Honey adulteration remains a major challenge for ensuring food authenticity and sustainable quality control. In this study, near-infrared (NIR) spectroscopy and hyperspectral imaging (HSI) were comparatively evaluated as green, non-destructive analytical techniques for the discrimination of pure and adulterated honey using chemometric modeling. A total of 180 honey samples, including pure and adulterated samples with agave syrup, sucrose syrup, or water at varying concentrations, were analyzed using two NIR platforms (MicroNIR™ 1700 and NIRS™ DS2500) and an HSI system (Micro-Hyperspec® NIR camera). Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were applied for exploratory analysis and supervised classification, respectively. Both techniques enabled effective discrimination between pure and adulterated honey. The results demonstrated that the two NIR platforms achieved superior classification performance: the MicroNIR™ 1700 yielded overall sensitivities, specificities, and accuracies of 100%, respectively. While the HSI system provided complementary spectral-spatial information, its performance and that of the NIRS™ DS2500 were slightly lower, with an overall accuracy of 93.10%, particularly at low levels of adulteration (≤10%). Overall, these results demonstrate that NIR-based spectroscopy is a reliable, fast, non-destructive, and eco-friendly analytical tool for testing the authenticity of honey. The portable NIR system, in particular, provides a cost-effective and field-deployable solution for in situ quality control. Integrating it into routine quality control practices could help prevent food fraud, protect consumer trust, and promote sustainable industry development.
Bilgin et al. (2026) studied this question.