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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

Lookup NU author(s): Professor Lynn FrewerORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Abstract

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 (IoT) 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 (LMICs) 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.

Publication metadata

Author(s): Mu W, Kleter GA, Bouzembrak Y, Dupouy E, Frewer LJ, Radwan Al Natour FN, Marvin HJP

Publication type: Article

Publication status: Published

Journal: Comprehensive Reviews in Food Science and Food Safety

Year: 2024

Volume: 23

Issue: 1

Pages: 1-18

Print publication date: 24/01/2024

Online publication date: 24/01/2024

Acceptance date: 15/12/2023

Date deposited: 18/12/2023

ISSN (electronic): 1541-4337

Publisher: Institute of Food Technologists

URL: https://doi.org/10.1111/1541-4337.13296

DOI: 10.1111/1541-4337.13296

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