Predictive Microbiology Flashcards
What is predictive microbiology
Predictive microbiology integrates
traditional microbiology,
mathematical models, statistics and
information systems and technology
to estimate the behavior of
microorganisms under certain
conditions
predictive microbiology - Concepts
Microorganisms react reproducibly (or predictably) to environmental
conditions (temperature, pH, water activity etc).
Therefore if we can measure their environment, we can predict if they
will grow, survive or die.
The goal of predictive microbiology is to develop mathematical
equations that describe the behaviour of microorganisms under different
environmental factors.
Predictive models allow the estimation of the shelf-life of foods, isolate
critical points in the production and distribution process and give
insights into how environment changes can affect the behaviour of
spoilage and pathogenic bacteria
How predictive models are made
Based on measurements of changes in microbial numbers over time
and environmental conditions
Data can be from
- Deliberately designed studies
- Data mining
- Studies in broths or in foods
Models are usually developed from experiments conducted in labs
and these models are then extrapolated to foods.
o Data are analysed and patterns of response are identified.
o These are expressed in the form of mathematical relationships.
o The relationships are turned into equations by finding the best
values of the parameters to describe individual sets of data i.e.
specific to a particular organism – this is the process of “model
fitting”.
o Equations are incorporated into “user-friendly” software.
applications of Predictive Microbiology in industry
- Product innovation: assessing speed of microbial proliferation,
growth limits, or inactivation rate associated with particular food
formulations and/or process conditions in order to develop new
products and processes, reformulate existing products, determine
storage conditions and shelf-life. - Operation support: supporting food safety decisions that need to
be made when implementing or running a food manufacturing
operation such as setting CCPs in HACCP. - Incident support: estimating the impact on consumer safety or
product quality in the case of problems with products on the market.
Limitations of models
- Models work well in defined, controlled systems with few variables.
But the environment is not always known (e.g. microenvironments
exist in foods), there are bacterial strain differences (between those
used to generate models and strains in foods) etc - The models cannot be extrapolated outside the ranges (e.g. TC, aw)
in which they were derived. - The models usually predict faster growth than are observed. This
makes them fail-safe but they be overly conservative. - The models derived in static conditions may not be applicable to
fluctuating conditions i.e. those in which environmental conditions
like temperature, pH, gaseous atmosphere change during the life of
the product.