Risk assessment of Listeria monocytogenes in ready-to-eat-foods

Predicting mycotoxins in foods: A review

Modelling bacterial growth in quantitative microbiological risk assessment: is it possible?

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.

Modeling Microbial Growth Within Food Safety Risk Assessments

Microbial food safety has been the focus of research across various disciplines within the risk analysis community. Natural scientists involved in food microbiology and related disciplines work on the identification of health hazards, and the detection of pathogenic microorganisms. To perform risk assessment, research activities are increasingly focused on the quantification of microbial contamination of food products at various stages in the food chain, and modeling the impact of this contamination on human health. Social scientists conduct research into how consumers perceive food risks, and how best to develop effective risk communication with consumers in order to improve public health through improved food handling practices. The two approaches converge at the end of the food chain, where the activities regarding food preparation and food consumption are considered. Both natural and social sciences may benefit from input and expertise from the perspective of the alternative discipline, although, to date, the integration of social and natural sciences has been somewhat limited. This article therefore explores the potential of a transdisciplinary approach to food risk analysis in terms of delivering additional improvements to public health. Developing knowledge arising from research in both the natural and social sciences, we present a novel framework involving the integration of the two approaches that might provide the most effective way to improve the consumer health associated with food-borne illness.

Improving food safety in the domestic environment: the need for a transdisciplinary approach.

Predictive microbiology and food safety.

The effect of abomasal fructo-oligosaccharides supplementation on sheep faecal microbiota was investigated in a balanced, two Latin square, cross-over design experiment. Ten fistulated sheep were managed in five consecutive periods, with each of five treatments (an ‘acidified saline’ control or one of four prebiotic candidates chosen to represent different types of oligosaccharides: Arabinogalactan, Fibruline, Raftilose, or Yacon) administered to two sheep in each period. Seven grams of fructo-oligosaccharides were used daily for each animal. In each period, fresh faecal samples were collected before, during and after ‘supplementation’ for the analysis of bifidobacteria, lactobacilli, Escherichia coli, sulphite-reducing clostridia, total anaerobes, pH and dry matter. The treatments with Fibruline, Raftilose and Yacon increased the sheep faecal bifidobacteria after 9 days of daily dosing. Compared with the control, the Raftilose treatment caused the greatest increase by 2.128 log10 CFU/g of faeces...

https://www.tandfonline.com/doi/pdf/10.1080/00288231003777657

Effect of abomasal prebiotic supplementation on sheep faecal microbiota

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Predictive microbiology and food safety.

Effect of abomasal prebiotic supplementation on sheep faecal microbiota