Analytical advances in food authentication and origin traceability: from mass spectrometry to AI and IoT-enabled smart sensing systems

Abstract

Over recent years, food supply chain has been constantly widening its horizons with its increasing complexity, due to which various parameters are liable to influence food quality and safety. Traders, driven by economic interests, are inclined to deteriorate the original food quality and substitute it with illegal products. As a corollary, it necessitates the requirement of rapid authentication of agricultural food products and their origin traceability. A diverse range of analytical techniques exemplify high throughput and resolution along with amplified accuracy such that complex food matrices can be analysed. This review highlights significant advancements in mass spectrometry (MS)-based techniques, and proteogenomic techniques. Artificial intelligence (AI), internet of things (IoT), separately and collectively with non-destructive techniques have embodied modern technology and have attracted a great deal of notice, which boosts the handling of complex data. We further emphasize on the applications, portability, and sophistication of biosensing devices in synergy with AI models, which are changing the paradigm for assessing food quality and its traceability. Current research gaps regarding data leakage, real-time interpretable models, and the need for structured protocols have been reviewed. AI models, smart sensors, and IoT with their dynamic algorithms are escalating the fidelity in detecting food adulteration.