## 3 Results and Discussion
### CATA Analysis and PCA
The PG sample was characterized by pronounced sourness and grape-like taste in the CATA, consistent with the sensory evaluation showing relatively high sourness scores. In contrast, the JK sample was closely associated with a strong grape flavor in the CATA; however, the sensory evaluation indicated higher hardness, resulting in only moderate overall acceptability. Additionally, the JS and JG samples showed strong associations with astringency in the CATA and recorded the lowest scores in overall acceptability during sensory evaluation. This finding indicates that astringency is a major factor negatively affecting consumer preferences. For the PS sample, CATA indicated a relationship with an unpleasant aftertaste, but the sensory evaluation showed a moderate level of overall acceptability and a low aroma score. This suggested that the negative perception of the aftertaste may have been partially offset by a weaker aroma. Hardness was not strongly associated with any specific sample in the CATA, but sensory evaluation revealed higher hardness values for the JG and JS samples. These results suggested that the combination of hardness and flavor, rather than hardness alone, had a greater impact on consumer preference. In summary, the balance between the sensory attributes and texture plays a crucial role in determining the consumer acceptability of each sample. Particularly, astringency and excessive hardness have been identified as key factors contributing to lower consumer preference. Figure 3: Correspondence analysis biplot based on CATA data for different Yanggaeng. The plot shows the relationships between products and sensory attributes along dimensions F1 (74.82%) and F2 (14.59%). BY: control, PG: Powder Campbell Early, PK: Powder Kyoho, PS: Powder Shine Muscat, JG: Juice Campbell Early, JK: Juice Kyoho, JS: Juice Shine Muscat. All samples contained 8% grape powder or juice. Statistical analysis was performed using XLSTAT 2025 software. The PCA data are presented in Figure 4. PCA was performed to visually analyze the correlations among the sensory attributes of the Yanggaeng samples. In a PCA biplot, the farther a sensory attribute is from the origin, the greater its contribution to the explanation of the data [48]. Additionally, the relative direction and distance between attributes indicate the nature of their correlations: attributes located in similar directions are positively correlated (i.e., they tend to increase or decrease together), whereas those positioned in opposite directions are negatively correlated (i.e., as one increases, the other tends to decrease). For example, softness and hardness were nearly the opposite, suggesting that products perceived as softer tended to have lower hardness. Similarly, sweetness and unpleasant aftertaste were placed in opposing directions, indicating that higher sweetness levels were associated with a reduced unpleasant aftertaste. In contrast, grape flavor and taste were clustered, reflecting the tendency for these attributes to be enhanced. While CATA visualizes the association between products and sensory attributes, PCA enables the interpretation of the relationships among the attributes themselves and their contributions to the overall variance in the dataset. Figure 4: Principal component analysis (PCA) correlation circle of sensory attributes of Yanggaeng products. The plot displays the distribution of active variables along the first two principal components: PC1 (20.88%) and PC2 (13.33%). BY: control, PG: Powder Campbell Early, PK: Powder Kyoho, PS: Powder Shine Muscat, JG: Juice Campbell Early, JK: Juice Kyoho, JS: Juice Shine Muscat. All samples contained 8% grape powder or juice. Statistical analysis was performed using XLSTAT 2025 software.