## 3 Results and Discussion
### Analysis of Metabolites
From this, it can be seen that there are similar compounds in these thirteen spices, but each spice has some unique metabolites, so we can distinguish them by these differential metabolites. Afterward, multivariate statistical analysis was performed on the GC-MS data, including principal component analysis (PCA) and orthogonal partial least squares discriminant analysis (OPLS-DA), as shown in Figure 3. From the score plots of PCA (Figure 3A) and OPLS-DA (Figure 3B), it could be seen that some species exhibit a clear separation trend, including _Anomum saoko_, _Alpinia galanga_, _Anomum kravanth_, _Anomum maximum_, and _Alpinia oxyphylla_ (as shown in the red circle in the figure). The four spices distinguished by PCA and OPLS-DA scatter plots are exactly the four spices with the farthest clustering distance in the clustering heatmap, indicating that the metabolites detected by GC-MS can distinguish and classify different species of Zingiberaceae to a certain extent. However, there was no significant separation between the other spices because the metabolites of these species were relatively similar, as shown in the heatmap, which made it difficult to distinguish them through GC-MS.  
Figure 2: The heatmap of hierarchical clustering analysis (HCA) of thirteen Zingiberaceae spices. A: _Anomum koenigii_; B: _Anomum kravanth_; C: _Anomum longiligulare_; D: _Anomum tsaoko_; E: _Anomum villosum_; F: _Anomum maximum_; G: _Anomum parsaoko_; H: _Alpinia galanga_; I: _Alpinia katsumdai_; J: _Alpinia zerumbel_; K: _Alpinia japonica_; L: _Alpinia oxyphylla_; M: _Zingiber striolatum_. The variable importance in projection (VIP) is the variable weight value of the OPLS-DA model, which can be used to measure the impact and explanatory power of the accumulation differences of various metabolites on the classification and discrimination of each group of samples [36]. VIP \(\geq\) 1 is a common screening standard for differential metabolites, and a total of 37 significant variables were screened (Figure 3C). Among them, terpenoids accounted for 37.84%, including 11 monoterpenes and 3 sesquiterpennes. Monoterpenes and sesquiterpennes are common chemical constituents in Zingiberaceae, and their accumulation differences contribute greatly to the classification and discrimination of samples. The second highest contributor to the model was esters, with methyl acetate having the highest VIP value and having the greatest impact on sample classification. By screening the differential metabolites of different species, the interference of redundant information can be reduced, providing the performance of classification of the model. Therefore, the metabolites with VIP \(\geq\) 1 were screened out for OPLS-DA analysis again (Figure 3D). It can be found that there is an obvious separation trend between more species, and the classification performance of the model is better than the original data model. The OPLS-DA resulted in R\({}^{2}\)(X) of 0.874, R\({}^{2}\)(Y) of 0.84, and Q\({}^{2}\) of 0.644 of the variables, which indicated good prediction ability of the model. In addition, the results of the permutation testing (n = 200) of the OPLS-DA model show that the model had no risk of overfitting (intercepts: R\({}^{2}\) = 0.437, Q\({}^{2}\) = \(-\)0.631) (Figure 3E). However, the PCA score plot and OPLS-DA score plot established by raw GC-MS data explained 29.5% (PCI:0.165, PC2:0.13) and 29.2% (PCI:0.162, PC2:0.13) of the variation, respectively. Further, the OPLS-DA score plot established by differential metabolites explained 30.2% of the variation (PCI:0.16, PC2:0.142).