# Enhancing the Flavor Profile of Summer Green Tea via Fermentation with _Aspergillus niger_ RAF106
## 3 Results  
### Changes of Tea Volatiles during Fermentation  
#### 3.1.1 E-Nose Evaluation  
The radar plot effectively visualizes the response degree of 10 sensors in the electronic nose to the aroma of tea leaves at different fermentation times. Although there are similarities among the sample profiles, variations in signal intensity were observed among certain sensors, particularly W1W, W2W, W1S, W2S, and W5S. This observation showed a significant increase in organic volatiles such as sulfides, organic sulfides, methyl compounds, alcohols, aldehydes, ketones, and nitrogen and oxygen compounds after fermentation. Notably, the most substantial changes occurred after 6 days of fermentation (Figure 2A).  
Principal component analysis (PCA) is a comprehensive evaluation method that employs dimensionality reduction calculations to condense multiple variables into a few principal components while maximizing the retention of original index information. According to the results of the PCA analysis, two principal components (PCs), namely PC1 and PC2, were derived, explaining 98.63% and 1.12% of the total variance, respectively, with a cumulative contribution rate of 99.75%, which indicated that these two principal components effectively captured the main aroma information of tea leaves at different fermentation times. The distribution areas of CK, D2, and D4 were adjacent to each other; however, the distribution area of fermented tea for 6 days was far removed from the other samples. Therefore, with the increase in fermentation time, the aroma of tea showed a regular change, and the change in tea aroma was more obvious on the 6 th day of fermentation (Figure 2B).  
Figure 2: E—nose data of tea at different fermentation times: **(A)** Radar plot **(B)** PCA plot **(C)** LDA plot **(D)** Loading analysis plot.  
LDA analysis effectively discriminates between groups of samples by preserving all sensor information, thereby bringing similar samples closer together and separating those with larger differences. When applied to the electronic nose data of tea leaves with varying fermentation times, the two principal components account for a total contribution rate of 99.82%, capturing most of the tea aroma information. The distinct distribution patterns observed among the different fermentation time samples indicate significant variations in their aromas. Specifically, D2, D4, and D6 were clustered together on one side, suggesting relatively minor differences among them. However, D2 and D4 exhibit closer proximity to each other, indicating higher similarities in their aroma components (Figure 2C).  
Loading analysis can be employed to assess the contribution of the electronic nose sensor in differentiating samples based on their distance from the origin; a greater sensor contribution to the principal component and sample differentiation was observed as it moved further away from the origin. Sensor W1S exhibited the highest displacement in the ordinate direction, while W1W exhibited the greatest displacement in the abscissa direction, indicating that methyl analogs and sulfides significantly contribute to distinguishing tea leaves with varying fermentation times. Additionally, organic sulfides, nitrogen oxides, alcohols, aldehydes, and ketones also play a role in this discrimination. The loading analysis results were consistent with those obtained depicting electronic nose ray response data (Figure 2D).