New Term to Quantify the Effect of Temperature on pHmin-Values Used in Cardinal Parameter Growth Models for Listeria monocytogenes

• Main Authors: Veronica Martinez-Rios, Elissavet Gkogka, Paw Dalgaard
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2019-07-01
• Series: Frontiers in Microbiology
• Subjects: predictive microbiology; mathematical modeling; model validation; product development; risk assessment; food safety
• Online Access: https://www.frontiersin.org/article/10.3389/fmicb.2019.01510/full

The aim of this study was to quantify the influence of temperature on pHmin-values of Listeria 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-function 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-function. 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 and expanded 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 1,129 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-function markedly extended the range of applicability of 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 food with low pH including chemically acidified cheese and cream cheese.