Determination of Honey Origin Using Mineral Profiling and Multivariate Statistical Analysis

Abstract

Honey authenticity and traceability have become increasingly important due to rising concerns over adulteration and mislabeling in the global market. This study aims to evaluate the potential of elemental composition for classifying honey from different origins using multivariate chemometric techniques. Six honey samples were analyzed for selected major and trace elements using spectrometric methods, followed by statistical evaluation through one-way ANOVA, Principal Component Analysis (PCA), and Hierarchical Cluster Analysis (HCA). The results showed significant differences in elemental concentrations, particularly calcium (Ca) and cesium (Cs) (p < 0.001), indicating strong discriminatory potential, while potassium (K) was dominant but highly variable. Univariate analysis exhibited limited classification capability due to overlapping distributions and small sample size. In contrast, chemometric approaches improved classification performance, where HCA showed partial clustering and PCA after standardization provided clearer separation among sample groups. This study concludes that multivariate analysis enhances the reliability of honey classification compared to univariate methods. The findings demonstrate that meaningful classification can be achieved using limited variables and small datasets, supporting the development of cost-effective and accessible approaches for honey authentication and traceability.