## 2 Results
### Identification of Key Volatiles  
Variation in contents of volatiles observed in Figure 2 prompted us to investigate if finger citron organs could be distinguished based on volatile profiles. A PLS-DA model was constructed to explore the separation of organs using volatiles as variables. Permutation and cross validation tests validated the significance of the treatment differences with \(p<0.01\) (Figure S1A,B), indicating that the obtained  
Figure 2: Hierarchical clustering and heat map visualization of changes in volatiles identified in developing finger citron. The left side of the heat map represents hierarchical clustering based on Pearson correlation. The color scale (\(-2\) to \(2\)) is shown on the right; red represents high content, blue represents low content. F1, flower bud; F2, full flower, L1, young leaf; L2, mature leaf; S1–6, fruit developing stages 1–6. Hierarchical clustering and heat map were constructed using MetaboAnalyst 4.0.  
PLS-DA model is reliable. A separation of the finger citron leaf, flower, and fruit was observed in the PLS-DA score plot (Figure 3A) with no points overlapped. In order to identify biomarkers for discriminating finger citron samples, variable importance in projection (VIP) scores were calculated for each volatile. It has been reported that variables with value exceeding 1.0 play important roles in the PLS-DA discriminant process [48]. A total of 24 volatiles with VIP \(>\) 1.0 were identified (Figure S1C), indicating these were volatiles responsible for clustering and separation of finger citron samples. Among the top 10 compounds, nine were monoterpoids, including \(\gamma\)-terpinene, \(\alpha\)-terpinene, camphene, terpinolene, \(\alpha\)-pinene, \(\alpha\)-thujene, \(\beta\)-pinene, terpineol, and \(\beta\)-terpineol. Compared to flower and leaf, the concentrations of these monoterpoids were higher in fruit, and they tended to accumulate during fruit ripening (Figure 2, Table S1). For volatiles mentioned above, terpinene isomers \(\gamma\)-terpinene and \(\alpha\)-terpinene were the top two volatiles with high VIP values of 1.78 and 1.76, respectively.  
Our previous study observed distinct volatile profiles of finger citron fruit compared to various other Citrus species and varieties [25]. PLS analysis was used in order to distinguish the six species by their volatile profiles (Figure 3B). The first two PLS-factors were able to explain 99% variance of citron relative to pummelo, hybrids, sour orange, mandarin, and sweet orange and the clustering results were in agreement with classic Citrus taxonomy. Volatile compounds that have important roles in discriminating citrus fruit samples were identified by [PERSON] et al. and the VID-coefficients were calculated [25]. As an arbitrary threshold, a VID coefficient with absolute value of 0.70 was taken [49]. By comparing the VID-coefficient and VIP values for each volatile compound, 6 volatiles with VID score exceeding 0.70 and VIP score exceeding 1.0 were observed (Figure 3C). These volatiles are suggested to be characteristic compounds of finger citron separating it from other Citrus species (Figure 3).  
Figure 3: Volatiles ranked by variable identification (VID) and variable importance in projection (VIP) scores. (**A**) partial least squares discriminant analysis (PLS-DA) 3D score plot of finger citron using volatiles as variables. (**B**) PLS bi-plots of eleven _Citrus_ varieties from six species. (**C**) Key volatiles ranked by VID and VIP scores. VID scores were calculated using Unscrambler vs 10.1 according to [PERSON] et al.; VIP score was obtained by PLS-DA using online tool MetaboAnalyst 4.0.