Food Safety Risk Assessment Research Articles

A novel method for the identification of illegal cooking oil (2): determination of special odd-chain fatty acids by multidimensional gas chromatography-mass spectrometry

Endogenesis referents from illegal cooking oil (ICO), namely, 13-methyl-tetradecanoic acid and undecanoic acid have been confirmed via non-target and target screening of fatty acids. The former is mainly originated from animal oil, and the later from heated vegetable oil. Based on the retention interactions between gas chromatographic columns with different polarities and alkyl acids, the alkyl acids with various carbon chain or their isomers with the same carbon chains can be separated effectively using multidimensional gas chromatographymass spectrometry. And the accurate quantification of 13-methyl-tetradecanoic acid and undecanoic acid was obtained. The target compounds can be cleaned up online and enriched with the developed method. Subsequently, the fourth and fifth assessments organized by China National Center for Food Safety Risk Assessment were performed in our laboratory. After continuous improvement, the precision of the method was increased. In detail, 100% vegetable oils, 71% ICO (the 4th batch) and 75% ICO (the 5th batch) have been identified. In combination with the capsaicinoid contents, edible oils can be identified in a comprehensive way, and the precision for ICO samples was increased to 89% and 100% for the 4th batch and the 5th batch samples, respectively. Owing to the aforementioned advantages, the developed method has been chosen as one of the four instrumental methods for the identification of ICO by China Ministry of Public Health, waiting for the validation from authoritative departments.

食品中化学危害暴露组与毒理学测试新技术中国技术路线图

This is a brief perspective on China roadmap for risk management technology translation dealing with the new outcome from risk assessment. Traditionally, risk assessment is based on deterministic endpoints. From this start point, the disadvantage is reviewed in current toxicology test and dietary exposure, e.g., the result extrapolation from high dose in animal test to human being, rare in individuals’ exposomes. The vision and strategy has been explored for exposure science and toxicity testing in the 21st century (TT21C), China roadmap should been explored how to establish the platform and combining translational toxicology and exposomics in food safety risk assessment in China.

Integrating surveillance of animal health, food pathogens and foodborne disease in the European Union

The European Food Safety Authority (EFSA) is the keystone of European Union (EU) risk assessment for food and feed safety. In collaboration with national authorities and in consultation with its stakeholders, EFSA provides independent scientific advice and information about existing and emerging risks. Assessing biological risks at the human-animal interface is becoming ever more challenging because this interface is in a permanent state of flux. In addition, questions about food safety cannot usually be categorised under one discipline; most of the time, they need to be addressed in a transdisciplinary way. Two scientific panels of EFSA, on biological hazards (BIOHAZ) and on animal health and welfare (AHAW), have, in many instances, jointly addressed such complex, multifaceted questions of risk. This paper reviews the integrated approach of the EU towards risk assessment, with a special focus on human health and the whole food chain, and on science-based interventions to lower the risk to consumers.

Editorial: EFSA calls for integrated and coordinated actions at EU and international levels to address global declines of pollinators

The European Food Safety Authority (EFSA) is the keystone of the European Union’s food and feed safety risk assessment. Indeed, EFSA provides scientific advice to risks managers for the safety of food in the EU and a high level of protection for food-producing animals, including bees, their ecosystem services, such as crop pollination and honey production. The large bee losses reported worldwide over the last decades have stimulated a lot of research on the monitoring of bees, mainly Apis bees (honeybees), and their stressors (e.g. pathological, agrochemicals, environmental, nutritional, etc.), but principally on pathogens. During this process, extensive datasets have been generated and collated on honeybee losses that have been linked to diseases, pests and pathogens in Europe and North America. However, given the importance of all bee pollinators not only honeybees and the universally agreed multifactorial origin of bee losses, such an approach seems too limited and may fail in meeting the global protection goals of ensuring bee diversity, crop pollination and honey production.

Sample size guidelines for fitting a lognormal probability distribution to censored most probable number data with a Markov chain Monte Carlo method

Levels of pathogenic organisms in food and water have steadily declined in many parts of the world. A consequence of this reduction is that the proportion of samples that test positive for the most contaminated product–pathogen pairings has fallen to less than 0.1. While this is unequivocally beneficial to public health, datasets with very few enumerated samples present an analytical challenge because a large proportion of the observations are censored values. One application of particular interest to risk assessors is the fitting of a statistical distribution function to datasets collected at some point in the farm-to-table continuum. The fitted distribution forms an important component of an exposure assessment. A number of studies have compared different fitting methods and proposed lower limits on the proportion of samples where the organisms of interest are identified and enumerated, with the recommended lower limit of enumerated samples being 0.2. This recommendation may not be applicable to food safety risk assessments for a number of reasons, which include the development of new Bayesian fitting methods, the use of highly sensitive screening tests, and the generally larger sample sizes found in surveys of food commodities. This study evaluates the performance of a Markov chain Monte Carlo fitting method when used in conjunction with a screening test and enumeration of positive samples by the Most Probable Number technique. The results suggest that levels of contamination for common product–pathogen pairs, such as Salmonella on poultry carcasses, can be reliably estimated with the proposed fitting method and samples sizes in excess of 500 observations. The results do, however, demonstrate that simple guidelines for this application, such as the proportion of positive samples, cannot be provided.

Risk Assessment of Food Safety in the Dairy Supply Chain by Hybrid Mining Approach

Risk Assessment of Food Safety in the Dairy Supply Chain by Hybrid Mining Approach

A physiologically based pharmacokinetic model for the prediction of the depletion of methyl‐3‐quinoxaline‐2‐carboxylic acid, the marker residue of olaquindox, in the edible tissues of pigs

To estimate the consumer exposure to olaquindox (OLA) residues in porcine edible tissues, a physiologically based pharmacokinetic (PBPK) model for methyl-3-quinoxaline-2-carboxylic acid (MQCA), the marker residue of OLA, was developed in pigs based on the assumptions of the flow-limited distribution, hepatic metabolism, and renal excretion. The model included separate compartments corresponding to blood, muscle, liver, kidney, adipose, and an extra compartment representing the remaining carcass. Physiological parameters were determined from literatures. Plasma protein binding, partition coefficients, and renal clearance for MQCA were determined in in vitro and in vivo studies. The metabolic conversion of OLA to MQCA was assumed as a simple, one-step process, and an apparent first-order rate constant (k) was employed to describe this metabolic process. The PBPK model was optimized and validated with plasma and tissue data from literatures and our study. Sensitivity analysis and Monte Carlo simulation were also implemented to estimate the influence of model parameters on the goodness of fit. When compared with the observed data, the PBPK model underestimated the MQCA level in all compartments at the early time points, whereas gave excellent predictions of MQCA concentration in porcine edible tissues at later time points. The correlation coefficients between the predicted and observed values were over 0.88. The consistency between the model predictions and the real residues of OLA in pigs proved the good applicability of our model in food safety risk assessment.

Label-Free and Selective Bacteria Detection Using a Film with Transferred Bacterial Configuration

Specific identification of bacteria has been achieved through precisely transferred bacterial structure on the surface of overoxidized polypyrrole (OPPy) film. The recognition of target bacteria was successfully carried out in real time using OPPy film in combination with dielectrophoresis. The unique combination of both techniques made the specific detection of template bacilli possible at concentrations as low as 10(3) CFU/mL within 3 min, without any bacterial pretreatment. The observation of the movement of bacteria by using a fluorescent microscope revealed that living bacteria were being trapped vertically in the cavity created in the OPPy film. Further, the bacterial cavities had high selectivity and were able to discriminate particular target bacteria, Pseudomonas aeruginosa, out of bacterial mixtures containing Acinetobacter calcoaceticus, Escherichia coli, and Serratia marcescens, which are known to have a similar shape. This simple method can be used for a wide variety of applications in which rapid bacterial detection is required, such as food safety risk assessment, clinical point-of-care testing, and continuous environmental monitoring.

Mechanisms for assessing food safety risk

PurposeThe purpose of this paper is to review the methods for assessing food safety risk within a food safety plan.Design/methodology/approachThe research involved analysis of both qualitative and quantitative methods of risk assessment.FindingsRisk assessment is a key element of the HACCP approach to food safety. It requires food business operators and those on HACCP teams to determine both the acceptable level of contamination and the risk for the food business, and ultimately the consumer. The choice of food safety risk assessment model is crucial to an organisation. The mechanisms to determine what is acceptable can be a combination of scientific based and value based criteria and utilise qualitative or semi‐quantitative approaches. Whilst fuzzy logic has a place in making risk assessment more quantitative; specific software tools are required to enable quantitative risk assessment especially where what is acceptable at one point could, subject to other factors later in the supply chain, change to an unacceptable level of risk to the consumer. Quantitative mechanisms are required to make these decisions at organisational, or indeed at policy level, fully transparent.Originality/valueThis research is of academic value and of value to policy makers and practitioners in the food supply chain.

Risk Assessment of Food Safety in the Dairy Supply Chain

Risk Assessment of Food Safety in the Dairy Supply Chain

Implementation of systematic reviews in EFSA scientific outputs workflow

Systematic reviews (SR) are an evidence synthesis approach that provides robust and transparent answers to clearly formulated questions. Originally developed for use in clinical practice, SRs have wider applicability, including food and feed safety risk assessment. EFSA has implemented the use of SRs, and this document contributes to the further development of this in-house capacity. Since the publication of the document “Application of Systematic Review Methodology to Food and Feed Safety Assessments to Support Decision Making”, which mainly focuses on interventions and exposures (PECO/PICO), little has changed in this arena. Fast increasing fields of application include chemical and environmental risk assessment, and analysing environmental management interventions. Considering time constraints at EFSA, the use of SRs should be pursued thoughtfully. Important are the use of explicit systematic methods aimed at minimising bias and maximising transparency in order to produce the most reliable findings that can be used to inform decision making. Participants of the training courses indicated SRs should be a priority for controversial topics (which might be subject to greater scrutiny by external parties, including the public, and thereby would benefit from maximum transparency) or topics for which there was disagreement amongst experts. Some areas addressed by EFSA have considerable potential impact, for example related to public health or animal trade, and these topics could be prioritised for SR. Under severe time constraints, a full SR may not be possible, but a rapid review can be considered. However rapid reviews are not a substitute for systematic reviews. Adoption of rapid reviews exchanges one set of concerns (time and resources contracts) for another (lack of robustness and comprehensiveness). In the view of the Consortium, the continuation of training opportunities is important. Appropriate commissioning of SR expertise is an important step in establishing the role of the methodology in EFSA risk assessments.

A novel method for the identification of illegal cooking oil (1): detection of three capsaicinoids with liquid chromatography-mass spectrometry

Illegal cooking oil (ICO, also named swill-cooked dirty oil) has recently become a serious food safety problem in China. Now, the identification method of ICO is also a hot research area. Owning to the special eating habits of Chinese people, cayenne is widely used in catering business. Capsaicinoids are main spicy compounds in cayenne. So, they are potential evaluation indices for the identification of ICO. In this study, a solid phase extraction-liquid chromatography-mass spectrometry (SPE-LC-MS) method has been developed to detect the trace residues of three capsaicinoids (capsaicin, dihydrocapsaicin and nonylic acid vanillylamide) in cooking oil. The oil sample was first extracted with 20 g/L sodium hydroxide, the C18 SPE cartridge was then used to clean-up the sample and enrich the analytes before the liquid chromatography-mass spectrometry (LC-MS) detection. With this method, sixty seven blind samples provided by China National Center for Food Safety Risk Assessment were analyzed. The results showed that the capsaicinoids are good evaluation indices for the identification of ICO. In all the 48 ICO samples, 36 samples were successfully recognized. All the 19 normal oil samples were accurately identified. This method has been chosen and authorized as one of the four standard instrumental identification methods for ICO by the National Ministry of Health of China.

A Closer Look at GE Corn Findings

A Closer Look at GE Corn Findings

EFSA's approach to identifying emerging risks in food and feed: taking stock and looking forward

The Emerging Risks Unit has the responsibility for coordinating EFSA’s activities to establish a capacity for the identification of emerging risks. A process was trialled and further developed during a pilot period of 18 months from 2010-2012. This included the implementation of an operational procedure for emerging risks identification, the assessment of selected data sources, the testing of tools for collecting information, the consolidation of knowledge networks for sharing information and the development of a methodological framework. Specific issues were identified for follow-up activities using an expert judgment approach. These include a study on climate change and the emergence of aflatoxins in cereal crops in the European Union (EU), a European-wide survey on the consumption of energy drinks, a task force on human risk assessment of chemical mixtures, an internal task force on bee health, and a foresight study on the potential impact of omics technologies on food and feed safety risk assessment. These follow-up activities will contribute to the determination of whether the issues identified can indeed give rise to emerging risks. Overall, our experience shows that emerging risks identification requires a high level of expertise due to major data gaps and uncertainties in the evaluation process. Effective networking has proven to be essential for exchanging methods, data and evaluations of emerging risks. The system piloted has shown some potential for the identification of issues that may give rise to emerging risks. Useful knowledge has been gained in the area of gathering and filtering large amounts of information and building knowledge networks on emerging risks. Next steps include the establishment of a standing Working Group (WG) on Emerging Risks, the reinforcement of the engagement with Member States and Stakeholders, the fine tuning of the revised methodological framework, and the completion of the projects on the issues identified.

Inferring an Augmented Bayesian Network to Confront a Complex Quantitative Microbial Risk Assessment Model with Durability Studies: Application to Bacillus Cereus on a Courgette Purée Production Chain

The Monte Carlo (MC) simulation approach is traditionally used in food safety risk assessment to study quantitative microbial risk assessment (QMRA) models. When experimental data are available, performing Bayesian inference is a good alternative approach that allows backward calculation in a stochastic QMRA model to update the experts' knowledge about the microbial dynamics of a given food-borne pathogen. In this article, we propose a complex example where Bayesian inference is applied to a high-dimensional second-order QMRA model. The case study is a farm-to-fork QMRA model considering genetic diversity of Bacillus cereus in a cooked, pasteurized, and chilled courgette purée. Experimental data are Bacillus cereus concentrations measured in packages of courgette purées stored at different time-temperature profiles after pasteurization. To perform a Bayesian inference, we first built an augmented Bayesian network by linking a second-order QMRA model to the available contamination data. We then ran a Markov chain Monte Carlo (MCMC) algorithm to update all the unknown concentrations and unknown quantities of the augmented model. About 25% of the prior beliefs are strongly updated, leading to a reduction in uncertainty. Some updates interestingly question the QMRA model.

Application of MALDI-TOF mass spectrometry for the detection of enterotoxins produced by pathogenic strains of the Bacillus cereus group

Enterotoxins produced by different species of the Bacillus cereus group, such as cytotoxin K1 (CytK1) and non-haemolytic enterotoxin (NHE), have been associated with diarrhoeal food poisoning incidents. Detection of CytK1 is not possible with commercial assays while NHE is recognised by an immunological kit (TECRA) that does not specifically target this protein because it is based on polyclonal antibodies. It is evident that the lack of suitable tools for the study of enterotoxins hampers the possibilities for accurate hazard identification and characterisation in microbial food safety risk assessment. We applied matrix-assisted laser desorption/ionisation time-of-flight mass spectrometry (MALDI-TOF/MS) for the detection of CytK1 and NHE produced by pathogenic strains of the B. cereus group using protein digests from 1D gel electrophoresis. Secretion of CytK1 and two of the three components of NHE was confirmed in supernatants of different B. cereus cultures. For each protein, we introduce biomarkers that could be used for the screening of food poisoning or food/environmental isolates that can secrete enterotoxins. For example, tryptic peptides of 2,310.2 and 1,192.5 Da (calculated mass) can be indicators for CytK1 and NheA, respectively, although a simultaneous detection of other enterotoxin-specific peptides is recommended to assure the presence of a toxin in an unknown sample. Comparison of MALDI-TOF/MS with the TECRA kit showed that our methodological strategy performed well and it had the competitive advantage of specifically detecting NheA. Therefore, MALDI-TOF/MS can be successfully incorporated into risk assessment procedures in order to determine the involvement of strains of the B. cereus group in foodborne outbreaks, including the recently described cytK1 producing species, Bacillus cytotoxicus.

Methods for fitting a parametric probability distribution to most probable number data

Every year hundreds of thousands, if not millions, of samples are collected and analyzed to assess microbial contamination in food and water. The concentration of pathogenic organisms at the end of the production process is low for most commodities, so a highly sensitive screening test is used to determine whether the organism of interest is present in a sample. In some applications, samples that test positive are subjected to quantitation. The most probable number (MPN) technique is a common method to quantify the level of contamination in a sample because it is able to provide estimates at low concentrations. This technique uses a series of dilution count experiments to derive estimates of the concentration of the microorganism of interest. An application for these data is food-safety risk assessment, where the MPN concentration estimates can be fitted to a parametric distribution to summarize the range of potential exposures to the contaminant. Many different methods (e.g., substitution methods, maximum likelihood and regression on order statistics) have been proposed to fit microbial contamination data to a distribution, but the development of these methods rarely considers how the MPN technique influences the choice of distribution function and fitting method. An often overlooked aspect when applying these methods is whether the data represent actual measurements of the average concentration of microorganism per milliliter or the data are real-valued estimates of the average concentration, as is the case with MPN data. In this study, we propose two methods for fitting MPN data to a probability distribution. The first method uses a maximum likelihood estimator that takes average concentration values as the data inputs. The second is a Bayesian latent variable method that uses the counts of the number of positive tubes at each dilution to estimate the parameters of the contamination distribution. The performance of the two fitting methods is compared for two data sets that represent Salmonella and Campylobacter concentrations on chicken carcasses. The results demonstrate a bias in the maximum likelihood estimator that increases with reductions in average concentration. The Bayesian method provided unbiased estimates of the concentration distribution parameters for all data sets. We provide computer code for the Bayesian fitting method.

“Shake, Rattle and Roll”: risk assessment and management for food safety during two Christchurch earthquakes

Two earthquakes recently struck the Christchurch region. The 2010 earthquake in Canterbury was strong yet sustained less damage than the 2011 earthquake in Christchurch, which although not as strong, was more damaging and resulted in 185 deaths. Both required activation of a food safety response. The food safety response for both earthquakes was focused on reducing the risk of gastroenteritis by limiting the use of contaminated water and food, both in households and food businesses. Additional food safety risks were identified in the 2011 Christchurch earthquake due to the use of large-scale catering for rescue workers, volunteers and residents unable to return home. Using a risk assessment framework, the food safety response involved providing water and food safety advice, issuing a boil water notice for the region and initiating water testing on reticulation systems. Food businesses were contacted to ensure the necessary measures were being taken. Additional action during the 2011 Christchurch earthquake response included making contact with food businesses using checklists and principles developed in the first response and having regular contact with those providing catering for large numbers. In the 2010 earthquake in Canterbury, several cases of gastroenteritis were reported, although most resulted from person-to-person contact rather than contamination of food. There was a small increase in gastroenteritis cases following the 2011 Christchurch earthquake. The food safety response for both earthquakes was successful in meeting the goal of ensuring that foodborne illness did not put additional pressure on hospitals or affect search and rescue efforts.

Farm food safety and diseases risk assessments: case studies from the horticultural and salmonid farms

This study explores the perceptions of selected fresh produce and salmonid farms on the types and importance of various risk assessments conducted at the primary production level. Both the fresh produce and salmonid farms in this study are vigilantly conducting risk assessments to prevent or reduce potential food safety and infectious diseases from occurring. The participating farms generally carried out at least eight types of risk assessments such as health and safety, water, workers’ hygiene, site assessment and pesticides/veterinary medicines on an annual basis. Health and safety, pesticides/veterinary health, fresh produce safety and fish health assessments are some of the most important types of risk assessments for the farms. Most farms will continue with the good practice of conducting risk assessments even if it is not required by their customers or regulations but would prefer to reduce the amount of paper/electronic-based risk assessments.

Methods for fitting the Poisson-lognormal distribution to microbial testing data

The Poisson distribution can be used to describe the number of microorganisms in a serving of food, but in most food-safety applications, the variability of the Poisson distribution is insufficient to describe the heterogeneity in microbial contamination across the population of all servings. To model how contamination varies across the population of all possible servings, the lognormal distribution can be paired with the Poisson distribution to create an over-dispersed distribution, which is referred to as the Poisson-lognormal distribution. An advantage of this distribution is that random draws from the distribution are integer-valued. This is beneficial for some food-safety risk assessments because a modeled serving is either contaminated or not. This distribution is also appropriate when the results of a laboratory test are integer-valued, such as when direct plating is used to enumerate samples. While some surveys perform only absence/presence screenings tests, surveys that are more thorough can pair a screening test with enumeration of screen-test positive samples via direct plating. For this application, statistical methods that accommodate censored data are required to fit the data to a Poisson-lognormal distribution. This study compares a Bayesian hierarchical model to a maximum likelihood estimation approach for fitting data to the Poisson-lognormal distribution. Across a range of datasets, the Bayesian method demonstrates superior performance. OpenBUGS and R code are provided to implement both methods.