Rapid Assessment of Italian Honey Chemical Composition and Botanical Origin Using NIR Spectroscopy Coupled with Chemometric Analysis

Honey quality and authenticity assessment require rapid and reliable analytical tools capable of supporting both laboratory and on-site applications. Near-infrared (NIR) spectroscopy represents a non-destructive and cost-effective approach; however, its performance depends on instrument characteristics and chemometric strategies. This study compared one benchtop and two portable NIR-based systems for predicting key physicochemical parameters (moisture, electrical conductivity, glucose, fructose, reducing sugars, pH, hydroxymethylfurfural, and diastatic activity) and for discriminating botanical origin in 80 Italian honey samples. Spectral data were processed using multiple pre-processing techniques and algorithms (PLS, k-NN, Random Forest, SVM), with and without wavelength selection (siPLS and CARS-PLS), under cross-validation schemes. The benchtop system achieved the highest regression performance (R2 up to 0.91 for glucose and electrical conductivity) and the most reliable botanical classification (balanced accuracy = 0.90). Portable systems showed moderate predictive ability for bulk compositional parameters (R2 up to 0.86 for glucose) but limited classification performance. Wavelength selection resulted in only marginal improvements. Hydroxymethylfurfural and diastatic activity were poorly predicted (R2 up to 0.49), likely due to their low concentrations. Summarising, the main outcomes suggested that tested portable NIR settings are also suitable for rapid quantitative screening of chemical traits, whereas the benchtop system provide higher precision for botanical qualitative authentication.