Cardinal parameter model containing a new nisin term to predict growth of listeria monocytogenes in prosessed cheese

Veronica Martinez Rios*, Mikael Pedersen, Monica Pedrazzi, Elissavet Gkogka, Jørn Smedsgaard, Paw Dalgaard

*Corresponding author for this work

Research output: Contribution to conferenceConference abstract for conferenceResearchpeer-review

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Abstract

Introduction: Nisin is a preservative with a well-documented use for the control of sporeforming bacteria in processed cheese. However, little information is available with regards to its protective effect against pathogens such as Listeria monocytogenes, when introduced in processed cheese by cross-contamination at the consumer phase.
The objective was to develop a mathematical model to predict growth of L. monocytogenes in processed cheese containing added nisin.
Methodology: Minimum inhibitory concentration (MIC) values for nisin were determined experimentally in broth at pH 5.5 and 6.0 and collected from literature at different pH values. A polynomial MIC-function was developed to describe the effect of pH on nisin MIC values. Two existing growth and growth boundary models were expanded with the new MIC-function for nisin to predict growth of L. monocytogenes in chemically acidified cheese and processed cheese. To generate growth data for model evaluation, challenge tests (n=45) were performed with L. monocytogenes inoculated in chemically acidified cheeses and processed cheeses containing added nisin (0-25 mg/kg). A LC-MS/MS method was developed and validated to quantify nisin A and Z in cheese.
Results: The nisin recoveries ranged from 83 to 110 % for nisin A and from 95 to 113 % for nisin Z. The limits of detection and quantification for both nisin A and nisin Z were 0.04 mg/kg and 0.12 mg/kg, respectively. Applicability of the LC-MS/MS method was tested by analysing 13 different cheeses containing nisin. Five cheese samples contained nisin A at concentrations in the range from 0.16 to 0.19 mg/kg. Evaluation of the model by comparison of observed and predicted growth rates resulted in bias and accuracy factor-values of 1.02 and 1.12 for a total of 18 growth responses in processed cheese. Further studies with higher concentrations of nisin will be beneficial to validate the new nisin MIC-function including the effect of pH on nisin MIC values.
Conclusions and relevance: The developed model can be used to support product development, reformulation or risk assessment of processed cheeses containing nisin A.
Original languageEnglish
Publication date2019
Number of pages1
Publication statusPublished - 2019
Event11th International Conference on Predictive Modelling in Food (ICPMF11) - Bragança, Portugal
Duration: 17 Sep 201919 Sep 2019
Conference number: 11

Conference

Conference11th International Conference on Predictive Modelling in Food (ICPMF11)
Number11
CountryPortugal
CityBragança
Period17/09/201919/09/2019

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