# Hyperspectral Imaging as a Rapid Quality Control Method for Herbal Tea Blends
## 3 Results and Discussion
### Roibos and Cancer Bush Blend  
Principal component analysis of the image constituting cancer bush and rooibos raw materials was performed. The results demonstrated chemical differences between the two teas most observable along principal component 3 (PC3). The score image shows different color intensities for the two species where cancer bush had lower score values, with a dark blue color and rooibos showed slightly higher score values represented by a light blue-green color (Figure 7a). These differences were also displayed in the scatter plot where two clear pixel clusters can be observed along principal component 3 (PC3), each representing a different species (Figure 7b). When colored according to the  
Figure 6: (**a**) UHPLC-MS-ES\({}^{+}\) chromatograms showing the marker compound aspalathin (\(Rt\) = 5.43 min) identified in green rooibos and (**b**) mangiferin (\(Rt\) = 3.34 min) and hesperidin (\(Rt\) = 8.02 min) in green honeycomb and (**c**) all three compounds in the tea bag blend. score values, which correspond to the score image, the two clusters could be clearly assigned to cancer bush (PC3+) and rooibos (PC3\(-\)) with 7.27% chemical variation. In the principal component 2 (PC2) direction, which explained 22.80% of variation in the data, clear color gradients could be observed in the scatter plot illustrating the occurrence of various chemical compounds within each species (Figure 7b). Cumulatively, 79.5% (R2 Xcum = 0.795) chemical variation was modelled using three principal components (PCs). Using the developed principal component analysis score image, each raw material was assigned to a class and a supervised PLS-DA model was constructed. A three component PLS-DA model describing 79.3% (R2 Xcum = 0.793) chemical variation with 91.3% (Q2 Ycum = 0.93) predictive ability estimated by cross-validation was obtained. Figure 8 shows the PLS Y-image where the calibration samples are assigned different colors, each representing a particular species. The calibration set was then used to predict raw material constituents in the tea bag blend and according to pixel abundance the proportion of each raw material was determined. Figure 8b shows both qualitative and quantitative predictions of the tea bag constituents. The image clearly displays the presence and spatial distribution of cancer bush and rooibos in all three tea bags (Figure 8b). The prediction results show that tea bag 1 contained 94.9% of rooibos and 5.60% of cancer bush, tea bag 2 had 91.3% of rooibos and 8.73% of cancer bush while tea bag 3 contained 93.0% of rooibos and 7.0% of cancer bush. Table 1, which illustrates the results for all 18 tea bag images, shows that all the predictions were accurate with minimum deviation from the production formulation which is 96:4 (rooibos:cancer bush). The lowest proportions recorded were 89.4%:10.6% and the highest proportion was 98.6%:1.4%. Ultra-high performance liquid chromatography mass spectroscopy (UHPLC-MS) was used to identify marker molecules of each of the species and to confirm their presence in the tea bag blends. Chromatograms representing the raw materials (rooibos and cancer bush) and the tea blend are illustrated in Figure 9. Aspalathin (\(Rt\) = 5.43 min) was identified as the major constituent in the chromatographic profiles of both rooibos (Figure 9a) and the tea bag blend (Figure 9c) when analyzed in the positive ion mode. Chromatographic profiles of cancer bush confirm the presence of SU1, which is the marker compound in cancer bush. Figure 9b shows the presence of SU1 (\(Rt\) = 10.80 min) in the raw material and the same compound was also identified in the tea bag blend (Figure 9c). These results confirm that cancer bush was included in the tea bag blend. Figure 7: (**a**) Score image of rooibos and cancer bush after PCA analysis and (**b**) the corresponding scatter plot (t2 vs. t3) colored according to t2 score values.