## 3 Results and Discussion
### The Correlation Analysis between Amino Acids in Grape Berries  
The difference of amino acid content in wine grape will affect the fermentation speed; Arg, Ala, and Glu are important nitrogen sources of Saccharomyces cerevisiae, while Pro and Thr are difficult to be utilized by yeast [35]. However, the low content of Arg in this study may be related to the soil type in this region. The ethyl propionate, isobutanol, acetic acid, and octanoic acid in wine aroma components can be expressed by multiple regression model with eight amino acids: Asp, Thr, His, Phe, Ser, Pro, Arg, and Met [34]. A correlation analysis of amino acids showed that Asp was positively correlated with Thr, His, Ser, and Pro, and negatively correlated with Arg significantly under CK treatment (Figure 4A). There was a significant positive correlation between Thr, His, Ser, Arg and Pro under T1 treatment; Phe was negatively correlated with Arg and Met. There was a significant positive correlation between Ser under CK, T1, and T2 treatments. Pro, and Ser were negatively correlated with Arg. There was a significant negative correlation between Pro and Arg and Met under T3 treatment. Therefore, this region should supply nitrogen fertilizers after vineyard regulation to improve the content of Arg. Many previous studies have reported that the changes in amino acid content in grape can affect wine volatile composition [36], but rarely consider the correlation between the training systems with free amino acids. Or say, the training system could alter the delivery value or change the amount of synthetics, and therefore, it can provide a theoretical basis for fertilization in this area, so as to increase the content of volatile aroma in grape berry. Figure 4: (**A**) Correlation analysis of the contents of amino acids in ‘Cabernet Gernischt’ (_Vitis vinifera_ L.) with different treatments. The amino acids with relevant significance were marked in the red box (\(p\leq 0.05\)). (**B**) 15 amino acids on PC1 vs. PC2 by bi-plot (scores plot and loadings plot). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Free Amino Acid** & **CK** & **T1** & **T2** & **T3** \\ \hline Val & 0.09 \(\pm\) 0.003 bc & 0.086 \(\pm\) 0.004 c & 0.085 \(\pm\) 0.002 c & 0.123 \(\pm\) 0.001 a \\ Met & 0 & 0 & 0.057 \(\pm\) 0.005 a & 0 \\ Lys & 0.048 \(\pm\) 0.001 b & 0.045 \(\pm\) 0.003 b & 0.046 \(\pm\) 0.004 b & 0.072 \(\pm\) 0.001 a \\ Leu & 0.028 \(\pm\) 0.002 b & 0.032 \(\pm\) 0.005 b & 0.031 \(\pm\) 0.004 b & 0.075 \(\pm\) 0.001 a \\ Pro & 0.128 \(\pm\) 0.004 a & 0.169 \(\pm\) 0.003 a & 0.04 \(\pm\) 0.001 b & 0.188 \(\pm\) 0.04 a \\ Ile & 0.012 \(\pm\) 0.003 b & 0.014 \(\pm\) 0.002 b & 0.014 \(\pm\) 0.001 b & 0.036 \(\pm\) 0.001 a \\ Phe & 0.299 \(\pm\) 0.001 a & 0.266 \(\pm\) 0.007 ab & 0.122 \(\pm\) 0.006 c & 0.165 \(\pm\) 0.07 bc \\ Total free amino acid & 1.2 \(\pm\) 0.06 b & 1.191 \(\pm\) 0.01 b & 1.036 \(\pm\) 0.03 b & 1.592 \(\pm\) 0.14 a \\ \hline \hline \end{tabular}
\end{table} 
Table 3: _Cont._Figure 4B shows that the principal components (PC) contribution rate of 15 amino acids compound cumulative variance is 99%, representing 78% and 21% of the total attributable to PC 1 and PC 2, respectively. The distribution of different treatments is more scattered, CK and T1 were located in right bottom quadrant and had highly relevance with Arg and Phe; T2 was located in right bottom quadrant and had high relevance with Met and Ala; T3 was located in the left bottom quadrant, which was associated with mostly amino acids. According to the previous study, the mainly aroma-producing and flavoring-producing substances in Cabernet Gernischt are (\(Z\))-3-hexen-1-ol, \(\beta\)-damascenone, \(\beta\)-ionone, (\(E\), \(Z\))-2,6-nonadienal, 4-terpineol, \(\alpha\)-terpineol, benzyl alcohol, 2-phenylethanol, and phenol [37]. The above substances will impart the unique floral and grassy aroma, and we often think that grassy is the typical variety aroma of Cabernet gernisch. Otherwise, [PERSON] et al.