Juarez Vicente, Volmir Kist, Caciane Peinhopf Mega, Otávio Bagiotto Rossato, Gilmar de Oliveira Veloso, Maria Geralda de Carvalho
C NMR, mid-infrared FT-IR, and near-infrared NIR) combined with multivariate chemometrics has matured into a core analytical paradigm for rapid screening, classification, and quantification of fraudulent and adulterated matrices across the agro-food chain. This review synthesizes advances (2005-2025) in instrumentation, spectral preprocessing, algorithmic modeling, validation strategies and data fusion, with particular emphasis on robustness, transferability and on-site deployment. We discuss matrix-specific challenges (oils, dairy, beverages, seeds, feeds, fertilizers), summarize performance metrics reported in the literature, and identify critical gaps (model generalizability, open-world detection, standardized reference libraries) that constrain regulatory uptake. Recent comparative and systematic reviews and domain-specific studies are used to underpin recommendations for best practice.