# Effect of Blue Light Intensity During Spreading on the Aroma of Green Tea
## 3 Results
### Multivariate Statistical Analysis of Volatile Compounds  
Principal component analysis (PCA) is one of the most widely used data dimensionality reduction algorithms. The PCA score plot (Figure 5A) of the four green tea samples reveals unique characteristics of the samples with good reproducibility. The first two principal components, which together account for 84% of the variation, explaining 51.3% and 32.7% of the total variance, respectively. The use of hierarchical clustering analysis (HCA) revealed the similarities and differences between the groups. According to the results, MBL and LBL were in the same cluster, while HBL and DARK were in a different cluster (Figure 5B). The clustering revealed sensory similarities and differences in volatile composition related to sensory characteristics between four green tea samples exposed to different lighting, closely matching previous PCA study results. OPLS-DA is a multivariate statistical analysis method, which is mainly used for classification and feature selection. The purpose of OPLS-DA is to distinguish samples from different groups and identify key variables that affect group classification. To explore the differences in primary compounds of samples under various lighting conditions, OPLS-DA models were constructed based on PCA and HCA analysis. According to the findings of the PCA and HCA analysis, in this study, the score plot showed a distinct separation between various sample groups, with the MBL and LBL samples clustering closer together (Figure 5C). Figure 4: Classification and content of volatile substances. (**A**) Category and contents of compounds in green tea samples under different light conditions. (**B**) Content comparison of different categories of volatile compounds. Different letters indicated significance difference (\(p\) < 0.05). R2X and R2Y denote the interpretation rates for the X and Y matrices in the constructed model, while Q2 signifies the model's predictive capability. All three metrics are nearly 1, suggesting the model's stability and reliability. During the analysis, the fit index for the independent variable (R2X) was found to be 0.992, and for the dependent variable (R2Y), it was 0.999. Furthermore, the model prediction index (Q2) was determined to be 0.993, suggesting a strong fit with excellent predictive accuracy. To examine the model's robustness more thoroughly, 200 permutation tests were conducted. The results showed that the OPLS-DA model has no overfitting, and model validation was effective (Figure 5D). The differences in green tea compounds according to the light condition were more clearly shown in the OPLS-DA loading plot (Figure 5E). Specifically, the levels of compounds, like maltol, 4-vinylphenol, limonene, dibutyl phthalate, 2,6,11-trimethyl-dodecane, and 2,3-octanedione, were notably greater in the MBL and LBL samples compared to the other treatments. Conversely, the levels of compounds, like hexanal, 4-methyl- tridecane, indole, hexanoic acid ethyl ester, and 3-methyl-butanoic acid, were notably elevated in the HBL samples. Compounds, such as 6-methyl-tridecane, 4-methyl-tridecane, 3-methyl-tridecane, and 2-ethyl-1-hexanol, were present in much higher concentrations in the DARK samples. VIP was employed to pinpoint the key volatile components that distinguished the two groups. Components with VIP scores greater than 1 were typ  
Figure 5: Multivariate statistical analysis of volatile compounds identified under different light conditions. (**A**) Principal component analysis; (**B**) Hierarchical cluster analysis; (**C**) Orthogonal partial least squares discriminant analysis. R2X = 0.995, R2Y = 0.999, Q2 = 0.999. (**D**) Hypothesis testing of the OPLS-DA model. (**E**) Orthogonal partial least squares discriminant analysis loading plot for volatile compounds. X represents 116 volatile compounds, and Y represents 4 light treatments. The numbers in the figure correspond to the serial number of substances in Table S1.