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This study ($25 download fee) compiled 254 incidents of food adulteration reported across from 20 countries in the Middle East and North Africa (MENA) region between 2019 and 2024, gathered from primary sources published in Arabic (85 %), English (10 %), and French (5 %). It also analysed 1261 notifications from the RASFF concerning food products originating from MENA countries during the same period.

The authors report that Lebanon and Turkey contributed the highest number of reported incidents with mislabelling (particularly expiry-date falsification) being the most common fraud.

The web-based surveillance identified 254 incidents, with Lebanon contributing to the highest number (15 %) followed by Egypt, Jorda and Iraq, while 78.9 % of all signals were classified generically as “food product’ and the most common issues involved expiration-date manipulation (62.9 %).

In the RASFF system, 1261 notifications linked to MENA-origin products were recorded, dominated by Turkey with 564 notifications (44.7 %) followed by Egypt (18 %) with alerts increasing between 2019 and 2024 and mainly triggered by contaminants (45.7 %) or unauthorized substances (16.9 %)

[Image – EverythingBen, available under Creative Commons Universal Public Domain Dedication]

Authenticity testing of honey is the best-known example of the need for a weight-of-evidence approach.  One analytical test is unlikely to give a definitive answer.  Using a panel of different tests, using different techniques and principles, can give an incremental list of suspicions.

The routine use of machine learning for constructing reference databases has enabled the rapid expansion of techniques that – in principle – can discriminate differences between “authentic” and “inauthentic” reference samples, and thus could be added to this weight-of-evidence armoury.  Two recent publications are a case in point.

In the first study (purchase required) the authors produced their own reference set by adulterating honey with syrups, then showed that they could be discriminated from authentic honey using Differential Scanning Calorimetry (DSC)  (they used graph-based semi-supervised learning to construct the classification model).  DSC is based on melting curves, and is an indirect measure of water content.  It is a cheap, routine, test widely used in many industries and – as such – would be ideal as the first step in an analytical workflow in order to screen out the most crudely adulterated samples.

This second study (purchase also required) is an example of building much more focussed and granular reference databases to address a highly specific authenticity question.  The authors measured carbon-13 ratios in honeys (and in their constituent protein) from 196 authentic honeys sourced from 56 cities in Turkey.  This analytical technique is usually used as a marker for exogenous sugar addition, but in this study the authors used the more subtle variations (driven by changes in flora, temperatures and humidities) to build a classification model for regional origin.  They were able to cluster the honeys into one of 7 distinct geographical regions of Turkey based upon their carbon-13 ratios.

[thanks to FAN-member Peter Farnell for spotting that the original version of this blog referenced "Differential Scanning Colourimetry" - which would, indeed, have been a novel technique worthy of comment]