## 3 Results and Discussion
### Relationships between Descriptive and Aroma Volatiles Analyses in Green Tea Samples  
The partial least square regression (PLSR) was conducted to relate the instrumental data to the descriptive data for each brew. The PLSR biplots of the first and the second brews are shown in Figures 2 and 3. When terms on the map are closer they represent a higher relationship among the attributes and chemical composition. Of 25 descriptive attributes, only the aromatic attributes were included for the analysis (excluding bitter, astringent, and tooth-etch, which are not dependent on volatile compounds). Generally, the number of sensory attributes perceived, their intensities, and the number of volatile compounds detected decreased markedly from the second brew to the third brew.  
Twelve volatile compounds were detected by GC-MS and 16 sensory attributes were perceived by the trained panelists in the first brew (Figure 2). The PLSR map shows that 95% of the instrumental data explained 38% of descriptive sensory data when the first two principal components were considered. The geraniol and linalool were explained mainly in the PC 1 and nonanal and benzenemethanol were explained in the PC 2. The geraniol, linalool and linalool oxide compounds were correlated with the floral/perfumy note. The nonanal, jasmone, and benzenemethanol compounds were related to the fruity and sweet aromatics notes.  
Twelve volatile compounds and 16 sensory attributes were found in the second brews of the green tea samples (Figure 3). The PLSR results indicated that 84% of the instrumental data explained 51% of the descriptive sensory data in the first two PCs. Geraniol and linalool were the main vectors in PC 1 and PC 2 was explained by nonanal, geraniol, and linalool. Geraniol, nonanal, and linalool oxide were also related to the floral/perfumy attributes.  
Only five volatile compounds, but 14 sensory terms, were used to describe the third brews of green tea samples. Around 96% of the instrumental data explained 25% of the descriptive data in the first two PC's. It appears that the floral/perfumy attributes are related to the presence of geraniol, linalool, and jasmone but it is hard to know for sure because the explanation rate of the descriptive data by the instrumental data was only 25%.  
Five volatile compounds and nine sensory descriptive attributes were present in the samples brewed four times. The PLSR data showed that 88% of the instrumental data explained 37% of the descriptive sensory data. As expected, nonanal, linalool, jasmone, and geraniol were related to the floral/perfumy notes.  
Only four volatile compounds were detected using GC-MS and nine flavor attributes were perceived in the fifth brew of green tea samples. The PLSR analysis showed that 87% of the instrumental data explained 54% of the descriptive sensory data. In the PLSR biplot, it was hard to draw any relationship between the volatile compounds and the descriptive attributes probably because of the low levels of both the attributes and the aroma compounds that were found in the fifth brew.  
Figure 2: Partial least square regression analysis of descriptive sensory and gas chromatography data for the first brew of green tea samples.