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Highlighting the Microbial Complexity of Foodborne Hazards The Microbial Ecology in Food Safety Risk Assessment” by Tom Ross

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“The Microbial Ecology in Food Safety Risk Assessment” written by Tom Ross, sets out to highlight the microbial complexity of foodborne hazards and its subsequent impact on food safety and possible outbreaks of foodborne illness. According to the author, In a system as complex as the production and consumption of food, a variety of factors affect both the likelihood and severity of the occurrence of foodborne disease, often for which little information is currently available..
The author proposes that current risk assessments for foodborne infection, which usually depend heavily upon numbers of microorganisms present on the food at the time of consumption, present challenges to industry professionals. As he postulates, the data are
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These depend on both physiochemical conditions of the food, and the condition of storage. He highlights the main effecting factors to be considered are Aw, pH, temperature and time of storage and processing, with time being the most important.

The author strongly argues that the ability of pathogenic bacteria to grow or die under certain environmental conditions is of great importance in risk assessment. It essentially allows us to see which hazards might occur; what is the likelihood; what is the severity, in biological terms if it does occur. To predict impact and te exposure dose, microbial growth under a variety of conditions and stressors much be evaluated. He believes modeling the behavior of microorganisms in the growth/no growth region is now seen as an important component of predictive microbiology. As he comes back to throughout the chapter, bacterial pathogens are exposed to a host of stresses during their lifetime, in particular during food processing. Predictive microbial modeling, he argues, enables estimates to be made of microbial growth under varying time, temperature and intrinsic conditions or stressors. He talks again about the importance of predictive modeling as a way to quantify the combined effect of various hurdles on the probability of growth and define combinations at which growth ceases. The combination of factors applied in the form of what he
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