Food Fraud Remains Widespread Despite New Detection Technologies

Food fraud, which includes the adulteration, mislabeling, substitution, or counterfeiting of food products for economic gain, continues to affect the global food industry despite advancements in detection technologies.

Experts estimate that a considerable portion of food products sold in retail settings contain some level of adulteration. This ongoing issue results in substantial financial losses for both the United States and the European Union each year.

High-value items such as olive oil, honey, and coffee are among the most commonly targeted goods due to their market price and the relative ease with which they can be adulterated. These products are often subject to economically motivated manipulation, which can be difficult to detect without specialized testing methods.

Analytical and digital tools, including DNA barcoding, CRISPR diagnostics, next-generation sequencing, and blockchain, have been developed to identify fraudulent activity in the food supply chain. However, the integration of these technologies into regulatory and industrial frameworks has been inconsistent, largely due to challenges related to cost, standardization, and system complexity.

What the numbers show

• Up to 10% of retail food products are estimated to be adulterated
• Annual economic damage from food fraud is $10–15 billion in the US
• Food fraud costs the EU €8–12 billion each year

Rapid, non-destructive analysis methods such as spectroscopic, electronic, and DNA-based techniques have been introduced to improve detection. Despite their speed, these approaches sometimes lack the specificity required for accurate identification or depend on complex laboratory procedures, limiting their widespread application.

Emerging technologies like Front-Face Fluorescence spectroscopy, Raman spectroscopy, and energy-dispersive X-ray fluorescence offer additional non-destructive testing options. Nonetheless, these methods still encounter obstacles related to specificity, expense, and accessibility, which restrict their broader use in food fraud prevention.

AI and blockchain solutions are being explored to enhance traceability and detection capabilities. However, these technologies face barriers such as high implementation costs, data integration difficulties, and limited standardization, which have slowed their adoption in the food industry.

The use of AI in food fraud detection is further complicated by data limitations, integration challenges, and the evolving tactics employed by those committing fraud. Additionally, AI can be used by fraudsters to create fake documentation, adapt their methods, and impersonate supply chain participants, which can make fraudulent activities more difficult to uncover.

* This article is based on publicly available information at the time of writing.