Integration of advanced analytical techniques, chemometrics, artificial intelligence, and regulatory approaches for food fraud detection and global food integrity

Abstract

Background

Food fraud has become a complicated worldwide issue that jeopardizes public health, financial stability, and food authenticity. The increasing variety of fraudulent tactics necessitates cross-border regulatory coordination and analytical accuracy. This review mainly covers studies published between 2009 and 2026 and critically evaluates contemporary analytical and digital approaches that integrate advanced spectroscopic, chromatographic–mass spectrometric, and omics-based technologies with chemometric and machine-learning techniques for food fraud detection and authentication. It also looks at changing international regulations, such as European Union (EU) Food Integrity rules, Food Safety Modernization Act (FSMA), and Codex Alimentarius Commission (Codex). Emerging platforms deliver molecular-level discrimination, quantitative adulterant detection, and origin authentication with unprecedented accuracy. Food fraud detection is evolving as a result of the integration of sophisticated analytical methods with chemometric modeling, artificial Intelligence (AI), and multi-sensor data fusion. Monitoring is now quicker, more precise, and less damaging to materials thanks to these developments. At the same time, supply chains are becoming less opaque due to blockchain and Internet of Things (IoT) technologies, which enable real-time product tracking and verification. But there are still difficulties. Global advancement is nonetheless hampered by disparities in testing criteria, a lack of consensus across testing centers, and disproportionate accessibility to technology across nations. A fair, open, and fraud-proof global food system requires bolstering international collaboration, developing shared reference datasets, and coordinating scientific advances with regulatory procedures.