Tools For Prediction Of Spoilage

Although it is all well and good to be able to detect a spoiled food, it is even better if we have the tools to predict when spoilage will occur. With that information, preventative measures may in some cases be applied. More often, the information is used to determine the distribution chain that should be taken by the product in order that it be bought and consumed before spoilage takes place.

Two approaches can be taken for prediction of spoilage. In one approach, the food is subjected to conditions that would accelerate its degradation. The time it takes to reach a defined level of deterioration is then related to the time it would take for the food to spoil under common storage practices. The inherent assumption to this approach, however, is that the conditions used for acceleration (temperature, oxidative catalysts, etc) would lead mechanistically to the same type of reactions as would have occurred without the activation. Such is not always the case.

In the second approach for prediction of spoilage, the state of the food at some earlier time period is related to the time at which spoilage would occur. In this approach, either microbial or chemical attributes may be used to describe the state of the food. For example, it has been shown that a chicken carcass with a significantly higher number of P. fluorescens would not continue to be hygienically acceptable as long as one that had a fewer number of spoilage organisms at day of processing (28). On the other hand, headspace GC data for milk that underwent a preincubation (24°C, 18 h) was related to the shelf life of milk stored at refrigeration temperatures (29). In both these examples, however, the predictions would only be valid for the product and storage conditions that existed for the study developing the model. In contrast, predictive microbiology aims to take into account the variability that occurs in product and storage conditions. It is based on the premise that the responses of populations of microorganisms to environmental factors are reproducible and that by considering environments in terms of identifiable dominating constraints it is possible, from past observations, to predict the responses of those microorganisms. The necessary components to predictive microbiology therefore include determining the specific spoilage microorganisms, their spoilage domains, and their growth kinetics for which a mathematical model is derived and validated. Those factors that should be considered during the development of the models are the pH, water activity, temperature, atmosphere, preservatives, and interactions among microorganisms. Many of the models currently developed have been incorporated into a commercial software program (Food Micromodel Ltd., Leatherhead, UK) where they may be applied to broad categories of food.

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