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Understanding Food Safety Risks and Uncertainties and Meeting Citizens’ Expectations as Food Systems Become More Complex

The First FAO/WHO/AU International Food Safety Conference Addis Ababa, 12-13 February 2019













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    Article
    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 2024
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    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 early signals of emerging food safety risks and to provide early warning in a timely manner. This review provides an overview of existing and experimental applications of artificial intelligence (AI), big data, and internet of things tools and methods as part of early warning and emerging risk identification 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 and tools increase the feasibility and effectiveness of prospective early warning and emerging risk identification, 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.
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    Booklet
    FAO regional Training on “Capacity building on risk categorization for ranking risk of ASEAN food hazards for developing the risk-based monitoring protocol for food safety” 2019
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    A well-designed risk-based monitoring procedure for food safety hazards is one of the key elements for food safety control system. To ensure the success and effectiveness of establishing a monitoring procedure, a risk-based approach in ranking food hazards should be applied during the initial step for developing any monitoring programme. This would permits governmental and regulatory authorities to allocate resources most effectively for food safety control in their country. The best risk-ranking tool to be used should be selected on the basic of risk management requirements as well as data availability. Risk categorization, or the use of a risk matrix, is an example of a risk ranking tool, which can be applied to microbiological or chemical food safety hazards, when limited quantitative data are available. In order to strengthen the capacity on risk-based monitoring programme, FAO, collaborated with Thai Food and Drug Administration (Thai FDA), held the regional training course on “Capacity building on risk categorization for ranking risk of ASEAN food hazards for developing the risk-based monitoring protocol for food safety” from 23 to 25 April 2019 in Bangkok, Thailand. Over 30 participants attended the three-day training course. Through technical presentations, examples of good practices in other countries, and group work session, participants obtained knowledge on the principles of risk categorization. The training course enabled participants to discuss which approach ASEAN would apply for risk categorization for ranking of ASEAN food safety hazards, which further be used for developing the ASEAN risk-based monitoring procedure for one ASEAN common hazard.
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