UV–Vis spectralprint-based discrimination and quantification of sugar syrup adulteration in honey using the Successive Projections Algorithm (SPA) for variable selection

Abstract

This work developed, for the first time, an improved analytical strategy for discriminating and quantifying honey adulteration by adding corn and agave syrups using the Successive Projections Algorithm (SPA) for variable selection in UV–Vis spectral analysis. Sample preparation involved dilution in water alone for obtaining the spectralprint data. By applying the first derivative Savitzky-Golay smoothing to spectra and interval selection by SPA, the iSPA-PLS-DA algorithm (Partial Least Squares - Discriminant Analysis) correctly classified all test samples (i.e., 100 % sensitivity, specificity, and accuracy) selecting 4 out of 15 intervals. Additionally, the quantification of adulteration honey using the iSPA-PLS algorithm achieved the lowest relative error of prediction (REP) and limit of detection (LOD) values of only 5.89 % and 7.02 mg g⁻¹, respectively, selecting 10 out of 20 intervals. The proposed method aligns with White and Green Analytical Chemistry principles, being simple, quick, affordable, and eco-friendly. It also aids in developing future protocols and legislation for honey quality.