Multivariate analysis of High-Performance Thin Layer Chromatography derived data of Banksia honeys

Presentation
Background
HPTLC fingerprinting is a novel method for the identification of a honey’s floral source. The fingerprints, which are
derived from the organic extract of the honeys, allow a visual comparison of the different banding patterns (aka HPTLC
fingerprints) in order to identify similarities and differences and to guide the authentication of their floral origin.
Hypothesis
Multivariate analysis of HPTLC derived fingerprints provides a better understanding of the honeys’ floral source
compared to a purely visual analysis.
Methodology
The study is based on the analysis of 31 Banksia honey samples, 14 were identified by the beekeeper as Banksia sessilis
honey, 10 as B. menziesii honey, one each as B. grandis, B. prionotes and B. victoriae honey and 4 were not assigned to a
particular species (i.e. Banksia spp. honey). The obtained dataset consists of the individual HPTLC tracks of the organic
extracts of these honeys at 254 nm and 366 nm as well as at 366 nm and white light after derivatization with vanillin
reagent. Conversion of the images into their corresponding chromatograms allowed to record Rf values and the
corresponding intensity of each band. To account for differences in colour, band intensities were then multiplied by the
respective colour value (RBG value) and the obtained values plotted against the Rf to construct the data matrix.
Multivariate analysis was performed with the resulting scatter plot illustrating the clustering of the various Banksia
honeys based on the similarity of their HPTLC fingerprints.
Findings
Using a 60% confidence assessment, two major clusters could be clearly identified, those of B. sessillis and B. menziesii
organic honey extracts, which were in the main concurrent with the beekeepers’ assessment of the honeys’ floral origin.
The clustering also allowed assigning some of the Banksia spp. honeys to either of these two clusters.
Conclusion
Multivariate analysis of HPTLC derived data supports the distinguishing between B. sessilis and B. menziesii honeys and
also allows to potentially assign Banksia spp. honeys to either of the two clusters. It might also be a suitable
authentication tool for the floral source of other honeys.