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FREQUENTLY ASKED QUESTIONS
Q: WHAT IS COMBASE PREDICTOR?
A:
ComBase Predictor is a predictive modelling tool comprising a
set of predictive models, including growth and thermal death models. Models can
be used for predicting the response of a range of pathogens and spoilage
microorganisms to key environmental factors (temperature, pH and salt
concentration).Some models also include an additional, fourth environmental
factor, such as the concentration of carbon dioxide or acetic acid.
Q: WHAT DOES THE MAXIMUM RATE
[log10(cfu/g)/h] MEAN?
A: The maximum rate is the maximum
slope of the “log(cell conc.) vs time” curve, in a given environment. It is
expressed as the base (10) logarithm of the numbers of cells (colony forming
units; cfu) per gram or millilitre per time unit (h; hour). The most important
environmental parameters which influence the maximum growth rate are
temperature, pH and water activity (a quantification of the water available to
the cells). Other factors such as the concentration of additives,
preservatives, etc. may also influence the growth rate.
Q: WHAT DOES THE D-VALUE MEAN?
A: The D-value is the time in
defined conditions necessary to obtain a decimal reduction of the
microorganisms being studied (i.e. to kill 90% of the organisms). It is deduced
from the death rate by the formula:
D=1/death rate
Q: WHY DON'T I GET PREDICTIONS FOR THE BACTERIAL LAG TIME?
A: Although modelling the lag time is very important, to date
predictive modelling has primarily concentrated on growth rates. This is
because lag is more difficult to model as it depends not only on the current
conditions (temperature, pH, Aw, etc) but also on the history, or physiological
state, of the cells (See Figure 1). Cells that have come from a different
environment or are damaged (for example after heat treatment or freezing) may
require more time to synthesise macromolecules and repair damage before they
can divide than undamaged cells coming from a similar environment.
In ComBase Predictor, the lag time is modelled through a dimensionless number
between 0 and 1 (‘phys state’), which expresses this physiological state. If
phys. state=0, then there is no growth, and the lag time is infinite; if phys.
state= 1, there is no lag, and growth will commence immediately. This parameter
can be set by the user.
Figure 1. The number of cells present at a given time will depend not only on
the maximum specific growth rate but also on the lag time which is
history-dependent. These growth curves are from replicate experiments, except
that the inocula were prepared differently and led to different physiological
states of the primary culture. The maximum specific growth rates are the same
while the lag periods, which depend on the history of the cells, are different.
Q: WHICH VALUE SHOULD I INPUT FOR THE PHYS. STATE ?
A: Because the user is rarely able to
provide its value, a typical value is used as default. This
means that when the input box is left empty a history, typical for the
experiments used to provide the basis of the model, is assumed for the cells.
The user might experimentally evaluate the phys. state value suitable for the
pre-incubation conditions of interest. This can be done by fitting growth
curves obtained after incubatory conditions simulating the history of the cells
(e.g. stress). Once the lag and the growth rate are calculated, the phys. state
can be deduced by the equation:
phys. state= 10^(-lag x Max.rate)
It is suggested that users try different values for the physiological state, to
study its effect on the growth curve.
Q: ON WHICH DATA ARE THE MODEL PREDICTIONS BASED?
A: The models in ComBase Predictor are based on the same data
as the previous UK predictive modelling software, Food MicroModel. However, the
original models have been improved and contain new features. Note that the
ComBase Predictor models are based on extensive experimental data obtained in
liquid culture media under well-controlled laboratory conditions. This is why
model predictions are usually ‘fail-safe’ compared with observations in food
and there can be no guarantee that predicted values will match those that would
occur in any specific food system.
A similar predictive package, Pathogen Modeling Program, based on data generated
under the funding of the Agricultural Research Service of the USDA, can be
downloaded from www.arserrc.gov/mfs/pathogen.htm.
If you have any questions regarding ComBase Predictor, please do
not hesitate to contact our helpdesk: ComBase@bbsrc.ac.uk
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