Development of a log-quadratic model to describe microbial inactivation, illustrated by thermal inactivation of Clostridium botulinum

Applied and Environmental Microbiology

Volumn 75, Issue 22, 2009, Pages 6998-7005

  Stone, G ^a   Chapman, B ^b   Lovell, D ^c  

^a MACQUARIE UNIVERSITY   (Australia)

^b CSIRO   (Australia)

^c AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

ACCIDENT PREVENTION; CLOSTRIDIUM; KINETICS; PHYSIOLOGICAL MODELS; PHYSIOLOGY;

CLOSTRIDIUM BOTULINUM; INACTIVATION KINETICS; MATHEMATICAL FRAMEWORKS; MICROBIAL INACTIVATION; MICROBIAL INACTIVATION KINETICS; MICROBIOLOGICAL SAFETY; SPORE-FORMING BACTERIA; STATISTICAL FRAMEWORK;

THERMAL LOGGING;

BACTERIUM; CONFIDENCE INTERVAL; FOOD SAFETY; MICROBIAL ACTIVITY; NUMERICAL MODEL; PHYSIOLOGY; REACTION KINETICS; SPORE;

ARTICLE; BACTERIAL SPORE; BACTERIUM CONTAMINATION; CLOSTRIDIUM BOTULINUM; CONTROLLED STUDY; DISINFECTION; FOOD CONTAMINATION; FOOD INDUSTRY; HEAT TREATMENT; STATISTICAL MODEL; TEMPERATURE SENSITIVITY;

BACTERIA (MICROORGANISMS); CLOSTRIDIUM BOTULINUM;

EID: 70449448944     PISSN: 00992240     EISSN: 10985336     Source Type: Journal    
DOI: 10.1128/AEM.01067-09     Document Type: Article

References (27)

1. Albert, I., and P. Mafart. 2005. A modified Weibull model for bacterial inactivation. Int. J. Food Microbiol. 100:197-211.
2. Anderson, W. 1997. Non-log-linear thermal inactivation of Clostridium botulinum spores. Ph.D. thesis. University of Reading, Reading, United Kingdom.
3. Anderson, W., P. J. McClure, A. C. Baird-Parker, and M. B. Cole. 1996. The application of a log-logistic model to describe the thermal inactivation of Clostridium botulinum 213B at temperatures below 121.1°C. J. Appl. Bacteriol. 80:283-290.
4. Bakalis, S., P. W. Cox, and P. J. Fryer. 2001. Modelling particular thermal technologies, p. 113-137. In P. Richardson (ed.), Thermal technologies in food processing. Woodhead Publishing, Cambridge, England.
5. Ball, C. O. 1943. Short-time pasteurization of milk. Ind. Eng. Chem. 35:71-84.
6. Cerf, O. 1977. Tailing of survival curves of bacterial spores: a review. J. Appl. Bacteriol. 42:1-19.
7. Cole, M. B., K. W. Davies, G. Munro, C. D. Holyoak, and D. C. Kilsby. 1993. A vitalistic model to describe the thermal inactivation of Listeria monocytogenes. J. Ind. Microbiol. 12:232-239.
8. Diels, A., I. Van Opstal, B. Masschalck, and C. W. Michiels. 2007. Modelling of high-pressure inactivation of microorganisms in foods, p. 161-197. In S. Brul, S. van Gerwen, and M. Zwietering (ed.), Modelling microorganisms in foods. CRC Press LLC, Boca Raton, FL, and Woodhead Publishing Limited, Cambridge, England.
9. Food Science Australia and D. Warne. 2005. Australian Quarantine Inspection Service approved persons course for thermal processing of low-acid foods. Food Science Australia, Werribee, Australia.
10. Geeraerd, A. H., C. H. Herremans, and J. F. Van Impe. 2000. Structural model requirements to describe microbial inactivation during a mild heat treatment. Int. J. Food Microbiol. 59:185-209.
11. Geeraerd, A. H., V. P. Valdramidis, and J. F. Van Impe. 2005. GInaFit, a freeware tool to assess non-log-linear microbial survivor curves. Int. J. Food Microbiol. 102:95-105. (Erratum, Int. J. Food Microbiol. 110:297, 2006.)
12. Mafart, P., O. Couvert, S. Gaillard, and I. Leguerinel. 2002. On calculating sterility in thermal preservation methods: application of the Weibull frequency distribution model. Int. J. Food Microbiol. 72:107-113.
13. Peleg, M. 1996. Evaluation of the Fermi equation as a model of dose response curves. Appl. Microbiol. Biotechnol. 46:303-306.
14. Peleg, M. 1999. On calculating sterility in thermal and non-thermal preservation methods. Food Res. Int. 32:271-278.
15. Peleg, M. 2006. Advanced quantitative microbiology for foods and biosystems: models for predicting growth and inactivation. CRC Series in Contemporary Food Science. CRC Press, Boca Raton, FL.
16. Peleg, M., and M. B. Cole. 1998. Reinterpretation of microbial survival curves. Crit. Rev. Food Sci. 38:353-380.
17. Peleg, M., M. D. Normand, and M. G. Corradini. 2005. Generating microbial survival curves during thermal processing in real time. J. Appl. Microbiol. 98:406-417.
18. Peleg, M., M. D. Normand, and E. Dramrau. 1997. Mathematical interpretation of dose-response curves. Bull. Math. Biol. 59:747-761.
19. Pflug, I. J. 1987. Factors important in determining the heat process value, FT, for low-acid canned food. J. Food Prot. 50:528-533.
20. R Development Core Team. 22 December 2008, accession date. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org/.
21. Rodriguez, A. C., G. H. Smerage, A. A. Teixeira, and F. F. Busta. 1988. Kinetic effects of lethal temperatures on population dynamics of bacterial spores. Trans. ASAE (Am. Soc. Agric. Eng.) 31:1594-1606.
22. Rodriguez, A. C., G. H. Smerage, A. A. Teixeira, J. A. Lindsay, and F. F. Busta. 1992. Population model of bacterial spores for validation of dynamic thermal processes. J. Food Sci. 57:1248-1257.
23. Sapru, V., G. H. Smerage, A. A. Teixeira, and J. A. Lindsay. 1993. Comparison of predictive models for bacteria spore population response to sterilisation temperatures. J. Food Sci. 58:223-228.
24. Stumbo, C. R., J. R. Murphy, and J. Cochran. 1950. Nature of thermal death time curves for P.A. 3679 and Clostridium botulinum. Food Technol. 4:321-326.
25. United States Food and Drug Administration. 22 December 2008, accession date. Code of Federal Regulations 21CFR108.35, Title 21, Food and Drugs, vol.2, chapter 1, Food and Drug Administration Department of Health and Human Services, subchapter B, Food for human consumption. http://www.accessdata.fda.gov/ scripts/cdrh/cfdocs/cfcfr/CFRSearch.cfm?fr=108.35.
26. Withell, E. R. 1942. The significance of the variation on shape of timesurvivor curves. J. Hyg. 42:124-183.
27. Xiong, R., G. Xie, A. E. Edmondson, and M. A. Sheard. 1999. A mathematical model for bacterial inactivation. Int. J. Food Microbiol. 46:45-55.