Predictive Microbiology

Question 1

What is predictive microbiology

Answer 1

Predictive microbiology integrates
traditional microbiology,
mathematical models, statistics and
information systems and technology
to estimate the behavior of
microorganisms under certain
conditions

Question 2

predictive microbiology - Concepts

Answer 2

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

Question 3

How predictive models are made

Answer 3

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.

Question 4

applications of Predictive Microbiology in industry

Answer 4

1. 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.
2. 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.
3. Incident support: estimating the impact on consumer safety or
   product quality in the case of problems with products on the market.

Question 5

Limitations of models

Answer 5

1. 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
2. The models cannot be extrapolated outside the ranges (e.g. TC, aw)
   in which they were derived.
3. The models usually predict faster growth than are observed. This
   makes them fail-safe but they be overly conservative.
4. 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.