Advanced analytical and digital approaches for proactive detection of food fraud as an emerging contaminant threat

• Food fraud is recognized as an emerging issue with global health relevance. • The review presents advanced analytical tools for detecting diverse food adulterants. • Digital technologies enable real-time traceability and fraud surveillance. • A systems-level framework supports proactive detection and risk assessment. The integrity of the global food supply is increasingly compromised by sophisticated fraudulent methods such as adulteration, mislabeling, and substitution. These deceptive practices introduce undisclosed and harmful substances that often evade traditional detection, posing significant risks to public health and consumer trust. This review synthesizes cutting-edge advancements in analytical and digital technologies that are redefining food fraud surveillance. We explore the convergence of molecular diagnostics (e.g., DNA barcoding, clustered regularly interspaced short palindromic repeats (CRISPR), next-generation sequencing (NGS)), high-throughput spectroscopic platforms (e.g., Raman spectroscopy, mass spectrometry, biosensors), and digital innovations (e.g., artificial intelligence (AI), Internet of Things (IoT), blockchain, and lab-on-a-chip (LOC) devices). These integrated approaches enable real-time, proactive detection of fraud across complex and globalized supply chains, offering sensitivity, scalability, and traceability previously unattainable. By framing food fraud as a form of emerging contamination, this work aligns with the paradigm shift toward predictive, data-driven risk assessment in contaminant detection. The review also addresses key translational bottlenecks such as cost, standardization, and interoperability, and proposes a systems-level roadmap to bridge scientific innovation with regulatory and industrial application. This interdisciplinary perspective advocates for a future where food authenticity assurance is intelligent, holistic, and resilient in the face of evolving adulteration threats.

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Publication Details

Journal
Talanta Open
Published
2025-06-09
DOI
https://doi.org/10.1016/j.talo.2025.100499
Citations
11
Primary Topic
Identification and Quantification in Food
Type
article
Field-Weighted Citation Impact
8.87

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article

Advanced analytical and digital approaches for proactive detection of food fraud as an emerging contaminant threat

Hany Abd El-Raheem, Waleed Alahmad, Ahmed S. El-tahlawy, Ahmed A. Allam et al.
11 citations
Talanta Open
Identification and Quantification in Food
8.87
article

Advanced analytical and digital approaches for proactive detection of food fraud as an emerging contaminant threat

Hany Abd El-Raheem, Waleed Alahmad, Ahmed S. El-tahlawy, Ahmed A. Allam, Abdullah S. Alawam, Rehab H. Mahmoud, Hassan A. Rudayn
article en
11 citations

Abstract

• Food fraud is recognized as an emerging issue with global health relevance. • The review presents advanced analytical tools for detecting diverse food adulterants. • Digital technologies enable real-time traceability and fraud surveillance. • A systems-level framework supports proactive detection and risk assessment. The integrity of the global food supply is increasingly compromised by sophisticated fraudulent methods such as adulteration, mislabeling, and substitution. These deceptive practices introduce undisclosed and harmful substances that often evade traditional detection, posing significant risks to public health and consumer trust. This review synthesizes cutting-edge advancements in analytical and digital technologies that are redefining food fraud surveillance. We explore the convergence of molecular diagnostics (e.g., DNA barcoding, clustered regularly interspaced short palindromic repeats (CRISPR), next-generation sequencing (NGS)), high-throughput spectroscopic platforms (e.g., Raman spectroscopy, mass spectrometry, biosensors), and digital innovations (e.g., artificial intelligence (AI), Internet of Things (IoT), blockchain, and lab-on-a-chip (LOC) devices). These integrated approaches enable real-time, proactive detection of fraud across complex and globalized supply chains, offering sensitivity, scalability, and traceability previously unattainable. By framing food fraud as a form of emerging contamination, this work aligns with the paradigm shift toward predictive, data-driven risk assessment in contaminant detection. The review also addresses key translational bottlenecks such as cost, standardization, and interoperability, and proposes a systems-level roadmap to bridge scientific innovation with regulatory and industrial application. This interdisciplinary perspective advocates for a future where food authenticity assurance is intelligent, holistic, and resilient in the face of evolving adulteration threats.

Talanta OpenVol. 12
Beni-Suef University (EG), Chulalongkorn University (TH), Zagazig University (EG), Imam Mohammad ibn Saud Islamic University (SA), Zewail City of Science and Technology (EG)
Imam Mohammed Ibn Saud Islamic University
Zero hunger
Openalex Percentile: Top 2%
Identification and Quantification in Food
8.87
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