Modeling Microbial Growth Within Food Safety Risk Assessments

Abstract

Risk estimates for food-borne infection will usually depend heavily on numbers of microorganisms present on the food at the time of consumption. As these data are seldom available directly, attention has turned to predictive microbiology as a means of inferring exposure at consumption. Codex guidelines recommend that microbiological risk assessment should explicitly consider the dynamics of microbiological growth, survival, and death in foods. This article describes predictive models and resources for modeling microbial growth in foods, and their utility and limitations in food safety risk assessment. We also aim to identify tools, data, and knowledge sources, and to provide an understanding of the microbial ecology of foods so that users can recognize model limits, avoid modeling unrealistic scenarios, and thus be able to appreciate the levels of confidence they can have in the outputs of predictive microbiology models. The microbial ecology of foods is complex. Developing reliable risk assessments involving microbial growth in foods will require the skills of both microbial ecologists and mathematical modelers. Simplifying assumptions will need to be made, but because of the potential for apparently small errors in growth rate to translate into very large errors in the estimate of risk, the validity of those assumptions should be carefully assessed. Quantitative estimates of absolute microbial risk within narrow confidence intervals do not yet appear to be possible. Nevertheless, the expression of microbial ecology knowledge in "predictive microbiology" models does allow decision support using the tools of risk assessment.

Growth rate and growth probability of Listeria monocytogenes in dairy, meat and seafood products in suboptimal conditions

“…htm) were not evaluated in this study because they were shown to overestimate the growth rates in food (Pinon et al 2004). Although acceptable limits were proposed for bias and accuracy factors (Ross 1999;Dalgaard 2000;Ross and McMeekin 2003), the interpretation of these indices remained difficult because the calculated values depended largely on the formula used (Baranyi et al 1999). Models including interactions between environmental factors appeared more effective to predict the growth/no growth of the pathogen.…”

“…This phenomenon was particularly pronounced for the growth of L. monocytogenes in cold-smoked salmon, where large differences in growth rates were observed between naturally and artificially contaminated samples. The weak growth of the pathogen in naturally contaminated salmon (Cortesi et al 1997;Lappi et al 2004) was mainly attributed to the effect of the competitive microflora (Dalgaard and Jørgensen 1998;Ross et al 2000;Ross and McMeekin 2003), and models were proposed to take into account these interactions (Giménez and Dalgaard 2004).…”

The formation ofStaphylococcus aureusenterotoxin in food environments and advances in risk assessment

“…Predictive models are useful tools that can be used to estimate these changes, depending on the properties of the microorganism, and the nature of the food and the way it is handled, stored and processed. 108 However, predictive models cannot be used as the sole determinant of product safety. 109 If important decisions are to be made based on the results of a predictive model it must be validated in the food of interest.…”

Stochasticity in Colonial Growth Dynamics of Individual Bacterial Cells

“…The need for stochastic bacterial growth models has increased since the establishment of quantitative microbial risk assessment (QMRA) as the basis of food safety management (34). The use of bacterial growth models in QMRA has different demands from those of "traditional" deterministic models (13). Deterministic models are developed and validated to produce point estimates of microbial population levels.…”

“…Indeed, if, for instance, the consequences of unacceptable levels of pathogenic microorganisms in a food are grave, knowledge of only the mean population growth is unlikely to be a sufficient basis for management decisions on the safety risk. Since contamination with pathogens usually occurs with very low numbers, the development of stochastic approaches that can describe the variability of single-cell behavior is necessary for realistic estimations of safety risks (13).…”