Discriminação e quantificação de adulteração de mel com xaropes à base de açúcar utilizando espectroscopia UV-Vis e quimiometria

Abstract

Honey adulteration is a growing concern in the food industry as it affects product authenticity, consumer trust, and regulatory compliance. Conventional detection methods, such as chromatography and mass spectrometry, while effective, are often expensive and require specialized equipment, making them impractical for routine analysis. To obtain the spectral data, sample preparation involved only dilution in water, eliminating the need for complex extraction or chemical modifications. In this context, UV-Vis spectroscopy combined with chemometric techniques offers a rapid, accessible, and environmentally sustainable alternative for honey authentication. With the application of Savitzky-Golay first-derivative smoothing to the spectra and interval selection by SPA (Successive Projections Algorithm), the iSPA-PLS-DA (Partial Least Squares - Discriminant Analysis) algorithm correctly classified all test samples (i.e., 100% sensitivity, specificity, and accuracy), selecting 5 out of 15 spectral intervals. Furthermore, the quantification of honey adulteration using the iSPA-PLS algorithm yielded the lowest values for relative prediction error (REP) and limit of detection (LOD), corresponding to just 5.89% and 7.02 mg g⁻¹, respectively, by selecting 10 out of 20 intervals. The selection of specific spectral intervals by SPA reduced data redundancy and enhanced model robustness, improving classification and quantification performance. Future research should explore expanding this method to include other adulterants, different honey varieties, and portable analytical systems to increase its applicability. In conclusion, the integration of UV-Vis spectroscopy with SPA-based chemometric modeling presents a powerful tool for honey authentication. This study demonstrates the effectiveness of the proposed approach in accurately identifying and quantifying adulteration, offering a practical solution for quality control and regulatory compliance. By combining analytical efficiency, environmental sustainability, and economic viability, this method represents a significant advancement in combating food fraud, reinforcing the importance of scientific innovation in ensuring honey authenticity.