Bacterial thermal death kinetics based on probability distributions: the heat destruction of Clostridium botulinum and Salmonella Bedford

J Food Prot. 2000 Sep;63(9):1197-203. doi: 10.4315/0362-028x-63.9.1197.


Despite the long history and excellent record of inactivation models used in thermal processing, there are relatively few approaches that attempt to describe the kinetics commonly observed. There are even fewer examples of models that allow the user to deal with the environmental conditions that influence these kinetics. We describe an approach that assumes a distribution of inactivation times within a population of bacterial cells. The concept allows for alternative interpretations of death kinetics and provides excellent descriptions of data generated with two important foodborne pathogens, Clostridium botulinum and Salmonella Bedford. The Salmonella Bedford data set used is unusual and perhaps unique in that it provides information where more than 50% of the population survival has been measured. These measurements are often overlooked or missed in experimental work but are essential when using a vitalistic approach, enabling calculation of a 50% lethal dose for destruction of bacteria. Use of the normal or Prentice distribution provided better fits to the data than other models commonly used to describe thermal death. There was no obvious bias in the fits even though significant tailing was evident. In addition, the procedure described allows data from all the conditions to be fitted rather than individual independent series. This enables a single equation to be derived that can be judged against the whole domain of the data. Approaches that provide accurate and unbiased descriptions of thermal death are likely to become increasingly important to ensure the safety of more marginal heat processes.

MeSH terms

  • Clostridium botulinum / growth & development*
  • Hot Temperature*
  • Hydrogen-Ion Concentration
  • Kinetics
  • Models, Biological
  • Models, Statistical
  • Probability
  • Salmonella / growth & development*
  • Statistical Distributions
  • Time Factors