## 2 Results and Discussion
### SPME-GC-MS Analysis
#### 2.2.2 Statistical Analysis  
Although there were some differences among each storage temperature, the chromatographic data were processed using statistical methods. Considering the results of the heterogeneity in the regression test (Table 2), significant differences (\(p<0.05\)) were found among all storage temperatures determined for \(\beta\)-pinene, \(p\)-cymenene, and terpinolene. While \(\beta\)-pinene was present in higher amounts at 4 \({}^{\circ}\)C and 20 \({}^{\circ}\)C, \(p\)-cymenene and terpinolene were present in higher amounts at 20 \({}^{\circ}\)C and 35 \({}^{\circ}\)C. Other terpen such as \(\beta\)-myrcene, \(\alpha\)-phellandrene, \(\alpha\)-terpinene, and limonene were present in higher proportions at 35 \({}^{\circ}\)C.  
Figure 1: Terpene degradation pathway of kaffir lime juice during storage for 1, 3, 7, and 10 days at 35 \({}^{\circ}\)C. The percentage data indicate the changes in the compound yields of kaffir lime juice in relation to day 0. Principal component analysis was performed in order to obtain an overall impression of the correlation between storage temperatures. The plot of the principal components (Figure 2a) shows partially differentiated groups of eigenvectors belonging to the different temperatures. Although the groups are not separated, the grouping tendency indicates that the identified terpen can be used for the purpose of kaffir lime juice discrimination. The first two principal components (PCs) described more than 72% of the total variability in the dataset. The first PC was mainly represented by terpionlene, fenchyl alcohol, \(\alpha\)-terpineol, and \(\beta\)-pinene, while the second PC was represented by \(\alpha\)-pinene, terpinene-4-ol, and borneol. The importance of these terpen was also confirmed by principal component factoring (PCF, Figure 2b) with varimax rotation, which resulted in a plot of factors corresponding to the PCA data projection (Figure 2a). The result of canonical discriminant analysis (CDA, Figure 2c) shows the discrimination of samples into three discrete zones--the discrimination score achieved 100% accuracy in correctly classifying samples according to the storage temperature. As in the case of PCA and PCF, terpinen-4-ol, borneol, \(\alpha\)-pinene, and \(\alpha\)-thujene showed the highest discriminant power. On the contrary, the stepwise discriminant analysis identified \(p\)-cymenene, \(\alpha\)-thujene, and \(\alpha\)-terpineol as the most important, and the discrimination score reached more than 86% accuracy. \begin{table}
\begin{tabular}{c c c c c} \hline
**Parameter** & **Comparison a** & **Difference** & **Standard Error** & **Probability** \\ \hline \(\alpha\)-thujene & 20–35 & 0.0760 & 0.0114 & 0.0001 \\  & 4–35 & 0.0700 & 0.0101 & 0.0002 \\  & 35–4 & 0.5680 & 0.1307 & 0.0102 \\  & 20–4 & 0.5540 & 0.1348 & 0.0124 \\ \(\beta\)-pinene & 4–35 & 25.1560 & 0.1566 & 0.0000 \\  & 20–35 & 11.0080 & 0.1564 & 0.0000 \\  & 4–20 & 14.1480 & 0.1559 & 0.0000 \\ \(\beta\)-myrcene & 35–4 & 2.0780 & 0.2460 & 0.0000 \\  & 35–20 & 1.5180 & 0.2459 & 0.0002 \\ \(\alpha\)-phellandrene & 35–4 & 0.5100 & 0.0889 & 0.0006 \\  & 35–20 & 0.1940 & 0.0889 & 0.0413 \\ \(\alpha\)-terpinene & 35–4 & 1.3680 & 0.2317 & 0.0004 \\  & 35–20 & 0.8360 & 0.2329 & 0.0377 \\ limonene & 35–4 & 6.9000 & 1.0472 & 0.0001 \\  & 35–20 & 4.3500 & 1.0468 & 0.0145 \\ \(\gamma\)-terpinene & 35–4 & 3.5340 & 0.5663 & 0.0002 \\  & 20–4 & 2.0680 & 0.5598 & 0.0350 \\ \(p\)-cymenene & 35–4 & 0.0660 & 0.0040 & 0.0000 \\  & 20–4 & 0.0300 & 0.0044 & 0.0000 \\  & 35–20 & 0.0360 & 0.0038 & 0.0000 \\ terpionlene & 35–4 & 5.2880 & 0.4622 & 0.0000 \\  & 20–4 & 2.7940 & 0.4578 & 0.0003 \\  & 35–20 & 2.4940 & 0.4601 & 0.0013 \\ fenchyl alcohol & 35–4 & 0.5020 & 0.0445 & 0.0000 \\  & 20–4 & 0.4060 & 0.0436 & 0.0000 \\ borneol & 20–4 & 0.3620 & 0.0405 & 0.0000 \\  & 35–4 & 0.2980 & 0.0387 & 0.0000 \\ terpinen-4-ol & 20–4 & 3.0060 & 0.5401 & 0.0009 \\  & 20–35 & 1.9800 & 0.5412 & 0.0342 \\ \hline \end{tabular} a  
\end{table}