# Volatile Metabolome and Aroma Differences of Six Cultivars of _Prunus mume_ Blossoms
## 2 Results and Discussion
### Analysis of Volatile Metabolite Differences in Six Cultivars of P. mume Blossoms  
In order to understand the differences in the composition of volatile metabolites in _P. mume_ blossoms among cultivars, unsupervised principal component analysis (PCA) was used to perform pattern recognition on all _P. mume_ blossom samples. A total of fifty-seven data from twenty-nine samples (including two repetitions of each sample; one GLH sample data was excluded) and nine data from the QC sample were imported into the SIMCA software and, after performing Pareto scaling, a principal component analysis model was established. In the two-dimensional score plot of the first two principal components (Figure 1), the variance explained by the first two principal components was 47.7% and 25.7%, indicating that the PCA model had a good explanation rate for the difference in the original data. The QC samples were close to the origin and clustered well, indicating that the HS-SPME-GC-MS analysis method used in this study was stable and reliable. Samples were clustered mainly according to cultivars in the PCA score plot. The cultivars YD and DFZS clustered well and separated from other cultivars, indicating significant inter-cultivar differences. The cultivars GLH and GF, LE and JM were closely clustered in the first principal component and not significantly separated from each other in the second principal component, indicating some similarity between GLH and GF, LE and JM in terms of the volatile metabolites in blossoms. A supervised PLS-DA was used to investigate the differential metabolites among the six cultivars of _P. mume_ blossoms. The 57 data were divided into categories according to the sample information in advance, and a supervised PLS-DA model was established (R\({}^{2}\)X = 0.961, R\({}^{2}\)Y = 0.957, Q\({}^{2}\) = 0.867), as shown in Figure 2A. R\({}^{2}\)X and R\({}^{2}\)Y higher than 0.9, in this model, represent that the model has a high interpretation rate for the X and Y variables, and the high Q\({}^{2}\) represents a good model fit and high predictive ability. The cross-validation results of 100 permutation tests indicated that this PLS-DA model was reliable without overfitting (intercepts of R\({}^{2}\) and Q\({}^{2}\) were 0.229 and \(-\)0.741, respectively; Q\({}^{2}\) and R\({}^{2}\) of the permutation test models were both lower than the current model) (Figure 2B). The variable importance of projection (VIP) value was used to find compounds that had a greater contribution to differences. As shown in the loading plot of the PLS-DA model in Figure 2C, 15 compounds with VIP \(>\) 1 were selected as differential compounds, as listed in Table 2, including benzaldehyde, 4-allylphenol, methyl benzoate, benzyl benzoate, eugenol, (E)-cinnamyl acetate, benzyl alcohol, (E)-cinnam  
Figure 1: PCA score plot of volatile compounds of six varieties of _P. mume_ blossom samples. acetate, unkown19, alkane3, methyleugenol, (E)-methyl isoeugenol, and ethyl benzoate. These compounds made greater contributions to the differences in volatile metabolites of different cultivars of _P. mume_ blossom. Except for the unidentified unknown19 and Alkane3, the remaining 13 compounds all belong to phenylpropanol/benzene compounds, which are mainly synthesized by the shikimate pathway. Figure 2: PLS-DA analysis of volatile compounds of six varieties of _P. mume_ blossom samples. (**A**) PLS-DA score plot; (**B**) cross-validation plot of the PLS-DA model with 100 permutation tests; (**C**) loading plot of PLS-DA (red dot represents the most differential compounds, VIP > 1).