Predictive microbiological models – what are they and how can they be used in the food industry
Why use models?
Predictive microbiological models are tools that can be used to assess product shelflife and safety. Models can also be used:
- In product development
- To identify areas where challenge testing should be undertaken
- As a tool with HACCP and risk assessment plan development
Predictive microbiological models are computer based software packages which allow the user to estimate the rate of microbial growth or get an indication of whether growth of a particular microorganism will occur under a specified set of conditions.
The models are based on laboratory generated data. Microbiological growth media (broths) are produced with different instrinsic parameters such as pH and salt level and are then inoculated with the relevant organism or cocktail of organisms. These broths are then stored at a range of temperatures and the microbial level present is assessed over time. These are known as kinetic growth models, as they allow assessment of the amount of growth that can occur.
A different approach is to note time to turbidity rather than assess microbial levels. These are growth/no growth or time to growth models, as they cannot estimate the level of growth, but simply if growth occurs.
Once the data is generated, statistical equations are fitted and these are then combined with a user-friendly interface.
Predictive models have been developed for both spoilage and pathogenic organisms and there are both growth and survival models available for use. The models will usually include the following variables:
- Temperature of storage, including fluctuating temperatures.
- Salt or equivalent water activity.
Some models also take into account levels of preservatives such as nitrite, CO2 and lactic and acetic acid.
Once parameters have been entered into the system, a prediction is produced. The prediction will usually be in the form of a growth curve (Figure 1), but parameters such as lag time, time to reach a specified microbial level and level at a specified time can also be predicted.