## 2 Results and Discussion
### Discrimination Between CP and GCP Based on Multivariate Analysis
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**No. a**} & \multirow{2}{*}{**Compound**} & \multirow{2}{*}{**RT b** **(min)**} & \multirow{2}{*}{**LRI c**} & & \multicolumn{2}{c}{**Relative Area d**} \\  & & & & & **“Guangchenpi”** & **CP** \\  & & & & & **(GCP)** & **(n = 23)** \\ \hline
1 & \(\alpha\)-Pinene & 10.83 & 936 & [19] & 0.26 \(\pm\) 0.14 & 0.41 \(\pm\) 0.24 \\
2 & \(\beta\)-Pinene & 12.19 & 977 & [20] & 0.25 \(\pm\) 0.12 & 0.27 \(\pm\) 0.15 \\
3 & \(\beta\)-Myrcene & 12.62 & 990 & [21,22] & 0.35 \(\pm\) 0.16 & 1.06 \(\pm\) 0.88 \\
4 & \(o\)-Cymene & 13.65 & 1022 & [23] & 1.33 \(\pm\) 0.66 & 1.02 \(\pm\) 0.53 \\
5 & D-Limonene & 13.84 & 1028 & [21,22,24] & 24.29 \(\pm\) 7.45 & 55.61 \(\pm\) 26.24 \\
6 & \(\gamma\)-Terpinene & 14.77 & 1058 & [25] & 8.55 \(\pm\) 3.43 & 6.77 \(\pm\) 3.95 \\
7 & trans-4-Thujanol & 15.04 & 1066 & [21] & 0.97 \(\pm\) 1.24 & 0.46 \(\pm\) 0.29 \\
8 & 2-Cyclohexan-1-ol, & 15.98 & 1096 & [26] & 5.51 \(\pm\) 2.83 & 11.92 \(\pm\) 6.29 \\
9 & trans-p-Mentha-2,8-dienol & 16.64 & 1119 & [22] & 0.90 \(\pm\) 0.45 & 0.67 \(\pm\) 0.27 \\
10 & 4-Terpineol & 18.35 & 1177 & [27] & 2.69 \(\pm\) 0.90 & 1.37 \(\pm\) 0.87 \\
11 & p-Cymen-8-ol & 18.49 & 1181 & [21] & 0.89 \(\pm\) 0.28 & 0.26 \(\pm\) 0.14 \\
12 & \(\alpha\)-Terpineol & 18.72 & 1189 & [21] & 10.22 \(\pm\) 4.74 & 6.83 \(\pm\) 2.50 \\
13 & Perilla aldehyde & 21.00 & 1271 & [28] & 1.34 \(\pm\) 0.56 & 1.50 \(\pm\) 0.68 \\
14 & Carvacrol & 21.57 & 1291 & [29] & 3.35 \(\pm\) 1.23 & 2.06 \(\pm\) 2.81 \\
15 & Copaene & 23.97 & 1383 & [30] & 1.02 \(\pm\) 0.54 & 1.57 \(\pm\) 1.20 \\
16 & \(\beta\)-Cubebee & 24.31 & 1396 & [30] & 0.70 \(\pm\) 0.35 & 2.76 \(\pm\) 2.26 \\
17 & Benzoic acid, 2-(methylamino)-, methyl ester & 24.55 & 1406 & [31] & 40.07 \(\pm\) 10.44 & 15.60 \(\pm\) 10.47 \\
18 & Caryophyllene & 25.11 & 1428 & [30] & 3.41 \(\pm\) 1.96 & 1.61 \(\pm\) 1.37 \\
19 & D-Germacrene & 26.60 & 1489 & [25] & 0.51 \(\pm\) 0.35 & 2.72 \(\pm\) 2.42 \\
20 & \(\alpha\)-Farnesene & 27.03 & 1507 & [32] & 11.41 \(\pm\) 5.14 & 8.72 \(\pm\) 8.31 \\
21 & Cadinene & 27.55 & 1529 & [30] & 1.71 \(\pm\) 0.99 & 2.85 \(\pm\) 2.24 \\ \hline \hline \end{tabular}  
* Numbers represent the compound index for the principal-component-analysis (PCA) loading plot shown in Figure 2b. b Retention time (min). c Linear retention indices. d Peak-area ratios relative to n-tridecane (internal standard).  
\end{table} 
Table 2: Identification of volatile compounds in Citri reticulatae pericarpium (CRP). Figure 2: Multivariate data analysis of Citri reticulatae pericarpium (CRP). (**a**) Score plot of the principal-component-analysis (PCA) result. The variances accounted by the first principal component (PC1) and the second principal component (PC2) were 32.3% and 23.0%, respectively. Circles represent “Guangchenpi” (GCP) samples, and triangles represent CP samples. (**b**) Loading plot of the PCA result (PC1 vs. PC2). (**c**) Dendrograms of the hierarchical-cluster-analysis (HCA) result. Supervised OPLS-DA was applied in the discrimination, and also to define the most significant contributors toward discrimination. OPLS-DA extended a regression of the PCA, and used the class membership (as Y variables) to maximize variation, introducing an orthogonal signal correction filter to separately handle systematic variation correlated with or uncorrelated with the Y variable. Therefore, OPLS-DA had better discriminant ability than PCA for samples with larger intra-class divergence [34]. In the presented study, CP and GCP samples were clearly separated in the OPLS-DA score plot (shown in Figure 3a). The R\({}^{2}\)X of the OPLS-DA model was 0.633, indicating that 63.3% of the variation in the dataset could be modeled by the selected components. The R\({}^{2}\)Y was 0.878, indicating that the model was well fitted. The Q\({}^{2}\) was 0.826, indicating a good predictive ability.