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

Abstract

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.