appl. Bacteriol., 81, 501–508, 1996.Google Scholar
  30. Ross, T. and McMeekin, T.A., Predictive microbiology, Int. J. Food Microbiol., 23, 241–264, 1994.CrossRefGoogle Scholar
  31. Ross, T., Dalgaard, P. and Tienungoon, S., Predictive modeling of the growth and survival of Listeria in fishery products, Int. J. Food Microbiol., 62, 231–245, 2000.CrossRefGoogle Scholar
  32. Rosso, L., Lobry, J.R., Bajard, S. and Flandrois, J.P., Convenient model to describe the combined effects of temperature and pH on microbial growth, Appl. Environ. Microbiol., 61 (2), 610–616, 1995.Google Scholar
  33. Salter, M.A., Ratkowsky, D.A., Ross, T. and McMeekin, T.A., Modeling the combined temperature and salt (NaCl) limits for growth of a pathogenic Escherichia coli strain using nonlinear logistic regression, Int. J. Food Microbiol., 61, 159–167, 2000.CrossRefGoogle Scholar
  34. te Giffel, M.C. and Zwietering, M.H., Validation of predictive models describing the growth of Listeria monocytogenes, Int. J. Food Microbiol., 46, 135–149, 1999.CrossRefGoogle Scholar
  35. Tienungoon, S., Ratkowsky, D.A., McMeekin, T.A. and Ross, T. Growth limits of Listeria mono-cytogenes as a function of temperature, pH, NaCl and Lactic acid, Appl. Environ. Microbiol., 66 (11), 4979–4987, 2000.CrossRefGoogle Scholar
  36. Van Impe, J.F., Nicolaï, B.M., Martens, T., De Baerdemaeker, J. and Vandewalle, J., Dynamic mathematical model to predict microbial growth and inactivation during food processing , Appl. Environ. Microbiol., 58 (9), 2901–2909, 1992.Google Scholar
  37. Vereecken, K.M. and Van Impe, J.F., Analysis and practical implementation of a model for combined growth and metabolite production of lactic acid bacteria, Int. J. Food Microbiol., 73, 239–250, 2002.CrossRefGoogle Scholar
  38. Vereecken, K., Bernaerts, K., Boelen, T., Dens, E., Geeraerd, A., Versyck, K. and Van Impe, J., State of the art in predictive food microbiology, Med. Fac. Landbouwwet. Universiteit Gent., 63 (4), 1429–1437, 1998.Google Scholar
  39. Vereecken, K.M., Devlieghere, F., Bockstaele, A., Debevere, J. and Van Impe, J.F., A model for lactic acid-induced inhibition of Yersinia enterocolitica in mono- and coculture with Lactobacillus sakei, Food Microbiol., 20, 701–713, 2003.CrossRefGoogle Scholar
  40. Whiting, R.C. and Buchanan, R.L., A classification of models in predictive microbiology — reply, Food Microbiol., 10 (2), 175–177, 1993.CrossRefGoogle Scholar
  41. Whiting, R.C. and Buchanan, R.L., Microbial modeling, Food Technol., 48 (6), 113–120, 1994.Google Scholar
  42. Xiong, R., Xie, G., Edmondson, A.E. and Sheard, M.A., A mathematical model for bacterial inactivation, Int. J. Food Microbiol., 46, 45–55, 1999.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  • F. Devlieghere
    • 1
  • K. Francois
    • 1
  • A. Vermeulen
  • J. Debevere
    • 1
  1. 1.Department of Food Safety and Food QualityGhent UniversityGhentBelgium

Personalised recommendations