Bayesian Networks modeling of diarrhetic shellfish poisoning in Mytilus edulis harvested in Bantry Bay, Ireland

Application of Bioinformatics and Machine Learning Tools in Food Safety

PURPOSE OF REVIEW

Food safety is a fundamental challenge in public health and sustainable development, facing threats from microbial, chemical, and physical contamination. Innovative technologies improve our capacity to detect contamination early and prevent disease outbreaks, while also optimizing food production and distribution processes.

RECENT FINDINGS

This article discusses the role of new bioinformatics and machine learning technologies in promoting food safety and contamination control, along with various related articles in this field. By analyzing genetic and proteomic data, bioinformatics helps to quickly and accurately identify pathogens and sources of contamination. Machine learning, as a powerful tool for massive data processing, also can discover hidden patterns in the food production and distribution chain, which helps to improve risk prediction and control processes. By reviewing previous research and providing new solutions, this article emphasizes the role of these technologies in identifying, preventing, and improving decisions related to food safety. This study comprehensively shows how the integration of bioinformatics and machine learning can help improve food quality and safety and prevent foodborne disease outbreaks.

Ensemble models improve near-term forecasts of harmful algal bloom and biotoxin risk

Harmful algal blooms pose a significant threat to marine ecosystems, aquaculture industries, and human health. To mitigate these risks, agencies around the globe perform regular monitoring and operate early warning systems based on expected risk levels. However, bloom dynamics can be influenced by a large range of physical and biological factors, leading to high uncertainty in predictions of future blooms. Here, we explore the effectiveness of ensemble models for forecasting risk of algal blooms and associated toxins in Scotland, employing a diverse set of candidate models, including tree-based approaches, neural networks, and hierarchical Bayesian regression. These models predicted the probability that algal densities or biotoxin concentrations would exceed a threshold (either 'amber' status in the traffic light guidance system, or 'detection') in the next week using publicly available environmental products combined with regulatory monitoring data from dozens of locations in Scotland (2015-2022; Alexandrium spp., Dinophysis spp., Karenia mikimotoi, Pseudo-nitzschia spp. [+ delicatissima, serriata groups], domoic acid (DA), okadaic acid / dinophysistoxins / pectenotoxins (DSTs), paralytic shellfish toxins (PSTs)). The forecasted probabilities from the candidate models were used as inputs for a stacking ensemble model. Compared to individual candidate and null models, the ensemble models consistently improved forecasting performance across two years of withheld out-of-sample validation data, as assessed by five distinct performance metrics (ensemble skill scores among metrics and targets: mean = 0.499, middle 95 % = 0.214-0.900; skill score give improvement over the null model, with 1 indicating perfect performance). Performance varied by monitoring target, with best forecasts for DSTs (mean ensemble skill: 0.747) and poorest for K. mikimotoi (mean ensemble skill: 0.334). Autoregressive terms and regional spatiotemporal patterns emerged as the most informative predictors, with effects of environmental conditions contingent on the algal density or toxin concentration. Our results demonstrate the clear advantage of the ensemble approach. The operational implementation of these models provides probabilistic forecasts to enhance Scotland's monitoring program and early warning system. Ensemble modelling leverages the combined strengths of the wide array of modern techniques available, offering a promising path toward improved forecasts.

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.

Making food systems more resilient to food safety risks by including artificial intelligence, big data, and internet of things into food safety early warning and emerging risk identification tools

To enhance the resilience of food systems to food safety risks, it is vitally important for national authorities and international organizations to be able to identify emerging food safety risks and to provide early warning signals in a timely manner. This review provides an overview of existing and experimental applications of artificial intelligence (AI), big data, and internet of things as part of early warning and emerging risk identification tools and methods in the food safety domain. There is an ongoing rapid development of systems fed by numerous, real-time, and diverse data with the aim of early warning and identification of emerging food safety risks. The suitability of big data and AI to support such systems is illustrated by two cases in which climate change drives the emergence of risks, namely, harmful algal blooms affecting seafood and fungal growth and mycotoxin formation in crops. Automation and machine learning are crucial for the development of future real-time food safety risk early warning systems. Although these developments increase the feasibility and effectiveness of prospective early warning and emerging risk identification tools, their implementation may prove challenging, particularly for low- and middle-income countries due to low connectivity and data availability. It is advocated to overcome these challenges by improving the capability and capacity of national authorities, as well as by enhancing their collaboration with the private sector and international organizations.

Weighted Bayesian network for the classification of unbalanced food safety data: Case study of risk-based monitoring of heavy metals

Historical data on food safety monitoring often serve as an information source in designing monitoring plans. However, such data are often unbalanced: a small fraction of the dataset refers to food safety hazards that are present in high concentrations (representing commodity batches with a high risk of being contaminated, the positives) and a high fraction of the dataset refers to food safety hazards that are present in low concentrations (representing commodity batches with a low risk of being contaminated, the negatives). Such unbalanced datasets complicate modeling to predict the probability of contamination of commodity batches. This study proposes a weighted Bayesian network (WBN) classifier to improve the model prediction accuracy for the presence of food and feed safety hazards using unbalanced monitoring data, specifically for the presence of heavy metals in feed. Applying different weight values resulted in different classification accuracies for each involved class; the optimal weight value was defined as the value that yielded the most effective monitoring plan, that is, identifying the highest percentage of contaminated feed batches. Results showed that the Bayesian network classifier resulted in a large difference between the classification accuracy of positive samples (20%) and negative samples (99%). With the WBN approach, the classification accuracy of positive samples and negative samples were both around 80%, and the monitoring effectiveness increased from 31% to 80% for pre-set sample size of 3000. Results of this study can be used to improve the effectiveness of monitoring various food safety hazards in food and feed.

Applying federated learning to combat food fraud in food supply chains

Ensuring safe and healthy food is a big challenge due to the complexity of food supply chains and their vulnerability to many internal and external factors, including food fraud. Recent research has shown that Artificial Intelligence (AI) based algorithms, in particularly data driven Bayesian Network (BN) models, are very suitable as a tool to predict future food fraud and hence allowing food producers to take proper actions to avoid that such problems occur. Such models become even more powerful when data can be used from all actors in the supply chain, but data sharing is hampered by different interests, data security and data privacy. Federated learning (FL) may circumvent these issues as demonstrated in various areas of the life sciences. In this research, we demonstrate the potential of the FL technology for food fraud using a data driven BN, integrating data from different data owners without the data leaving the database of the data owners. To this end, a framework was constructed consisting of three geographically different data stations hosting different datasets on food fraud. Using this framework, a BN algorithm was implemented that was trained on the data of different data stations while the data remained at its physical location abiding by privacy principles. We demonstrated the applicability of the federated BN in food fraud and anticipate that such framework may support stakeholders in the food supply chain for better decision-making regarding food fraud control while still preserving the privacy and confidentiality nature of these data.

Time-Lagged Correlation Analysis of Shellfish Toxicity Reveals Predictive Links to Adjacent Areas, Species, and Environmental Conditions

Diarrhetic Shellfish Poisoning (DSP) is an acute intoxication caused by the consumption of contaminated shellfish, which is common in many regions of the world. To safeguard human health, most countries implement programs focused on the surveillance of toxic phytoplankton abundance and shellfish toxicity levels, an effort that can be complemented by a deeper understanding of the underlying phenomena. In this work, we identify patterns of seasonality in shellfish toxicity across the Portuguese coast and analyse time-lagged correlations between this toxicity and various potential risk factors. We extend the understanding of these relations through the introduction of temporal lags, allowing the analysis of time series at different points in time and the study of the predictive power of the tested variables. This study confirms previous findings about toxicity seasonality patterns on the Portuguese coast and provides further quantitative data about the relations between shellfish toxicity and geographical location, shellfish species, toxic phytoplankton abundances, and environmental conditions. Furthermore, multiple pairs of areas and shellfish species are identified as having correlations high enough to allow for a predictive analysis. These results represent the first step towards understanding the dynamics of DSP toxicity in Portuguese shellfish producing areas, such as temporal and spatial variability, and towards the development of a shellfish safety forecasting system.