## 3 Results and Discussion
### Relationships among Chemical, Volatile, and Sensory Profiles Using Multiple Factor Analysis  
To understand the relationships among the chemical, volatile, and sensory data sets including 4 chemical compositions, 56 volatile compounds, and 22 sensory attributes, MFA was applied to the 18 distilled samples. From the global analysis of the MFA using the three  
Figure 2: Principal component analysis loadings for 22 sensory attributes (red) and scores for the 18 distilled _soju_ samples (blue). The samples and sensory attribute codes are defined in Tables 1 and 2.  
data sets, the first two factorial axes accounted for 36.9% and 11.7% of the total variance, respectively. The first eigenvalue, 2.47, of the global MFA was very close to the maximum eigenvalue that could be reached, while the eigenvalue for the second factor was 0.78. In this sense, the first factor was a major direction for the interpretation of the MFA. The coordinates, respective contributions, and squared cosines of the groups of variables of the first two factors of the global MFA are presented in Table 6. The coordinates of the three data sets were highly related to the first factor as shown by the values of 0.84, 0.76, and 0.86 for the chemical, sensory, and volatile groups, respectively. The three data sets showed a similar contribution (30.9-35.1%) to the formation of F1, while volatile (42.5%) and sensory (25.6%) showed a difference in the formation of F2 (Table 3). The RV coefficient, which is a multivariate analogue of the correlation coefficient in MFA, was 0.59 between the chemical and volatile data, which is stronger than the RV coefficients of the chemical and sensory data or the volatile and sensory data, with values of 0.45 and 0.53, respectively. Figure 3 shows the projections of the active variables in the global MFA. The four chemical parameters, namely, lactic acid, TA, Brix, and pH, showed a strong contribution to F1 with values of 12.9, 10.1, 6.0, and 4.9%, respectively. Secondly, the sensory variables such as 'earthy', 'yeasty', 'barley','soy_A','salty', and'sour' on the positive side of F1, with'sweet', 'fruity', and fruit-related attributes on the opposite side, showed a strong contribution to the first factor. This distinction also appeared in the PCA of the sensory data (Figure 2). However, 'woody_A','sour_A', 'bleach_A', 'body', and 'alcohol_A' were found to show a weak contribution to F1 and F2. Because the volatile data consisted of 56 compounds, many compounds showed little contribution to the formation of factors 1 and 2, especially those positioned near the centroid. However, ethyl trans-4-decenoate (es36), ethyl nonanoate (es25), 1-nonanol (al15), ethyl 3-phenylpropanoate (es54), ethyl undecanoate (es41), ethyl tetradecanoate (es56), octanoic acid (ac2), ethyl lactate (es16), 3-methylbutyl octanoate (es23), diethyl succinate (es37), and ethyl phenylacetate (es45) showed a strong contribution to F1, with a clear association with the 'earthy', 'yeasty', 'barley','soy_A','salty', and'sour' attributes. Furthermore, all of these compounds also showed strong positive correlations with TA and lactic acid, as well as with the 'earthy', 'earthy_A','salty', and'sour' attributes (\(p<0.05\)). Conversely, fruit- and sweet-related sensory attributes were highly correlated with isobutyl acetate (es4), isoamyl acetate (es8), and 2-phenylethylal acetate (es47) (\(p<0.05\)). The distribution of the 18 samples in the global MFA is presented in Figure 4. This plot shows an overall similar pattern to the sensory PCA. MSO, which showed prominent yeasty and earthy flavors under atmospheric pressure distillation, was located on the far-right side, with a dominant contribution (56.8%) to F1. Other atmospherically distilled samples such as PJU were also positioned on the right side of F1. However, atmospherically distilled GSO and R40 did not show a similar grouping. Other samples such as JRO, DJA, R25, and HBI showed higher levels of contributions to F1 in the range from 5.14 to 7.63%.