strategies (2)

This review (purchase required) and its associated recommendations is primarily aimed at regulators and competent authorities, but also has implications for food businesses.

The aim of was to consider food-related fraud prevention initiatives, understand what has worked well, and develop a series of recommendations on preventing food fraud, both policy related and for future research.

The authors found that reactive (including intelligence based) food fraud detection dominates over prevention strategies, especially where financial, knowledge, and time resources are scarce. First-generation tools have been developed for food fraud vulnerability assessment, risk analysis, and development of food fraud prevention strategies. However, examples of integrated food control management systems at food business operator, supply chain, and regulatory levels for prevention are limited.

They conclude that the lack of hybrid (public/private) integration of food fraud prevention strategies, as well as an effective verification ecosystem, weakens existing food fraud prevention plans. While there are several emergent practice models for food fraud prevention, they need to be strengthened to focus more specifically on capable guardians and target hardening.

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This review (open access) covers technological and digital solutions to mitigate food fraud risk, concentrating on recent developments.  It categorises solutions as either systematic interventions (e.g. risk prioritisation databases, digital fraud prediction tools), fraud detection techniques (analytical test methods) or package-level technologies (e.g. traceability systems, anti-counterfeiting markers, RFID tags).

It concludes that a notable gap exists in converting laboratory based sophisticated technologies to tools in high-paced, live industrial applications. New frontiers such as handheld laser-induced breakdown spectroscopy (liBS) and smart-phone spectroscopy have emerged for rapid food authentication. Multifunctional devices with hyphenating sensing mechanisms that are combined with deep learning strategies to compare food fingerprints can be a great leap forward in the industry. Combination of different technologies such as spectroscopy and separation techniques will also be superior where quantification of adulterants are preferred. with the advancement of automation these technologies will be able to be deployed as in-line scanning devices in industrial settings to detect food fraud across multiple points in food supply chains.

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