ABSTRACT
It is well known that the factors most affecting the growth of microorganisms are pH, storage temperature, water activity, preservatives, and modification of atmosphere during packaging (Gibson et al., 1988), among others. Given an adequate database, the response of many microbes in food can be predicted based on knowledge of the food’s formulation, processing, and storage conditions; afterward this knowledge can be applied to food product development and food safety risk assessment. There is increasing interest in modeling microbial growth as an alternative to time-consuming traditional microbiological enumeration techniques. Predictive microbiology poses several difficulties that have yet to be overcome. The first difficulty involves the complexity of the cell physiology; little is known about how it reacts to various extrinsic biochemical and environmental conditions. Second, its characteristic high biological variability must be taken into account. Moreover, the accuracy of data in accumulation techniques is poor, largely because the direct plate countthe standard method of enumeration-affords a very low degree of precision. Close to suboptimal conditions, the microorganism’s vitality undergoes drastic changes, resulting in considerable variability of the response (Jeyamkondan et al., 2001).
