Extreme Heat Resistance of Food Borne Pathogens Campylobacter jejuni, Escherichia coli, and Salmonella typhimurium on Chicken Breast Fillet during Cooking

The aim of this research was to determine the decimal reduction times of bacteria present on chicken fillet in boiling water. The experiments were conducted with Campylobacter jejuni, Salmonella, and Escherichia coli. Whole chicken breast fillets were inoculated with the pathogens, stored overnight (4°C), and subsequently cooked. The surface temperature reached 70°C within 30 sec and 85°C within one minute. Extremely high decimal reduction times of 1.90, 1.97, and 2.20 min were obtained for C. jejuni, E. coli, and S. typhimurium, respectively. Chicken meat and refrigerated storage before cooking enlarged the heat resistance of the food borne pathogens. Additionally, a high challenge temperature or fast heating rate contributed to the level of heat resistance. The data were used to assess the probability of illness (campylobacteriosis) due to consumption of chicken fillet as a function of cooking time. The data revealed that cooking time may be far more critical than previously assumed

Data analyses and modelling for risk based monitoring of mycotoxins in animal feed

Following legislation, European Member States should have multi-annual control programs for contaminants, such as for mycotoxins, in feed and food. These programs need to be risk based implying the checks are regular and proportional to the estimated risk for animal and human health. This study aimed to prioritize feed products in the Netherlands for deoxynivalenol and aflatoxin B1 monitoring. Historical mycotoxin monitoring results from the period 2007–2016 were combined with data from other sources. Based on occurrence, groundnuts had high priority for aflatoxin B1 monitoring; some feed materials (maize and maize products and several oil seed products) and complete/complementary feed excluding dairy cattle and young animals had medium priority; and all other animal feeds and feed materials had low priority. For deoxynivalenol, maize by-products had a high priority, complete and complementary feed for pigs had a medium priority and all other feed and feed materials a low priority. Also including health consequence estimations showed that feed materials that ranked highest for aflatoxin B1 included sunflower seed and palmkernel expeller/extracts and maize. For deoxynivalenol, maize products were ranked highest, followed by various small grain cereals (products); all other feed materials were of lower concern. Results of this study have proven to be useful in setting up the annual risk based control program for mycotoxins in animal feed and feed materials

A holistic approach to food safety risks : Food fraud as an example

Production of sufficient, safe and nutritious food is a global challenge faced by the actors operating in the food production chain. The performance of food-producing systems from farm to fork is directly and indirectly influenced by major changes in, for example, climate, demographics, and the economy. Many of these major trends will also drive the development of food safety risks and thus will have an effect on human health, local societies and economies. It is advocated that a holistic or system approach taking into account the influence of multiple drivers on food safety is followed to predict the increased likelihood of occurrence of safety incidents so as to be better prepared to prevent, mitigate and manage associated risks. The value of using a Bayesian Network (BN) modelling approach for this purpose is demonstrated in this paper using food fraud as an example. Possible links between food fraud cases retrieved from the RASFF (EU) and EMA (USA) databases and features of these cases provided by both the records themselves and additional data obtained from other sources are demonstrated. The BN model was developed from 1393 food fraud cases and 15 different data sources. With this model applied to these collected data on food fraud cases, the product categories that thus showed the highest probabilities of being fraudulent were fish and seafood (20.6%), meat (13.4%) and fruits and vegetables (10.4%). Features of the country of origin appeared to be important factors in identifying the possible hazards associated with a product.The model had a predictive accuracy of 91.5% for the fraud type and demonstrates how expert knowledge and data can be combined within a model to assist risk managers to better understand the factors and their interrelationships

A holistic approach to food safety risks : Food fraud as an example

Production of sufficient, safe and nutritious food is a global challenge faced by the actors operating in the food production chain. The performance of food-producing systems from farm to fork is directly and indirectly influenced by major changes in, for example, climate, demographics, and the economy. Many of these major trends will also drive the development of food safety risks and thus will have an effect on human health, local societies and economies. It is advocated that a holistic or system approach taking into account the influence of multiple drivers on food safety is followed to predict the increased likelihood of occurrence of safety incidents so as to be better prepared to prevent, mitigate and manage associated risks. The value of using a Bayesian Network (BN) modelling approach for this purpose is demonstrated in this paper using food fraud as an example. Possible links between food fraud cases retrieved from the RASFF (EU) and EMA (USA) databases and features of these cases provided by both the records themselves and additional data obtained from other sources are demonstrated. The BN model was developed from 1393 food fraud cases and 15 different data sources. With this model applied to these collected data on food fraud cases, the product categories that thus showed the highest probabilities of being fraudulent were fish and seafood (20.6%), meat (13.4%) and fruits and vegetables (10.4%). Features of the country of origin appeared to be important factors in identifying the possible hazards associated with a product.The model had a predictive accuracy of 91.5% for the fraud type and demonstrates how expert knowledge and data can be combined within a model to assist risk managers to better understand the factors and their interrelationships.</p

Facilitating the decision-making process after a nuclear accident - case studies in the Netherlands and Slovakia.

Nuclear accidents do not occur frequently, but their biological, psychosocial, and/or economic consequences may be severe. Hence, a thorough preparation for nuclear emergencies is needed to provide appropriate actions. During the transition phase of an accident, it is vital to include stakeholders in the decision-making process in order to gain support for the recovery strategy to be implemented as well as to share different perspectives, knowledge, and views on the decision problem. Because nuclear accidents are complex, involving many relevant factors that range from technical aspects such as health effects and costs to nontechnical issues such as social acceptance, a multicriteria decision analysis (MCDA) may facilitate the decision-making process. The aim of this study was to investigate the usefulness of MCDA in the transition phase of a nuclear accident. To this end, an MCDA tool, which uses the weighted sum of a set of normalized criteria, was explored in exercises carried out in panel meetings with a selected set of (largely) governmental stakeholders. The panel meetings were performed in the Netherlands and the Slovak Republic. The exercises were based on a fictitious case study that affected the urban environment of a small city. Prior to the meetings, a set of 8 possible recovery strategies was identified. The use of the MCDA tool showed that it facilitated the decision-making process because it allowed for a structured and transparent approach in which stakeholders with diverse backgrounds can express their opinions and perspectives and reach consensus on the most appropriate recovery strategy. As such, it could be applied to a broader field of research involving any chemical release that necessitates an extended recovery strategy. Future research is needed in order to incorporate psychosocial effects of a nuclear accident as well as a broader group of stakeholders in exercises. Integr Environ Assess Manag 2021;17:376–387.</p

Changes in summer temperature.

<p>Changes (1975-1994 to 2031-2050) in mean summer (JJA) temperature (top panel) and standard deviation of summer mean monthly temperature (bottom) as projected by a) the KNMI model and b) the HC model.</p

Changes in summer precipitation.

<p>Changes (1975-1994 to 2031-2050) in mean summer (JJA) precipitation (top panel) and in the standard deviation of mean monthly precipitation (bottom) as projected by a) the KNMI model and b) the HC model.</p

Estimated future DON concentration.

<p>Estimated DON concentration (log transformed, in μg kg^-1) in the future scenario period (2031-2050) relative to the baseline period (1975-1994) using the KNMI model (left panels) and the HC (right panels) climate model data. DON contamination is expressed in both 50^th and 90^th percentile values.</p

Estimated future flowering and full maturity dates of winter wheat.

<p>Estimated flowering (a) and full maturity dates (b) as well as difference between these two dates (length of grain filling period, c) as average of each grid (n=31) for winter wheat in the baseline period (1975-1994) and the future scenario period (2031-2050) using KNMI model data and HC climate model data. Dates are expressed as Julian dates.</p