Applied Microbiology Predictive Microbiological Models

Future models include dynamic modelling in which interaction between bacteria and environmental factors and the structure of the food. Lag modelling which models will take into account the effect of history and physiological state of the bacteria. Modelling including probability growth will address the probability of the prediction. Modelling single-cell kinetics will account for variability at a single-cell level. Relating predictive microbiology and molecular microbiology which will utilise knowledge about responses at the molecular level for instance gene expression under certain conditions.
Predictive models are used in a two-step approach for growth curves and death. In the first step, the growth or death model is established in a constant environment that is the primary model. In the second step how the parameters of the primary model are affected by other factors such as environmental factors are determined. This is the secondary model.
Predictive microbiological models are based on laboratory-generated data. Kinetic growth models allow assessment of the amount of growth that can occur. Microbiological growth in media is produced with different intrinsic parameters such as pH and salt concentration. The models are inoculated with the microorganisms of interest and a stored at a range of temperatures and the microbial level is assessed over time. Another model is growth or no growth models or time to growth models in which a different approach is to note to turbidity rather assess microbial levels. (Pearson &amp. Dutson, 1999)
The effects on microbial growth of single controlling factors include temperature, pH, water activity (aw) and acceptance that particular microbes of concern would not grow below certain temperatures. Other factors such as the composition of the atmosphere, preservative and food structure.&nbsp.

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