Influence of the Initial Cell Number on the Growth Fitness of Salmonella Enteritidis in Raw and Pasteurized Liquid Whole Egg, Egg White, and Egg Yolk

Modelling the low temperature growth boundaries of Salmonella Enteritidis in raw and pasteurized egg yolk, egg white and liquid whole egg: Influence of the initial concentration

Salmonella is the most frequently reported cause of foodborne outbreaks with known origin in Europe, with eggs and egg products standing out as the most frequent food source (when it was known). The growth and survival of Salmonella in eggs and egg products have been extensively studied and, recently, it has been reported that factors such as the initial concentration and thermal history of the egg product can also influence its growth capability. Therefore, the objective of this study was to define the boundary zones of the growth/no growth domain of Salmonella Enteritidis (4 strains) as a function of temperature (low temperature boundary) and the initial concentration in different egg products. A series of polynomial logistic regression equations were successfully adjusted, allowing the study of these factors and their interaction on the probability of growth of S. Enteritidis in these products. Results obtained indicate that the minimum growth temperatures of Salmonella Enteritidis are higher in egg white (9.5-18.3 °C) than in egg yolk (7.1-7.8 °C) or liquid whole egg (7.2-7.9 °C). Results also demonstrate that in raw liquid whole egg and raw and pasteurized egg white, the minimum growth temperature of Salmonella Enteritidis does depend on the initial concentration. Similarly, the previous thermal history of the egg product only influenced the minimum growth temperature in some of them. On the other hand, large differences in the minimum growth temperatures among strains were observed in some products (up to approx. 6 °C in egg white). Finally, it should be noted that none of the strains grew at 5 °C under any of the conditions assayed. Therefore, storage of egg products (particularly whole liquid egg and egg yolk) below this temperature might be regarded/proposed as a good management approach. Our experimental approach has allowed us to provide a more accurate prediction of S. Enteritidis minimum growth temperatures in egg products by taking into account additional factors (initial concentration and thermal history) while also providing a quantification of the intra-specie variability. This would be of high relevance for improving the safety of egg products.

Training in modern statistical methodologies and software tools for the definition and analysis of (stochastic) quantitative microbial risk assessment models with a comparison between the Hungarian and Spanish food supply chains

Human pathogenic Salmonella enterica strains have been infecting people since historical times. The original human pathogens, typhoid Salmonella strains (e.g. S. Typhi) played a huge role in the previous centuries but nowadays in the developed world the number of cases or outbreaks caused by these serotypes deceased due to the development of personal and public hygiene. Nowadays in these regions the animal-borne zoonotic serotypes (e.g. S. Enteritidis) became more important because of their high prevalence in intensive animal husbandry. But these bacteria can also appear in fruits and vegetables. The fellow joined the scientific work of the Polytechnic University of Cartagena, Spain about the safety of plant-based products, where he could gain experience in microbiological laboratory exercises and theoretical calculations of statistics and modelling. The activities in the laboratory were part of the research lines already established at the host institution, being based on the protocols they have already implemented. Nonetheless, the fellow had the opportunity to design his own experiment, do the experimental work required and analysed the data within the context of a qualitative microbiological risk assessment. The main focus was on the heat resistance of two strains of zoonotic Salmonella spp. at different temperatures. Experiments were done using a reference strain and an extremely resistant variant to evaluate this rare phenotype. The experiments were executed using a Mastia thermoresistometer, a device patented by the host institution that provides more control when studying thermal treatments than traditional methods. The data was analysed using the principles of predictive microbiology, using the D-value as an estimate of heat resistance that provides insight into the bacterial behaviour. For this, the fellow used the bioinactivation software, developed within the host group. Through the work and results the fellow learned the principles of quantitative microbiological risk assessment (QMRA) and predictive microbiology, which was the aim for the EU-FORA programme.

The importance of what we cannot observe: Experimental limitations as a source of bias for meta-regression models in predictive microbiology

Meta-regression models have gained in popularity during the last years as a way to create more generic models for Microbial Risk Assessments that also include variability. However, as with most meta-analyses and empirical models, systematic biases in the data can result in inaccurate models. In this article, we define experimental bias as a type of selection bias due to the practical limitations of microbial inactivation experiments. Conditions with extremely high D-values (i.e. slow inactivation) need very long experimental runs to cause significant reductions. On the other hand, when the D-value is extremely low, not enough data points can be gathered before the microbial population is below the detection limit. Consequently, experimental designs favour conditions within a practical experimental range, introducing a selection bias in the D-values. We demonstrate the impact of experimental bias in meta-regression models using numerical simulations. Models fitted to data with experimental bias overestimated the z-value and underestimated variability. We propose a rapid heuristic method to identify experimental bias in datasets, and we propose truncated regression to mitigate its impact in meta-regression models. Both methods were validated using simulated data. Thereafter the procedures were tested by building a meta-regression model for actual data for the inactivation of Bacillus cereus spores. We concluded that the dataset included experimental bias, and that it would cause an overestimation of the microbial resistance at high temperatures (>120 °C) for classical meta-regression models. This effect was mitigated when the model was built using truncated regression. In conclusion, we demonstrate that experimental bias could potentially result in inaccurate models for predictive microbiology. Therefore, checking for experimental bias should be a routine step in meta-regression modelling, and be included in guidelines on data analysis for meta-regression.