|
[SSSP home]
Introduction to microbial spoilage (MS) models
|
Shelf-life prediction by microbial spoilage models rely on the assumption that relatively simple patterns of microbial growth and activity
exist during seafood spoilage.
It is well known that highly complex series of reactions take place during storage
of seafood but many of these changes are not related to spoilage and shelf-life. For example, enzymatic and
chemical reactions are usually responsible of the initial loss of freshness attributes
whereas microbial activity is responsible for the overt spoilage and thereby determines
product shelf-life (Dalgaard, 2000, Dalgaard 2003). Furthermore, seafood spoilage is dynamic with changes in
spoilage reactions and changes between different groups of spoilage micro-organisms
depending on product characteristics and storage conditions (Dalgaard 2000,
Dalgaard 2006). This dynamic
nature of seafood spoilage complicates the development of microbial spoilage models and
the application of these models for shelf-life prediction. However, the concept
of specific
spoilage organisms (SSO) has allowed the formulation of
microbial spoilage models (Dalgaard 2002). SSO has been defined as the part of the total
micro-flora responsible for spoilage of a given product and the spoilage domain as
the range of product characteristics and storage conditions within which a given SSO causes product rejection
(Dalgaard, 1995). The graph below shows an example of the SSO-concept. As a consequence of the simple SSO-concept shelf-life can be
predicted from:
- initial concentration of the SSO in a product
- growth rate of the SSO and
- concentration of the SSO corresponding to the minimal spoilage level.
|
 |
| Specific spoilage organism (SSO) concept. The
minimal spoilage level and the chemical spoilage index are, respectively, the numbers of SSO and the concentration of metabolites determined at the time of
sensory rejection (Dalgaard, 1993) |
|
Microbial spoilage models are typically developed by using a two step approach
as shown in the figure just below: (i) A primary growth model is fitted to
growth curve data to estimate the kinetic parameters e.g. maximum specific growth rare (µmax),
maximum cell concentration and lag time and (ii) a secondary model is then
fitted to values for the relevant kinetic parameters. Often the effect of
product characteristics and storage conditions on values of the maximum specific
growth rare (µmax) is the only secondary growth model
needed. When microbial
spoilage models are used to predict growth of microorganisms in food and thereby
product shelf-life then the effect of product characteristics and storage
conditions on kinetic parameters is determined first. Secondly, a predicted µmax-value
(and sometimes a lag time) is used together with a primary growth model to
predict how the concentration of microorganisms change over
time. |
Figure modified from Dalgaard et al. (1997) with permission from
Elsevier Ltd.
|
| The microbial spoilage models in SSSP uses the log-transformed
Logistic model as primary growth model and square-root
or polynomial models as secondary growth models. See the different
product specific MS-models in SSSP as well as Dalgaard (2002) and Ross and Dalgaard (2004) for
further details about primary and secondary growth models. |
| Microbial spoilage models must be evaluated in product validation studies
prior to use for shelf-life prediction. Accurate prediction of shelf-life
may be limited to specific seafoods or to a particular range of storage conditions.
Therefore, it is most important to determine the range of applicability for which a
microbial spoilage model is able to predicts shelf-life accurately enough for the model to be
useful in practice. The
range of applicability of microbial spoilage models depends very much on the range
of environmental factors for which a given SSO is responsible for spoilage of a seafood
i.e. the spoilage domain of the SSO. Microbial growth models can be
evaluated by comparison of predictions with data from
the literature or data from new experiments with naturally contaminated products. Observed and
predicted data can be compared by graphical methods, indices of performance like the bias-
and accuracy- factors and by direct comparison of predicted and observed shelf-life.
Clearly, comparison of the predicted shelf-life with shelf-life determined by a
sensory panel is of outmost importance for microbial models developed from
growth of a particular microorganism in a liquid laboratory medium. However, graphical methods and
indices of performance comparing observed and predicted lag times, maximum
specific growth rates (µmax) or times
for a 1000-fold increase in cell concentrations can be useful. If, for example, a microbial model
predicts a growth response that differs significantly from what is observed in seafood,
then the
model will also predict shelf-life incorrectly. Ross (1996) introduced the bias
factor and the accuracy factor for evaluation of the performance of
microbial growth models. The bias factor (Eqn. 1) indicates the systematic over- or under-prediction and
with a value of e.g. 1.2 a model predicts growth 1.2 times (20%) faster than observed in food.
In a similar way the accuracy factor (Eqn. 2) indicates the average deviation between
observed and predicted growth. Dalgaard (2002) suggested that bias factor values for seafood
spoilage micro-organism should be between 0.75 and 1.25 for a microbial spoilage model to
be successfully validated. |
Eqn.
1
Eqn.
2 |
|
