<p>Honey, with its exceptional nutritional and therapeutic properties, is a valuable natural resource cherished by humanity. Its authenticity holds significant importance from both commercial and health perspectives. This study evaluates the performance of several analytical techniques utilizing chemometric models to distinguish the botanical origin of honey samples. These techniques include physicochemical parameter analysis, Fourier-transform infrared spectroscopy, and ultraviolet spectroscopy. Multivariate statistical models using the principal component analysis method on the dataset from each analysis technique were established to classify eight major botanical origins of honey in Vietnam, including monofloral honey with high commercial value. Out of the three approaches, ultraviolet spectroscopy stood out as a simple and efficient method by explaining more than 93% of the total variance using the first two principal components, and successfully distinguished three botanical origins from the other groups. While varying performance was observed across individual models, the combined and simultaneous use of these models could improve the accuracy of the group classification, of which up to 90.9% of the samples in the test set were identified correctly by the combination of the two spectroscopic approaches. These findings support the use of statistical analysis methods as effective tools for the categorization of the botanical origin of honey, assist market control, and commodity authentication purposes.</p>