AI Meets Analytical Chemistry: The Next Era of Food Forensics

Food fraud and adulteration continue to threaten food safety, consumer confidence and international trade. The challenges of investigating food-forensics reliably are compounded by increasingly complex supply chains, changing deceptive practices and difficulties in interpreting high-dimensional analytical data. The review critically evaluates current analytical chemistry and artificial intelligence (AI) approaches for the detection of adulteration, authenticity verification and determination of geographical or botanical origin in seafood, spices, meat, dairy products, oils, honey and beverages, distinguishing between direct forensic applications and complementary studies of food quality. The methodological framework includes liquid chromatography–mass spectrometry (LC-MS), headspace solid-phase microextraction coupled to gas chromatography–mass spectrometry (HS-SPME-GC-MS), isotope-ratio mass spectrometry (IRMS), Raman and near-infrared spectroscopy, and sensor arrays, chemometrics, machine learning (ML), convolutional neural networks (CNNs), explainable artificial intelligence (XAI), and multimodal data fusion. The reviewed literature shows that classical chemometric approaches are still useful for the small datasets usually employed in food-forensic studies, while deep learning offers more advantages for larger or high-dimensional datasets but the reliability is dependent on appropriate validation, model interpretability and chemically meaningful features. Future research should be geared toward developing standardized cross-laboratory datasets, strong external validation, explainable and data-efficient models, multimodal data fusion, portable analytical tools and regulatory structures to promote transparent and defensible forensic choices.