Two complementary approaches to quantify variability in heat resistance of spores of Bacillus subtilis

Heidy M.W. den Besten*, Erwin M. Berendsen, Marjon H.J. Wells-Bennik, Han Straatsma, Marcel H. Zwietering

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

9 Citations (Scopus)

Abstract

Realistic prediction of microbial inactivation in food requires quantitative information on variability introduced by the microorganisms. Bacillus subtilis forms heat resistant spores and in this study the impact of strain variability on spore heat resistance was quantified using 20 strains. In addition, experimental variability was quantified by using technical replicates per heat treatment experiment, and reproduction variability was quantified by using two biologically independent spore crops for each strain that were heat treated on different days. The fourth-decimal reduction times and z-values were estimated by a one-step and two-step model fitting procedure. Grouping of the 20 B. subtilis strains into two statistically distinguishable groups could be confirmed based on their spore heat resistance. The reproduction variability was higher than experimental variability, but both variabilities were much lower than strain variability. The model fitting approach did not significantly affect the quantification of variability. Remarkably, when strain variability in spore heat resistance was quantified using only the strains producing low-level heat resistant spores, then this strain variability was comparable with the previously reported strain variability in heat resistance of vegetative cells of Listeria monocytogenes, although in a totally other temperature range. Strains that produced spores with high-level heat resistance showed similar temperature range for growth as strains that produced low-level heat resistance. Strain variability affected heat resistance of spores most, and therefore integration of this variability factor in modelling of spore heat resistance will make predictions more realistic.

Original languageEnglish
Pages (from-to)48-53
JournalInternational Journal of Food Microbiology
Volume253
DOIs
Publication statusPublished - 2017

Keywords

  • Heterogeneity
  • Mathematical modelling
  • Spoilage organism
  • Sporeformer
  • Thermotolerance

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