# Hyperspectral Imaging as a Rapid Quality Control Method for Herbal Tea Blends
## 3 Results and Discussion
### Green Rooibos and Green Honeybush Blend  
The green rooibos and green honeycomb blend is prepared in a 65:35 proportion to improve the taste and medicinal properties of the tea (green honeycomb:green rooibos). Image analysis of the raw materials using principal component analysis (PCA) revealed clear chemical differences between the raw materials as demonstrated in the score image plot (Figure 4a) of the third principal component (PC). The direction of maximum variance that showed separation of the two species was along principal component 3 (PC3) while principal component 1 (PC1) and principal component 2 (PC2) did not clearly differentiate the species. In the score image, green honeycomb was presented with a yellow-red color, while green rooibos showed an intense orange-red color (Figure 4a). The high score values for both species demonstrates greater chemical similarity between the two teas. The scatter plot clearly demonstrates that only 9.23% chemical variation is observed between green honeycomb and green rooibos (t3 = 9.23%) (Figure 4b). The pixel clusters separated along t3 are based on color gradients, however a high degree of overlap is also observed, which could be due to chemical similarities between the teas such as the presence of chlorophyll [29] in the two raw materials. Altogether, the cumulative variation modelled using three principal components PCs was 79.0% (R2 Xcum = 0.790). Following the successful distinction between green rooibos and green honeycomb raw materials using principal component analysis (PCA), a partial least squares discriminant analysis (PLS-DA) model was developed to predict the identity of raw material constituents in the tea bag blends. The cumulative variation modelled using three partial least squares (PLS) factors in the **X**-matrix was 92.1% (R2 Xcum = 0.921) and the predictive ability of the model was 92.7% (Q2 Ycum = 0.927) by cross-validation using a random method of exclusion. Figure 5a is a PLS **Y**-image comprising of calibration samples where each tea/species was assigned a different color. Eighteen tea bag images (3 tea bags \(\times\) 6 images) were then introduced into the partial least squares discriminant analysis (PLS-DA) model as an external dataset. The tea bag constituents were predicted based on data from the calibration set and quantification was performed based on pixel abundance. Figure 5b is the prediction image of three tea bag blends where the tea bag constituents were qualitatively and quantitatively predicted using the developed partial least squares discriminant analysis (PLS-DA) model. Qualitatively, the predictions show that all three tea bags contained green honeybush and green rooibos only (Figure 5b). Quantitative predictions showed that the two raw materials are present  
Figure 4: **(a)** Principal component analysis score image of t3 demonstrating distinct color amplitudes for the raw materials, green honeybush and green rooibos and (b) the score scatter plot (t2 vs. t3) illustrating two distinct pixel clusters colored according to score values. in proportions within range of the company formulation (65:35; green honeycomb:green rooibos); tea bag 1 (66.7%:33.3%), tea bag 2 (63.1%:36.9%) and tea bag 3 (62.3%:37.7%). All the regions within the analyzed tea bags could be correlated to the calibration data, therefore unclassified regions were not observed. Results for all 18 images show that the lowest proportion observed was 53.60%:45.20% and the highest proportion was 94.10%:5.89% (Table 1). This may be due to the following: HSI captures spatial distribution of up to 2 mm in depth and so inadequate mixing would be captured. For this reason, bags were shaken and turned as randomly, one species might end up on the surface. Secondly, pixel overlap, which demonstrates some chemical similarities, were noted. This overlap could result in misallocation of pixel information where closely related species are concerned.