New term to quantify the effect of temperature on pHmin-values used in cardinal parameter growth models for Listeria monocytogenes

Veronica Martinez Rios*, Elissavet Gkogka, Paw Dalgaard

*Corresponding author for this work

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The aim of this study was to quantify the influence of temperature on pHmin-values of L. monocytogenes as used in cardinal parameter growth models and thereby improve the prediction of growth for this pathogen in food with low pH. Experimental data for L. monocytogenes growth in broth at different pH-values and at different constant temperatures were generated and used to determined pHmin-values. Additionally, pHmin-values for L. monocytogenes available from literature were collected. A new pHmin-term was developed to describe the effect of temperatures on pHmin-values obtained experimentally and from literature data. A growth and growth boundary model was developed by substituting the constant pHmin-value present in the Mejlholm and Dalgaard (2009) model (J. Food. Prot. 72, 2132–2143) by the new pHmin-term. To obtain data for low pH food, challenge tests were performed with L. monocytogenes in commercial and laboratory-produced chemically acidified cheese including glucono-delta-lactone (GDL) and in commercial cream cheese. Furthermore, literature data for growth of L. monocytogenes in products with or without GDL were collected. Evaluation of the new model by comparison of observed and predicted μmax-values resulted in a bias factor of 1.01 and an accuracy factor of 1.48 for a total of 1129 growth responses from challenge tests and literature data. Growth and no-growth responses of L. monocytogenes in seafood, meat, non-fermented dairy products and fermented cream cheese were 90.3% correctly predicted with incorrect predictions being 5.3% fail-safe and 4.4% fail-dangerous. The new pHmin-term markedly extended the range of applicability for the Mejlholm and Dalgaard (2009) model from pH 5.4 to pH 4.6 and therefore the model can now support product development, reformulation or risk assessment of low pH chemically acidified cheese and cream cheese.
Original languageEnglish
Article number1510
JournalFrontiers in Microbiology
Number of pages12
Publication statusPublished - 2019


  • predictive microbiology
  • mathematical modelling
  • model validation
  • product development
  • risk assessment
  • food safety


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