Risk‐Based Approach for Microbiological Food Safety Management in the Dairy Industry: The Case of Listeria monocytogenes in Soft Cheese Made from Pasteurized Milk

According to Codex Alimentarius Commission recommendations, management options applied at the process production level should be based on good hygiene practices, HACCP system, and new risk management metrics such as the food safety objective. To follow this last recommendation, the use of quantitative microbiological risk assessment is an appealing approach to link new risk‐based metrics to management options that may be applied by food operators. Through a specific case study, Listeria monocytogenes in soft cheese made from pasteurized milk, the objective of the present article is to practically show how quantitative risk assessment could be used to direct potential intervention strategies at different food processing steps. Based on many assumptions, the model developed estimates the risk of listeriosis at the moment of consumption taking into account the entire manufacturing process and potential sources of contamination. From pasteurization to consumption, the amplification of a primo‐contamination event of the milk, the fresh cheese or the process environment is simulated, over time, space, and between products, accounting for the impact of management options, such as hygienic operations and sampling plans. A sensitivity analysis of the model will help orientating data to be collected prioritarily for the improvement and the validation of the model. What‐if scenarios were simulated and allowed for the identification of major parameters contributing to the risk of listeriosis and the optimization of preventive and corrective measures.     

Consumer-Phase Salmonella enterica serovar Enteritidis Risk Assessment for Egg-Containing Food Products

We describe a one-dimensional probabilistic model of the role of domestic food handling behaviors on salmonellosis risk associated with the consumption of eggs and egg-containing foods. Six categories of egg-containing foods were defined based on the amount of egg contained in the food, whether eggs are pooled, and the degree of cooking practiced by consumers. We used bootstrap simulation to quantify uncertainty in risk estimates due to sampling error, and sensitivity analysis to identify key sources of variability and uncertainty in the model. Because of typical model characteristics such as nonlinearity, interaction between inputs, thresholds, and saturation points, Sobol's method, a novel sensitivity analysis approach, was used to identify key sources of variability. Based on the mean probability of illness, examples of foods from the food categories ranked from most to least risk of illness were: (1) home-made salad dressings/ice cream; (2) fried eggs/boiled eggs; (3) omelettes; and (4) baked foods/breads. For food categories that may include uncooked eggs (e.g., home-made salad dressings/ice cream), consumer handling conditions such as storage time and temperature after food preparation were the key sources of variability. In contrast, for food categories associated with undercooked eggs (e.g., fried/soft-boiled eggs), the initial level of Salmonella contamination and the log10 reduction due to cooking were the key sources of variability. Important sources of uncertainty varied with both the risk percentile and the food category under consideration. This work adds to previous risk assessments focused on egg production and storage practices, and provides a science-based approach to inform consumer risk communications regarding safe egg handling practices.     

Communicating about the Risks and Benefits of Genetically Modified Foods: The Mediating Role of Trust

Recent research suggests that public attitudes toward emerging technologies are mainly driven by trust in the institutions promoting and regulating these technologies. Alternative views maintain that trust should be seen as a consequence rather than a cause of such attitudes. To test its actual role, direct as well as mediating effects of trust were tested in an attitude change experiment involving 1,405 consumers from Denmark, Germany, Italy, and the United Kingdom. After prior attitudes to genetic modification in food production had been assessed, participants received different information materials (either product-specific information or balanced/general information about genetic modification in food production) and were asked to evaluate different types of genetically modified foods (either beer or yoghurt). The information materials were attributed to different information sources (either an industry association, a consumer organization, or a government source). After completion, perceived risk and perceived benefit were assessed, and participants indicated their trust in the information sources to which the materials had been attributed. Direct and trust-mediated attitude change effects were estimated in a multi-sample structural equation model. The results showed that information provision had little effect on people's attitudes toward genetically modified foods, and that perceptions of information source characteristics contributed very little to attitude change. Furthermore, the type of information strategy adopted had almost no impact on postexperimental attitudes. The extent to which people trusted the information sources appeared to be driven by people's attitudes to genetically modified foods, rather than trust influencing the way that people reacted to the information. Trust was not driving risk perception-rather, attitudes were informing perceptions of the motivation of the source providing the information.     

A Practical Framework for the Construction of a Biotracing Model: Application to Salmonella in the Pork Slaughter Chain

A novel purpose of the use of mathematical models in quantitative microbial risk assessment (QMRA) is to identify the sources of microbial contamination in a food chain (i.e., biotracing). In this article we propose a framework for the construction of a biotracing model, eventually to be used in industrial food production chains where discrete numbers of products are processed that may be contaminated by a multitude of sources. The framework consists of steps in which a Monte Carlo model, simulating sequential events in the chain following a modular process risk modeling (MPRM) approach, is converted to a Bayesian belief network (BBN). The resulting model provides a probabilistic quantification of concentrations of a pathogen throughout a production chain. A BBN allows for updating the parameters of the model based on observational data, and global parameter sensitivity analysis is readily performed in a BBN. Moreover, a BBN enables “backward reasoning” when downstream data are available and is therefore a natural framework for answering biotracing questions. The proposed framework is illustrated with a biotracing model of Salmonella in the pork slaughter chain, based on a recently published Monte Carlo simulation model. This model, implemented as a BBN, describes the dynamics of Salmonella in a Dutch slaughterhouse and enables finding the source of contamination of specific carcasses at the end of the chain.     

The Role of Similarity Cues in the Development of Trustin Sources of Information About GM Food

In evaluating complex new technologies, people are usually dependent on information provided by others, for example, experts or journalists, and have to determine whether they can trust these information sources. This article focuses on similarity as the basis for trust. The first experiment ( N  = 261) confirmed that a journalist writing about genetically modified (GM) food was trusted more when his attitude was congruent with that of his readers. In addition, the experiment showed that this effect was mediated by the perceived similarity of the journalist. The second experiment ( N  = 172) revealed that trust in a journalist writing about the focal domain of GM food was even influenced by him expressing a congruent attitude in an unrelated domain. This result supports a general similarity account of the congruence effect on trust, as opposed to a confirmatory bias account.     

A Bayesian Approach to Quantify the Contribution of Animal-Food Sources to Human Salmonellosis

Based on the data from the integrated Danish Salmonella surveillance in 1999, we developed a mathematical model for quantifying the contribution of each of the major animal-food sources to human salmonellosis. The model was set up to calculate the number of domestic and sporadic cases caused by different Salmonella sero and phage types as a function of the prevalence of these Salmonella types in the animal-food sources and the amount of food source consumed. A multiparameter prior accounting for the presumed but unknown differences between serotypes and food sources with respect to causing human salmonellosis was also included. The joint posterior distribution was estimated by fitting the model to the reported number of domestic and sporadic cases per Salmonella type in a Bayesian framework using Markov Chain Monte Carlo simulation. The number of domestic and sporadic cases was obtained by subtracting the estimated number of travel- and outbreak-associated cases from the total number of reported cases, i.e., the observed data. The most important food sources were found to be table eggs and domestically produced pork comprising 47.1% (95% credibility interval, CI: 43.3-50.8%) and 9% (95% CI: 7.8-10.4%) of the cases, respectively. Taken together, imported foods were estimated to account for 11.8% (95% CI: 5.0-19.0%) of the cases. Other food sources considered had only a minor impact, whereas 25% of the cases could not be associated with any source. This approach of quantifying the contribution of the various sources to human salmonellosis has proved to be a valuable tool in risk management in Denmark and provides an example of how to integrate quantitative risk assessment and zoonotic disease surveillance.     

Food Risks on the Web: Analysis of the 2017 Fipronil Alert in the Italian Online Information Sources

In the summer of 2017, several European Union Member States were involved in a food alert caused by the presence of fipronil pesticide residues in chicken eggs. The food alert became a major news and received wide coverage both in the mass media and on the Internet. This article describes a study that analyzed how the Italian online information sources represented the fipronil alert, using web monitoring techniques and both manual and automatic content analysis methods. The results indicate that the alert was amplified because general news media could represent the alert within the frame of a political scandal, and because different social actors exploited the case. However, online information sources correctly communicated that the risks for consumers were low, reporting mainly what was officially communicated by the Italian health authorities. The study provides empirical evidence on how the online information sources represent food risks and food alerts and offers useful indications for health authorities in charge of the public communication of food risks.     

Redistributing food manufacturing: models for the creation and operation of responsive and agile production networks

Abstract

The food industry is a mature industry where its profit margins are thin and its R&D failure rate for new products is substantial. Both facts indicate that decent return on development investment cannot be provided and that the food industry cannot still rely on its traditional way of thinking and innovating. New product development cycles should involve not only the product itself but also the entire manufacturing and distribution network. Hence, collaborative manufacturing and servitization models are key elements for the rapid transformation of the new food industry. Moreover, recent developments in technology have the potential to shift manufacturing production away from large scale manufacturing plants to a network of distributed, smaller scale localized and customizable manufacturing facilities. Such a network is the crucial enabler for the on-demand manufacture of new or even, existing food products to rapidly respond to the consumers´ demands.     

Framework for Microbial Food‐Safety Risk Assessments Amenable to Bayesian Modeling

Regulatory agencies often perform microbial risk assessments to evaluate the change in the number of human illnesses as the result of a new policy that reduces the level of contamination in the food supply. These agencies generally have regulatory authority over the production and retail sectors of the farm‐to‐table continuum. Any predicted change in contamination that results from new policy that regulates production practices occurs many steps prior to consumption of the product. This study proposes a framework for conducting microbial food‐safety risk assessments; this framework can be used to quantitatively assess the annual effects of national regulatory policies. Advantages of the framework are that estimates of human illnesses are consistent with national disease surveillance data (which are usually summarized on an annual basis) and some of the modeling steps that occur between production and consumption can be collapsed or eliminated. The framework leads to probabilistic models that include uncertainty and variability in critical input parameters; these models can be solved using a number of different Bayesian methods. The Bayesian synthesis method performs well for this application and generates posterior distributions of parameters that are relevant to assessing the effect of implementing a new policy. An example, based on Campylobacter and chicken, estimates the annual number of illnesses avoided by a hypothetical policy; this output could be used to assess the economic benefits of a new policy. Empirical validation of the policy effect is also examined by estimating the annual change in the numbers of illnesses observed via disease surveillance systems.     

Re-distributed manufacturing and the food-water-energy nexus: opportunities and challenges

Abstract

Addressing the intersection of two important emerging research areas, re-distributed manufacturing (RDM) and the food-energy-water (FEW) nexus, this work combines insights from engineering, business and policy perspectives and explores opportunities and challenges towards a more localized and sustainable food system. Analysis centred on two specific food products, namely bread and tomato paste reveals that the feasibility and potential of RDM vary with the type of food product and the supply chain (SC) components. Physically, energy efficiency, water consumption and reduction of waste and carbon footprint may be affected by scale and location of production activities and potentials of industrial symbiosis. From the business perspective, novel products, new markets and new business models are expected in order for food RDM to penetrate within the established food industry. Studies on policies, through the lens of public procurement, call for solid evidence of envisioned environmental, social and economic benefits of a more localized food system. An initial integrated framework is proposed for understanding and assessing food RDM and the FEW nexus.     

When Evolution Works Against the Future: Disgust's Contributions to the Acceptance of New Food Technologies

New food technologies have a high potential to transform the current resource‐consuming food system to a more efficient and sustainable one, but public acceptance of new food technologies is rather low. Such an avoidance might be maintained by a deeply preserved risk avoidance system called disgust. In an online survey, participants (N = 313) received information about a variety of new food technology applications (i.e., genetically modified meat/fish, edible nanotechnology coating film, nanotechnology food box, artificial meat/milk, and a synthetic food additive). Every new food technology application was rated according to the respondent's willingness to eat (WTE) it (i.e., acceptance), risk, benefit, and disgust perceptions. Furthermore, food disgust sensitivity was measured using the Food Disgust Scale. Overall, the WTE both gene‐technology applications and meat coated with an edible nanotechnology film were low and disgust responses toward all three applications were high. In full mediation models, food disgust sensitivity predicted the disgust response toward each new food technology application, which in turn influenced WTE them. Effects of disgust responses on the WTE a synthetic food additive were highest for and lowest for the edible nanotechnology coating film compared to the other technologies. Results indicate that direct disgust responses influence acceptance and risk and benefit perceptions of new food technologies. Beyond the discussion of this study, implications for future research and strategies to increase acceptance of new food technologies are discussed.     

经济利益驱动下食品企业安全风险演化动态研究

消费扩大驱动下食品质量安全问题日渐严峻,食品企业经济利益驱动型掺假所具有的蓄意性、隐蔽性、技术性特点,为新时期食品安全治理提出了一系列挑战。鉴于此,本文借助演化博弈理论,考虑了食品企业收益、食品生产技术成本、生产残次食品损失以及政府监管成本、社会负面效益等因素对食品安全风险形成的影响,构建了食品掺假行为演化博弈模型,并对其演化状态进行了理论和仿真分析。在此基础上,运用元胞自动机理论,进一步考虑到食品企业策略转变意愿与基层食品监管机构策略转变意愿,从空间博弈角度对食品掺假行为及其监管的空间演化状态进行了深度剖析和刻画。研究结果显示,食品企业和基层食品监管机构在策略选择方面具有同步性振荡特征。而且,在食品企业策略转变意愿或基层食品监管机构策略转变意愿维持较低水平时,食品企业和基层食品监管机构的策略（严格监管,合规生产）是纯策略稳定状态。通过本文研究既丰富了我国食品安全监管理论,也为地方食品安全长效监管提供了思路借鉴和理论指导。     

Understanding Community Resilience from a PRA Perspective Using Binary Decision Diagrams

Probabilistic risk assessment (PRA) is a useful tool to assess complex interconnected systems. This article leverages the capabilities of PRA tools developed for industrial and nuclear risk analysis in community resilience evaluations by modeling the food security of a community in terms of its built environment as an integrated system. To this end, we model the performance of Gilroy, CA, a moderate‐size town, with regard to disruptions in its food supply caused by a severe earthquake. The food retailers of Gilroy, along with the electrical power network, water network elements, and bridges are considered as components of a system. Fault and event trees are constructed to model the requirements for continuous food supply to community residents and are analyzed efficiently using binary decision diagrams (BDDs). The study also identifies shortcomings in approximate classical system analysis methods in assessing community resilience. Importance factors are utilized to rank the importance of various factors to the overall risk of food insecurity. Finally, the study considers the impact of various sources of uncertainties in the hazard modeling and performance of infrastructure on food security measures. The methodology can be applicable for any existing critical infrastructure system and has potential extensions to other hazards.     

Bayesian Temporal Source Attribution of Foodborne Zoonoses: Campylobacter in Finland and Norway

Statistical source attribution approaches of food‐related zoonoses can generally be based on reported diagnosed human cases and surveillance results from different food sources or reservoirs of bacteria. The attribution model, or probabilistic classifier, can thus be based on the (sub)typing information enabling comparison between human infections and samples derived from source surveillance. Having time series of both data allows analyzing temporal patterns over time providing a repeated natural experiment. A Bayesian approach combining both sources of information over a long time series is presented in the case of Campylobacter in Finland and Norway. The full model is transparently presented and derived from the Bayes theorem. Previous statistical source attribution approaches are here advanced (1) by explicit modeling of the cases not associated with any of the sources under surveillance over time, (2) by modeling uncertain prevalence in a food source by bacteria type over time, and (3) by implementing formal model fit assessment using posterior predictive discrepancy functions. Large proportion of all campylobacteriosis can be attributed to broiler, but considerable uncertainty remains over time. The source attribution is inherently incomplete if only the sources under surveillance are included in the model. All statistical source attribution approaches should include a model fit assessment for judgment of model performance with respect to relevant quantities of interest. It is especially relevant when the model aims at a synthesis of several incomplete information sources under significant uncertainty of explanatory variables.     

Loss of Agro-Biodiversity, Uncertainty, and Perceived Control: A Comparative Risk Perception Study in Austria and China

The biogeographical centers of origin of important food crops-called Vavilov centers-are considered to be crucial sources of genetic diversity for present and future crop-breeding programs and thus for human food safety worldwide. Global environmental change and more intensified modes of crop production may cause genetic erosion (loss of traditional crop varieties and loss of crop wild relatives), especially in Vavilov centers. The present study focused on how the risk of genetic erosion (or loss of agro-biodiversity) is perceived in comparison to 16 other risk topics by experts and lay people in Austria and China. The most striking result was that genetic erosion was perceived to be an exceptionally unknown and uncertain risk topic, given that only genetically modified organisms (GMOs) were perceived as being even more uncertain. As a consequence of the high uncertainty, the idea of applying the precautionary principle to further prevent genetic erosion is discussed. An unprecedented finding-one that differs from Austrian participants-is that the Chinese have a higher perceived control over all risk topics. The increased perception of controllability in China is discussed in light of the theory of reflexive modernization. This theory strives to explain the increased critical attitude in Western countries such as Austria toward scientific innovations and toward the idea that everything can be calculated and mastered at will. By revealing different notions of risk perception, this research also provides additional scientific input to risk communication efforts for public education.     

Sales and operations planning: responding to the needs of industrial food producers

This paper investigates sales and operations planning (S&OP) at four Scandinavian industrial food producers in order to explore how the use of S&OP might help leaders to deal with the challenges set by the planning environment. Variables connected to the product and market, e.g. frequency of new product development, customer service levels and supply uncertainty, were identified as particularly critical for the S&OP process. It was found that there is great potential for S&OP in the food industry, foremost to cope with the imbalances between demand and supply and to create prerequisites for a stable production process.     

Adaptive agent-based manufacturing control and its application to flow shop routing control

The manufacturing industry is currently facing unprecedented challenges from changes and disturbances. The sources of these changes and disturbances are of different scope and magnitude. They can be of a commercial nature, or linked to fast product development and design, or purely operational (e.g. rush order, machine breakdown, material shortage etc.). In order to meet these requirements it is increasingly important that a production operation be flexible and is able to adapt to new and more suitable ways of operating. This paper focuses on a new strategy for enabling manufacturing control systems to adapt to changing conditions both in terms of product variation and production system upgrades. The approach proposed is based on two key concepts: (1) An autonomous and distributed approach to manufacturing control based on multi-agent methods in which so called operational agents represent the key physical and logical elements in the production environment to be controlled – for example, products and machines and the control strategies that drive them and (2) An adaptation mechanism based around the evolutionary concept of replicator dynamics which updates the behaviour of newly formed operational agents based on historical performance records in order to be better suited to the production environment. An application of this approach for route selection of similar products in manufacturing flow shops is developed and is illustrated in this paper using an example based on the control of an automobile paint shop.     

Consumer Selection of Food-Safety Information Sources

The aim of this study is to examine the preferences of consumers for different information sources when they have a question about food safety. On the basis of a nationally representative survey conducted in the Netherlands, five distinct consumer groups are identified that not only differ on the reported use of information sources but also regarding several personality characteristics and sociodemographic variables. The empirical results show that two-thirds of the consumers are selective in their use of information sources and prefer either institutional or social sources. So, multiple information-acquisition patterns exist among the general public. The study illustrates how these findings can help to develop effective risk communication strategies.     

A Modular Bayesian Salmonella Source Attribution Model for Sparse Data

Several statistical models for salmonella source attribution have been presented in the literature. However, these models have often been found to be sensitive to the model parameterization, as well as the specifics of the data set used. The Bayesian salmonella source attribution model presented here was developed to be generally applicable with small and sparse annual data sets obtained over several years. The full Bayesian model was modularized into three parts (an exposure model, a subtype distribution model, and an epidemiological model) in order to separately estimate unknown parameters in each module. The proposed model takes advantage of the consumption and overall salmonella prevalence of the studied sources, as well as bacteria typing results from adjacent years. The latter were used for a smoothed estimation of the annual relative proportions of different salmonella subtypes in each of the sources. The source‐specific effects and the salmonella subtype‐specific effects were included in the epidemiological model to describe the differences between sources and between subtypes in their ability to infect humans. The estimation of these parameters was based on data from multiple years. Finally, the model combines the total evidence from different modules to proportion human salmonellosis cases according to their sources. The model was applied to allocate reported human salmonellosis cases from the years 2008 to 2015 to eight food sources.     

Forty Years of Food Safety Risk Assessment: A History and Analysis

Before the founding of the Society for Risk Analysis (SRA) in 1980, food safety in the United States had long been a concern, but there was a lack of systematic methods to assess food-related risks. In 1906, the U.S. Congress passed, and President Roosevelt signed, the Pure Food and Drug Act and the Meat Inspection Act to regulate food safety at the federal level. This Act followed the publication of multiple reports of food contamination, culminating in Upton Sinclair's novel The Jungle, which highlighted food and worker abuses in the meatpacking industry. Later in the 20th century, important developments in agricultural and food technology greatly increased food production. But chemical exposures from agricultural and other practices resulted in major amendments to federal food laws, including the Delaney Clause, aimed specifically at cancer-causing chemicals. Later in the 20th century, when quantitative risk assessment methods were given greater scientific status in a seminal National Research Council report, food safety risk assessment became more systematized. Additionally, in these last 40 years, food safety research has resulted in increased understanding of a range of health effects from foodborne chemicals, and technological developments have improved U.S. food safety from farm to fork by offering new ways to manage risks. We discuss the history of food safety and the role risk analysis has played in its evolution, starting from over a century ago, but focusing on the last 40 years. While we focus on chemical risk assessment in the U.S., we also discuss microbial risk assessment and international food safety.