Integrated chemical profiling for honey authentication and origin discrimination

This study presents an integrative analytical approach for honey authentication by combining physico-chemical parameters, stable carbon isotope ratios, and detailed profiles of sugars and organic acids to differentiate honeys according to floral origin. The high-performance anion-exchange chromatography with pulsed amperometric detection (HPAEC-PAD) method for sugar analysis was optimized and validated for the simultaneous separation of 9 saccharides within a 35-minute run time. Additionally, nine organic acids were quantified using an optimized ion chromatography method with conductometric detection (IC-CD). Multivariate analyses (principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA)) revealed that minor saccharides (trehalose, raffinose, maltose, erlose, isomaltose), dominant sugars (fructose and glucose) and their ratios, organic acids (citric, malic, succinic, maleic, and isocitric), as well as key physico-chemical parameters (electrical conductivity, free acidity) are critical discriminative markers of botanical origin. Within the pre-screened dataset, the optimized PLS-DA models successfully classified Robinia pseudoacacia and Honeydew honeys with zero misclassification errors (class error = 0.00; R²pred > 0.86; RMSEP ≤0.14). Although differentiating closely related floral groups and utilizing multiple analytical platforms can be resource-intensive, this innovative, derivatization-free approach demonstrates strong discriminatory potential. Ultimately, integrating targeted chemical profiling provides a robust proof-of-concept framework for botanical authentication, offering a reliable tool to prevent mislabeling and adulteration of honey.