Authentication and quantification of adulterants in high-quality honey from the Brazilian semiarid region based on NIR spectroscopy and chemometrics

Abstract

This study proposes a rapid, non-destructive, and sustainable methodology based on near-infrared (NIR) spectroscopy combined with chemometrics to authenticate high-quality multifloral honeys produced in the semiarid region of Piauí and to quantify regional adulterants. Authentic samples were obtained from certified cooperatives, while binary mixtures were prepared in the laboratory using sugar and corn syrups and acidic adulterants (vinegar and lemon), which are regionally employed to alter acidity, flavor, and extend shelf life. Spectral preprocessing techniques (SNV, MSC, and Savitzky–Golay derivative) and different multivariate models were applied. Among the classification models evaluated (SIMCA, DD-SIMCA and OCPLS), all achieved maximum sensitivity (1.00), with the classic DD-SIMCA providing the best overall performance by reaching the highest specificity and accuracy (0.95). Partial least squares (PLS) regression models were also developed to predict purity and quantify adulterant syrups, presenting high predictive power (R² > 0.95) and low systematic bias. The model for sugar syrup yielded the lowest prediction errors (RMSEP = 2.61%), indicating greater robustness. The results confirm the effectiveness of NIR spectroscopy combined with chemometrics as a promising alternative for quality control, traceability, and fraud detection in honeys from the Caatinga biome.