# Hyperspectral Imaging as a Rapid Quality Control Method for Herbal Tea Blends
## 3 Results and Discussion  
### Rooibos and Buchu Blend  
Figure 1a shows the hyperspectral RGB (red, green, blue) image of the two raw materials (rooibos and buchu) before multivariate image analysis. Visual inspection of the image does not show chemical differences between the two teas, hence the need to apply multivariate analysis tools to reveal the chemical information. Principal component analysis of the image was performed and the results obtained are displayed in the score image (Figure 1b) and the scatter plot (Figure 1c) constructed after image cleaning, mean centering and SNV correction of the data. The cumulative chemical variation modelled (R2 Xcum) using three principal components (PCs) was 79.6% (R2 Xcum = 0.796). The three principal components (PCs) were investigated to determine the direction in which maximum chemical variation between the two teas was most prominent. According to the three score images (t1, t2 and t3), principal component 2 (PC2) demonstrated the most variation which could be attributed to separation of the two species as demonstrated in Figure 1b. The score image of t2 showed clear chemical differences between rooibos and buchu raw materials as demonstrated by the different color score values. The buchu image showed higher score values (orange-red), while a yellow-blue color (lower score value) was observed for rooibos, confirming clear chemical differences between the two teas. In the score plot of the second and third component, it was possible to visualize two clear clusters representing buchu and rooibos raw materials along PC2 (Figure 1c). The variation modelled along principal component 2 (PC2) was 23.1% which explained the chemical variation between the two clusters while the 10.8% chemical variation observed along principal component 3 (PC3) might account for compositional differences within each tea/species. Due to the interactive nature of the score image and scatter plot, it was possible to color the pixels according to the score values as in the score image, enabling clear distinction of the buchu (PC2+) and rooibos (PC2\(-\)) clusters. Both Figure 1b,c demonstrate some degree of chemical overlap between the two species as evidenced by pixels of similar score values in both species. Figure 2a is the PLS-DA Y-image of the raw materials following class assignment. The cumulative variation modelled in the X-matrix using three PLS factors was 76.7% (R2 Xcum = 0.767) and 93.2% (R2 Ycum = 0.932) in the Y-matrix. The cross-validation parameter Q2 cum, which describes the predictive ability of the model, was 93.2% (Q2 cum = 0.932) for the external dataset. The classification shown in the Y-image was then used to identify and quantify the raw material constituents in the tea bag blends. This was performed by introducing as an external dataset 18 tea bag images (3 bags \(\times\) 6 images), scanned separately and introduced into the PLS-DA for prediction. For illustration purposes, the visual prediction results of three tea bags are shown in the figures and the data for the repetitions is presented in Table 1. The prediction results correctly identified rooibos and buchu as the raw material constituents in the tea bag blends according to classification matches between the calibration samples and the prediction set (Figure 2b). Quantitatively, the model predicted rooibos as the main constituent accounting for >97% of the tea bag constituents while buchu accounted for <3% of the total composition in the three tea bags (Figure 2b). The percentage composition results were calculated from pixel abundance ratios obtained in a tabular form using EvinceTM (Version 2.5.0, Prediktera AB, Umea, Sweden). However, by making use of the pixel color coding, it is also visually clear that rooibos constitutes a higher proportion of the tea bag blend compared to buchu. Table 1  
illustrates pixel abundance ratios of rooibos and buchu in the tea bag blends for all repetitions.