Honey is a complex natural product valued for its nutritional and therapeutic properties, including antioxidant, antimicrobial, and anti-inflammatory effects. Its composition varies with botanical and geographical origin, making authenticity assurance essential. However, increasing adulteration, including the addition of low-cost sweeteners and mislabeling of origin, continues to undermine consumer trust and distort global markets. Rapid and reliable analytical approaches are therefore critical for effective quality control. This review provides a comprehensive overview of spectroscopic techniques for honey authentication and adulteration detection, including Ultraviolet–Visible spectroscopy, Near- and Mid-Infrared spectroscopy, Raman spectroscopy, Nuclear Magnetic Resonance spectroscopy, fluorescence spectroscopy, Hyperspectral Imaging, Laser-Induced Breakdown Spectroscopy, portable and field-deployable spectroscopic devices. These techniques rely on characteristic spectral fingerprints that capture subtle compositional variations. When coupled with multivariate chemometric tools, they enable efficient extraction of meaningful information from complex datasets, enhancing both classification and prediction performance. Representative studies demonstrate that techniques such as Near-Infrared spectroscopy combined with Partial Least Squares Discriminant Analysis achieve classification accuracies above 95%, while Raman spectroscopy with Partial Least Squares Regression enables quantification of sugar adulteration at low levels with high accuracy (R² > 0.98). The spectroscopic–chemometric approaches offer powerful, rapid, and non-destructive solutions for ensuring honey authenticity and market integrity. • Honey authentication ensures consumer safety and market integrity. • Spectroscopy enables rapid and non-destructive honey fingerprinting. • Chemometrics extracts key information for robust honey authentication. • Portable and field-deployable spectroscopic devices application for honey. • Spectroscopy-chemometrics achieve up to 100% accuracy in adulterant detection.
En-Najy et al. (Fri,) studied this question.