An explanation for the effect of inoculum size on MIC and the growth/no growth interface

Eva Bidlas, Tingting Du, Ronald J.W. Lambert*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)


The inoculum effect (IE) is the phenomenon observed where changes in the inoculum size used in an experiment alters the outcome with respect to, for example, the minimum inhibitory concentration of an antimicrobial or the growth/no growth boundary for a given set of environmental conditions. Various hypotheses exist as to the cause of the IE such as population heterogeneity and quorum sensing, as well as the null hypothesis - that it is artefactual. Time to detection experiments (TTD) were carried out on different initial inoculum sizes of several bacterial species (Aeromonas hydrophila, Enterobacter sakazakii, Salmonella Poona, Escherichia coli and Listeria innocua) when challenged with different pH and with combined pH and sodium acetate. Data were modelled using a modification to a Gamma model (Lambert and Bidlas 2007, Int. J. Food Microbiology 115, 204-213), taking into account the inoculum size dependency on the TTD obtained under ideal conditions. The model suggests that changes in minimum inhibitory concentration (MIC) or in the growth-no growth boundary with respect to inoculum size are due to using a smaller or larger inoculum (i.e. is directly related to microbial number) and is not due to other, suggested, phenomena. The model used further suggests that the effect of a changing inoculum size can be modelled independently of any other factor, which implies that a simple 1 to 2-day experiment measuring the TTD of various initial inocula can be used as an adjunct to currently available models.

Original languageEnglish
Pages (from-to)140-152
Number of pages13
JournalInternational Journal of Food Microbiology
Issue number1-2
Publication statusPublished - 15 Aug 2008
Externally publishedYes


  • Growth/no growth boundary
  • Inoculum effect
  • MIC
  • Predictive modelling
  • Preservation


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