AI-Driven Food Fraud Detection Systems: A Critical Systematic Review of the Detection–Prevention Gap

Food fraud is a persistent global threat estimated to cost the food industry over USD 30 billion annually. The integration of artificial intelligence (AI) with analytical instrumentation has generated significant research activity directed at developing detection systems capable of identifying adulteration, mislabeling, and substitution across diverse food matrices. This systematic review critically examines the extent to which AI-assisted instrumental technologies contribute to food fraud prevention, and identifies the structural limitations that constrain their real-world implementation. A systematic search of peer-reviewed literature published between 2021 and 2026 yielded 72 eligible studies after application of predefined inclusion criteria. Studies were required to report quantitative performance metrics (accuracy, R², RMSE, AUC, sensitivity, specificity), describe methodological limitations, and mention laboratory or industrial implementation contexts. Data were extracted into a structured seven-sheet workbook covering study characteristics, instrumental technologies, AI architectures, performance metrics, industrial validation status, implementation evidence, and methodological quality. The corpus reveals a systematic pattern of high reported analytical accuracy—frequently exceeding 95% and in many cases reaching 100%—under controlled laboratory conditions. However, 75% of studies (54/72) conducted no external validation, 100% of studies reported no pilot-scale or routine monitoring application, and no study achieved inter-laboratory validation. The predominant technology was NIR spectroscopy (26/72 studies, 36%), followed by gas chromatography-based systems (14/72, 19%) and electronic noses (8/72, 11%). Classical machine learning—predominantly SVM, Random Forest, and ANN—dominated methodological approaches (43/72, 60%), with deep learning architectures accounting for 26% of studies. Technology Readiness Levels were unreported in 97% of studies. Methodological quality was predominantly moderate (42/72 studies scoring 3/5), with 19 studies scoring 2/5 and only one achieving the maximum score. This review identifies a structural gap between detection and prevention as the central finding: the scientific literature consistently demonstrates high analytical precision in laboratory settings while providing minimal evidence of real-world industrial deployment, regulatory integration, or measurable impact on the prevention of food fraud events. The findings demonstrate that the limitation is not primarily technological but systemic, highlighting the need for a paradigm shift from performance-driven research toward validation-driven, deployment-oriented frameworks.