The fact thatTminis not affected byawor by pH over the ranges tested
indicate that these factors act independently of temperature. These two
models have been combined to describe the growth of Listeria mon-
ocytogenesat sub-optimal pH,awand temperature using an equation of
the form:
kp
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ðawawminÞðpHpHminÞq
ðTTminÞð 3 : 33 ÞMathematical models of growth are not simply tools for use in
development laboratories. For instance, by being able to predict accu-
rately the response of microbial growth rate to temperature, the effect of
a fluctuating temperature environment on microbial numbers through-
out a distribution chain can be predicted. The value of the technique is
illustrated by Figure 3.19 where what might appear as slightly different
temperature histories between depot and supermarket can have a dra-
matic effect on microbial numbers.
Time–temperature function integrators are available which integrate
the temperature history of a batch of product and express it as time at
some reference temperature. If the temperature of the product remains at
the reference temperature, say 0 1 C, then they run as clocks recording
real time. If the temperature fluctuates, then they speed up or slow down
depending on whether the temperature deviates above or below the
reference temperature. The relationship between rate and temperature
used is the same as that between microbial growth rate and temperature.
So quality loss as a result of microbial growth in a fluctuating temper-
ature environment can be known with some accuracy and without the
need for microbiological testing.
Figure 3.18 Growth data forYersinia enterocoliticaplotted according to the Ratkowsky
square root model
60 Factors Affecting the Growth and Survival of Micro-organisms in Foods