Application of Bayesian Networks in the development of herbs and spices sampling monitoring system

The application of systematic accident analysis tools to investigate food safety incidents

Effective food safety (FS) management relies on the understanding of the factors that contribute to FS incidents (FSIs) and the means for their mitigation and control. This review aims to explore the application of systematic accident analysis tools to both design FS management systems (FSMSs) as well as to investigate FSI to identify contributive and causative factors associated with FSI and the means for their elimination or control. The study has compared and contrasted the diverse characteristics of linear, epidemiological, and systematic accident analysis tools and hazard analysis critical control point (HACCP) and the types and depth of qualitative and quantitative analysis they promote. Systematic accident analysis tools, such as the Accident Map Model, the Functional Resonance Accident Model, or the Systems Theoretical Accident Model and Processes, are flexible systematic approaches to analyzing FSI within a socio-technical food system which is complex and continually evolving. They can be applied at organizational, supply chain, or wider food system levels. As with the application of HACCP principles, the process is time-consuming and requires skilled users to achieve the level of systematic analysis required to ensure effective validation and verification of FSMS and revalidation and reverification following an FSI. Effective revalidation and reverification are essential to prevent recurrent FSI and to inform new practices and processes for emergent FS concerns and the means for their control.

A data-driven approach for prioritising microbial and chemical hazards associated with dairy products using open-source databases

This study presents a data-driven approach for classifying food safety alerts related to chemical and microbial contaminants in dairy products using the Rapid Alert System for Food and Feed (RASFF) and the World Health Organization (WHO)'s Global Environmental Monitoring System (GEMS) food contaminants databases. This research aimed to prioritise microbial and chemical hazards based on their presence and severity through exploratory data analysis and to classify the severity of chemical hazards using machine learning (ML) approaches. It identified Listeria monocytogenes, Escherichia coli, Salmonella, Pseudomonas spp., Staphylococcus spp., Bacillus cereus, Clostridium spp., and Cronobacter sakazakii as the microbial hazards of priority in dairy products. The study also prioritised the top ten chemical hazards based on their presence and severity. These hazards include nitrate, nitrite, ergocornine, 3-MCPD ester, lead, arsenic, ochratoxin A, cadmium, mercury, and aflatoxin (G1, B1, G2, B2, G5 and M1). Using ML techniques, the accuracy rate of classifying food safety alerts as either 'serious' or 'non-serious' was up to 98 %. Additionally, the study identified Reference dose (RfD), substance amount, notification type, product, and substance as the most important features affecting the ML models' performance. These ML models (decision trees, random forests, k-nearest neighbors, linear discriminant analysis, and support vector machines) were also validated on an external dataset of RASFF alerts related to chemical contaminants in dairy products. They achieved an accuracy of up to 95.1 %. The study's findings demonstrate the models' robustness and ability to classify food safety alerts related to chemical contaminants in dairy products, even on new data. These results can enhance the development of more effective machine-learning models for classifying food safety alerts related to chemical contaminants in dairy products, highlighting the importance of developing accurate and efficient classification models for timely intervention.

Review of visual analytics methods for food safety risks

With the availability of big data for food safety, more and more advanced data analysis methods are being applied to risk analysis and prewarning (RAPW). Visual analytics, which has emerged in recent years, integrates human and machine intelligence into the data analysis process in a visually interactive manner, helping researchers gain insights into large-scale data and providing new solutions for RAPW. This review presents the developments in visual analytics for food safety RAPW in the past decade. Firstly, the data sources, data characteristics, and analysis tasks in the food safety field are summarized. Then, data analysis methods for four types of analysis tasks: association analysis, risk assessment, risk prediction, and fraud identification, are reviewed. After that, the visualization and interaction techniques are reviewed for four types of characteristic data: multidimensional, hierarchical, associative, and spatial-temporal data. Finally, opportunities and challenges in this area are proposed, such as the visual analysis of multimodal food safety data, the application of artificial intelligence techniques in the visual analysis pipeline, etc.

Application of machine learning to the monitoring and prediction of food safety: A review

Machine learning (ML) has proven to be a useful technology for data analysis and modeling in a wide variety of domains, including food science and engineering. The use of ML models for the monitoring and prediction of food safety is growing in recent years. Currently, several studies have reviewed ML applications on foodborne disease and deep learning applications on food. This article presents a literature review on ML applications for monitoring and predicting food safety. The paper summarizes and categorizes ML applications in this domain, categorizes and discusses data types used for ML modeling, and provides suggestions for data sources and input variables for future ML applications. The review is based on three scientific literature databases: Scopus, CAB Abstracts, and IEEE. It includes studies that were published in English in the period from January 1, 2011 to April 1, 2021. Results show that most studies applied Bayesian networks, Neural networks, or Support vector machines. Of the various ML models reviewed, all relevant studies showed high prediction accuracy by the validation process. Based on the ML applications, this article identifies several avenues for future studies applying ML models for the monitoring and prediction of food safety, in addition to providing suggestions for data sources and input variables.

A review of Canadian and international food safety systems: Issues and recommendations for the future

In January 2019, the Safe Food for Canadians Act/Safe Food for Canadians regulations (heretofore identified as SFCR) came into force across Canada and brought a more streamlined process to food safety practice in Canada. Food trade and production processes have evolved rapidly in recent decades, as Canada imports and exports food products; therefore it is critically important to remain aware of the latest advances responding to a range of challenges and opportunities in the food safety value chain. Looking through the optics of the recent SFCR framework, this paper places the spotlight on leading domestic and international research and practices to help strengthen food safety policies of the future. By shedding some light on new research, we also draw attention to international developments that are noteworthy, and place those in context as to how new Canadian food safety policy and regulation can be further advanced. The paper will benchmark Canada through a review study of food safety best practices by juxtaposing (i) stated aspirations with, (ii) actual performance in leading Organization for Economic Cooperation and Development (OECD) jurisdictions.

Food hazards on the European Union market: The data analysis of the Rapid Alert System for Food and Feed

The aim of the study was to examine similarities in notifications on main hazards within food reported in the Rapid Alert System for Food and Feed (RASFF) in 1979-2017. The main problems were mycotoxins in nuts, pathogenic microorganisms in poultry meat and fish, pesticide residues in fruits and vegetables, and heavy metals in fish. The increase in the number of notifications has been observed since 2002/2003. Products were notified mainly by Italy, Germany, and United Kingdom and originated from Asian and European Union countries. The notification basis was border control and official control, and the notification type was border rejections, information, and alerts. Notified products were not distributed and not placed on the market, distribution status could be also not specified, or distribution was possible, also to other countries. The risk decision on hazard was usually not made. Products were redispatched, withdrawn from the market, and destroyed, or import was not authorized. Remarks, which can be used to improve the RASFF database, were also presented. It was further pointed out that European law should significantly reduce the use of pesticides, drugs, and food additives, and European agriculture should be reoriented from an intensive farming to a more sustainable and ecological one.

Utilization of text mining as a big data analysis tool for food science and nutrition

Big data analysis has found applications in many industries due to its ability to turn huge amounts of data into insights for informed business and operational decisions. Advanced data mining techniques have been applied in many sectors of supply chains in the food industry. However, the previous work has mainly focused on the analysis of instrument-generated data such as those from hyperspectral imaging, spectroscopy, and biometric receptors. The importance of digital text data in the food and nutrition has only recently gained attention due to advancements in big data analytics. The purpose of this review is to provide an overview of the data sources, computational methods, and applications of text data in the food industry. Text mining techniques such as word-level analysis (e.g., frequency analysis), word association analysis (e.g., network analysis), and advanced techniques (e.g., text classification, text clustering, topic modeling, information retrieval, and sentiment analysis) will be discussed. Applications of text data analysis will be illustrated with respect to food safety and food fraud surveillance, dietary pattern characterization, consumer-opinion mining, new-product development, food knowledge discovery, food supply-chain management, and online food services. The goal is to provide insights for intelligent decision-making to improve food production, food safety, and human nutrition.

Food Supply Chain Stakeholders' Perspectives on Sharing Information to Detect and Prevent Food Integrity Issues

One of the biggest challenges facing the food industry is assuring food integrity. Dealing with complex food integrity issues requires a multi-dimensional approach. Preventive actions and early reactive responses are key for the food supply chain. Information sharing could facilitate the detection and prevention of food integrity issues. This study investigates attitudes towards a food integrity information sharing system (FI-ISS) among stakeholders in the European food supply chain. Insights into stakeholders' interest in participating and their conditions for joining an FI-ISS are assessed. The stakeholder consultation consisted of three rounds. During the first round, a total of 143 food industry stakeholders-covering all major food sectors susceptible to food integrity issues-participated in an online quantitative survey between November 2017 and February 2018. The second round, an online qualitative feedback survey in which the findings were presented, received feedback from 61 stakeholders from the food industry, food safety authorities and the science community. Finally, 37 stakeholders discussed the results in further detail during an interactive workshop in May 2018. Three distinct groups of industry stakeholders were identified based on reported frequency of occurrence and likelihood of detecting food integrity issues. Food industry stakeholders strongly support the concept of an FI-ISS, with an attitude score of 4.49 (standard deviation (S.D.) = 0.57) on a 5-point scale, and their willingness to participate is accordingly high (81%). Consensus exists regarding the advantages an FI-ISS can yield towards detection and prevention. A stakeholder's perception of the advantages was identified as a predictor of their intention to join an FI-ISS, while their perception of the disadvantages and the perceived risk of food integrity issues were not. Medium-sized companies perceive the current detection of food integrity issues as less likely compared to smaller and large companies. Interestingly, medium-sized companies also have lower intentions to join an FI-ISS. Four key success factors for an FI-ISS are defined, more specifically with regards to (1) the actors to be involved in a system, (2) the information to be shared, (3) the third party to manage the FI-ISS and (4) the role of food safety authorities. Reactions diverged concerning the required level of transparency, the type of data that stakeholders might be willing to share in an FI-ISS and the role authorities can have within an FI-ISS.

Recent Developments and Digital Perspectives in Food Safety and Authenticity

Food safety is of fundamental importance for the food processing industry, food retailers and distributors, and competent authorities because of its potentially direct impact on the health of consumers. Next to the prevention of microbiological, chemical, and physical hazards, increasing efforts are currently made to combat risks associated with food fraud or food authenticity. Food safety management systems nowadays comprise food safety, food defense, and food fraud prevention measures, trying to cope with the increasing complexity and globalization of the food supply chains. Future digital opportunities include the prediction of food safety and food authenticity issues by handling structured and unstructured data retrieved from various sources and origins to ensure the health of consumers and to minimize economical losses.

Developments in mycotoxin analysis: an update for 2016-2017

This review summarises developments in the determination of mycotoxins over a period between mid-2016 and mid-2017. Analytical methods to determine aflatoxins, Alternaria toxins, ergot alkaloids, fumonisins, ochratoxins, patulin, trichothecenes and zearalenone are covered in individual sections. Advances in proper sampling strategies are discussed in a dedicated section, as are methods used to analyse botanicals and spices and newly developed LC-MS based multi-mycotoxin methods. This critical review aims to briefly discuss the most important recent developments and trends in mycotoxin determination as well as to address limitations of the presented methodologies.