{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T11:07:19Z","timestamp":1788952039074,"version":"build-2803163510"},"reference-count":86,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Foods"],"abstract":"<jats:p>The economic impact of food fraud is difficult to quantify precisely, because fraud is structurally designed to evade detection; available estimates are indirect projections rather than direct forensic accounting and are commonly cited in the range of USD 10\u201315 billion annually. The integration of artificial intelligence (AI) with analytical instrumentation has generated a rapidly expanding body of research aimed at detecting adulteration, mislabeling, and substitution across food matrices. This systematic review examines the extent to which AI-assisted instrumental technologies contribute to food fraud prevention (as distinct from laboratory detection) and characterizes the structural factors that constrain real-world translation. A systematic search of the peer-reviewed literature published between 2021 and 2026 yielded 83 eligible records (80 primary studies and 3 review articles) after applying predefined inclusion criteria. 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 shows consistently high reported analytical accuracy under controlled laboratory conditions (median of extractable classification accuracies \u2248 99\u2013100%; \u226595% in 86% of studies with an extractable value). At the same time, 68 of 83 studies (82%) reported no external validation, no study (0\/83) achieved inter-laboratory validation, no study documented routine-monitoring application, and only one study reported testing in a genuine industrial environment. The most frequently featured platforms were NIR spectroscopy and electronic-nose arrays (each featuring in 30\/83 studies, frequently in data-fusion combinations), followed by gas-chromatography-based systems (16\/83) and hyperspectral imaging (13\/83). Classical machine learning predominated (57\/83 studies coded as classical ML, with a further 11 hybrid ML\/DL designs and 12 deep-learning-only designs). A direct statistical comparison found no significant difference in reported accuracy between classical-ML and deep-learning studies (median 100% vs. 98.2%; Mann\u2013Whitney U test, p = 0.16). A pre-specified test of the hypothesis that high reported accuracy is itself a marker of overfitting was not supported by the corpus: reported accuracy was not negatively associated with external-validation status (Fisher\u2019s exact p = 0.51) or with methodological-quality score (Spearman \u03c1 = 0.15, p = 0.23). Methodological quality was predominantly moderate (49\/83 scored 3\/5; 22 scored 2\/5; 11 scored 4\/5; one study scored 5\/5), and 19\/83 (23%) carried a high risk of bias. The review\u2019s central observation\u2014a measurable gap between demonstrated laboratory detection and evidenced real-world prevention\u2014is well supported by the deployment, inter-laboratory, and routine-monitoring data. We deliberately separate this strongly evidenced conclusion from weaker inferences (e.g., the overfitting hypothesis) that the corpus cannot currently establish, and we outline a validation-driven, deployment-oriented research agenda.<\/jats:p>","DOI":"10.3390\/foods15183185","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T09:24:02Z","timestamp":1788945842000},"page":"3185","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-Driven Food Fraud Detection Systems: A Critical Systematic Review of the Detection\u2013Prevention Gap"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9920-5919","authenticated-orcid":false,"given":"Orlando","family":"Meneses Quelal","sequence":"first","affiliation":[{"name":"Carrera de Alimentos, Universidad Polit\u00e9cnica Estatal del Carchi, Tulc\u00e1n 040101, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"}]},{"given":"David","family":"Pilamunga Hurtado","sequence":"additional","affiliation":[{"name":"Carrera de Alimentos, Universidad Polit\u00e9cnica Estatal del Carchi, Tulc\u00e1n 040101, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"funding-acquisition","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2036-7032","authenticated-orcid":false,"given":"Marco","family":"Burbano Pulles","sequence":"additional","affiliation":[{"name":"Carrera de Alimentos, Universidad Polit\u00e9cnica Estatal del Carchi, Tulc\u00e1n 040101, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"138207","DOI":"10.1016\/j.foodchem.2023.138207","article-title":"Geographical traceability of soybean: An electronic nose coupled with an effective deep learning method","volume":"440","author":"Sun","year":"2024","journal-title":"Food Chem."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"143953","DOI":"10.1016\/j.foodchem.2025.143953","article-title":"Rapid evaluation of Curcuma origin and quality based on E-eye, flash GC e-nose, and FT-NIR combined with machine learning technologies","volume":"481","author":"Guo","year":"2025","journal-title":"Food Chem."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"108350","DOI":"10.1016\/j.jfca.2025.108350","article-title":"A novel manifold discriminant extreme learning machine combined with an E-nose for chili pepper identification via aroma analysis","volume":"148","author":"Chen","year":"2025","journal-title":"J. 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