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Chapter 39 : Predictive Microbiology and Microbial Risk Assessment

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Abstract:

Quantitative microbial modeling of foods represents a proactive approach to food quality and safety by accumulating information on bacterial responses to environmental factors and by summarizing the responses through mathematical models and in databases. The first model for food-related processes, documented in the scientific literature in the 1920s, describes the relationship between heat treatment and inactivation of spores. However, it was not until the 1980s that growth and survival of microorganisms in food started to receive more focused attention. During the last 30 to 40 years, predictive microbiology has achieved status as a scientific discipline within food microbiology. In the past decade, the food safety discipline has addressed the assessment of hazards in foods within the framework of risk analysis, a science-based paradigm intended to ensure human health protection. The objectives of risk analysis are to estimate the risk to human health of a hazard associated with food consumption and, most importantly, to assess appropriate management strategies in the food chain capable of reducing the risks. Risk analysis represents a structured decision-making process with two closely connected components: risk management and risk assessment. This chapter focuses on methods of describing the unique aspects of microbial behavior in food systems using predictive microbiology and risk assessments.

Citation: Pradhan A, Mishra A, Pang H. 2019. Predictive Microbiology and Microbial Risk Assessment, p 989-1006. In Doyle M, Diez-Gonzalez F, Hill C (ed), Food Microbiology: Fundamentals and Frontiers, 5th Edition. ASM Press, Washington, DC. doi: 10.1128/9781555819972.ch39
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Figures

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Figure 39.1

Bacterial growth cycle.

Citation: Pradhan A, Mishra A, Pang H. 2019. Predictive Microbiology and Microbial Risk Assessment, p 989-1006. In Doyle M, Diez-Gonzalez F, Hill C (ed), Food Microbiology: Fundamentals and Frontiers, 5th Edition. ASM Press, Washington, DC. doi: 10.1128/9781555819972.ch39
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Image of Figure 39.2
Figure 39.2

Codex Alimentarius risk analysis framework.

Citation: Pradhan A, Mishra A, Pang H. 2019. Predictive Microbiology and Microbial Risk Assessment, p 989-1006. In Doyle M, Diez-Gonzalez F, Hill C (ed), Food Microbiology: Fundamentals and Frontiers, 5th Edition. ASM Press, Washington, DC. doi: 10.1128/9781555819972.ch39
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Image of Figure 39.3
Figure 39.3

Tornado graph showing the most important parameters and variables affecting the estimated number of illness cases per year due to consumption of O157:H7-contaminated fresh-cut lettuce. Reprinted from reference .

Citation: Pradhan A, Mishra A, Pang H. 2019. Predictive Microbiology and Microbial Risk Assessment, p 989-1006. In Doyle M, Diez-Gonzalez F, Hill C (ed), Food Microbiology: Fundamentals and Frontiers, 5th Edition. ASM Press, Washington, DC. doi: 10.1128/9781555819972.ch39
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Tables

Generic image for table
Table 39.1

Software and tools commonly used in predictive microbiology

Citation: Pradhan A, Mishra A, Pang H. 2019. Predictive Microbiology and Microbial Risk Assessment, p 989-1006. In Doyle M, Diez-Gonzalez F, Hill C (ed), Food Microbiology: Fundamentals and Frontiers, 5th Edition. ASM Press, Washington, DC. doi: 10.1128/9781555819972.ch39

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