Vegetative cells chain model: impact of variability on growth and inactivation

In some extent quantitative modelling can generate faster results compared to challenge tests. However, differences often occur between the prediction and the actual behaviour which are caused by several factors. One of the factors that influences this variation is strain, as different strains may have different kinetics under the same conditions. A recent work using Salmonella enterica as model organism observed extensive variation among strains with respect to their growth kinetics under unfavourable growth condition (1). In addition to that, experimental error, biological variability, cells history, and product specific effects are also amongst the factors that influence variability.

To date, growth and inactivation data are available for various pathogens and spoilage microorganisms. But, in most studies only a limited number of strains are used which makes it not possible to quantify the effect of strain variability. Quantification of this variability is important in quantitative modelling, since it can be integrated in the growth and inactivation models, to give a better quantification of the prediction interval of the models.

Aim

1.    To quantify the impact of variability on growth and inactivation kinetics

2.    To develop a generic model that integrates variability to provide a more realistic prediction of microbial dynamics in the food chain

Research

In order to get thorough information about the importance of variability on growth kinetics, a growth study using three model microorganisms, pathogenic and spoilage, was conducted using optical density measurements. Both some type strains and industrial isolates were used in this work. The effect of variability on growth rate was examined as function of pH, T, aw, and in the presence of lactic acid as a growth inhibiting compound.   

While variability on D-value was studied at 3 different temperatures,to estimate z-value with or without additional stress treatment (salt, acid, and low T). The result of the study later on will be used as input for generic model, and will be validated using 2 different food products to quantify product specific effect on variability.

The same frame work will be used for studying the variability of Salmonella Enteritidis and Lactobacillus plantarum.  In the end, step wise for new species related to the importance of variability factors can be proposed.

Expected Outcome

1.    The impact of variability is known and quantified
2.    Generic model that integrates variability is established

3.    The road map of new species is proposed

References

1. Lianou, A. and K. P. Koutsoumanis (2011). "Effect of the Growth Environment on the Strain Variability of Salmonella enterica Kinetic Behavior." Food Microbiology 28(4): 828-837.