## 2 Results and Discussion
### Multivariate Statistical Analysis  
The PLSR has become the most popular multivariate statistical analysis method. It is a useful tool when multicollinearity exists among explanatory variables and when the number of explanatory variables is much larger than the number of observations [22]. PLSR is widely used in the research of wine, such as the relationship between sensory evaluation and flavor substances, the relevance to infrared absorption and sensory analysis, the correlation with convergence and non-volatile components, and the relations between anthocyanins and their color in aqueous solution [23; 24]. Figure 2 shows the chemical and sensory results of the Cabernet Sauvignon wines overlaid over the five different vineyards in Loess Plateau, with the wines projected on to that space.  
In this picture, the distance between the variable and the center of the circle shows the interpretive degree of the principal components to the variable. The greater the distance from the center point to the variable, the better the interpretation of the relations between the first two principal components and the explanatory variables. On the contrary, if the variable was near the center of the circle, it meant that much information on the variable was lost to other dimensions and the interpretive degree was low. The distance between the two variables explains the correlation between them. The closer the two variables are, the greater the positive correlation between them. If the variables are in two opposite positions away from the center of the circle, they have negative correlations. If the two variables are in the vertical position, they are not relevant.  
Figure 2: Partial least squares regression (PLSR) analysis, chemical, sensory data and the correlation of Cabernet Sauvignon wines from the Chinese Loess Plateau region (t1, sensory data; t2, chemical data). SLP, TYP, NT, DA and DST are the abbreviations for five young vineyards in the Loess Plateau region.  
The correlation model of the Loess Plateau Cabernet Sauvignon wine region's sensory characteristics and aroma compounds was established to determine the influence of various aroma active substances on aroma attributes, using PLSR. As we can see from Figure 2, the aroma attributes of blackcurrant and mushroom can be used to distinguish the aroma of TYP and other regions. At the same time, TYP was also well related to 1-octen-3-one, 2-heptanol, 4 MMP, 4 MMPol and 3-hexen-1-ol, which showed the aroma of mushroom, blackcurrant, boxwood and green grass, respectively. SLP had high correlation with some sweet aromas such as flower and pear. It was also well related to some esters, 3-(methylthio)-propionaldehyde and 2,3-butanedione. Cabernet Sauvignon wines of DA reflected the smell of roast, smoked and pepper more. It had good relevance to benzyl alcohol, benzaldehyde, 3-isobutyl-2-methoxypyrazine (IBMP) and furfural. The red fruit was positively related to some alcohols and esters, as well as terpen, which displayed fruity and sweet aroma. Octanoic acid was shown in close proximity to the sensory attributes of dried plum. Green pepper was near the center of the circle and IBMP and green pepper were in the vertical position that meant the explanatory relationship of IBMP to green peppers was not good. While, as reported previously, IBMP did not exhibit a direct correlation with the green pepper descriptor, it was involved in a negative relationship with the intensity rating of the bourbon character [25]. The low correlation between IBMP and green pepper character could be explained by a possible masking of IBMP by other components in the wine. PLSR applied to volatile compounds with sensory descriptor datasets showed that some descriptive sensory attributes could be well correlated to chemical profiles.