## 3 Results and Discussion
### Apricot Fruit Headspace Volatile Analysis via SPME-GC-MS
#### 3.3.9 Aliphatic/Aromatic Hydrocarbons  
Aliphatic hydrocarbons amounted for 0.5-1% of the volatile composition in fruits, except apricot bitter fruit AP-B\({}_{1}\) that showed much higher levels (8.9%) represented by octadecane (peak 58) as major form (8.3%). Aliphatic hydrocarbons were previously identified in apricot fruit volatiles [52]. Among aromatics, \(\beta\)-methylnaphthalene (peak 32), was detected as major form in AP-B\({}_{1}\) (1.9%). p-Cymenene was present at trace levels in accordance with other reported data in apricot [48] in addition to \(\alpha\)-methylnaphthalene, diphenyl ether and dimethyl naphthalene detected at trace levels. Multivariate Data Analyses of Sweet and Bitter Apricot Fruit Cultivars Aroma Profile Cultivated in Different Soil Types  
Heterogeneity in the volatile distribution (70 peaks for volatiles) of apricot fruit accessions was explored in a more holistic way using PCA, as employed in the primary metabolites dataset (Supplementary Figure S3). PCA score plot of the apricot fruit cultivars aroma profile (Supplementary Figure S3A) was ascribed by two main vectors, i.e., PC1 and PC2, both accounting for 57% of the total variance. The performance of the classification model was validated by the computed parameters \"R2 (0.34)\" and \"Q2 (\(-\)0.1)\", which indicated the low prediction power of the model, and suggestive that aroma compounds cannot be used for fruits classification. The main volatile responsible for fruit specimens' segregation were revealed from the corresponding PCA loading plot (Supplementary Figure S4B) to include E-anethole (peak 25), being most enriched in sweet apricot fruit cultivar (AP-S\({}_{2}\)-F). HCA analysis was employed in order to better assess volatiles heterogeneity among apricot fruit specimens. HCA dendrogram showed two distinct clusters (Supplementary Figure S4C), where sweet apricot fruit cultivar (AP-S\({}_{2}\)-F), clustered in group 1, while bitter apricot fruit cultivars (AP-B\({}_{1}\)-F, AP-B\({}_{2}\)-Fand AP-M\({}_{3}\)-F) clustered in group 2. However, the clustering of all bitter apricot fruit cultivars together in group 2 indicated that HCA failed to characterize the impact of soil type based on their volatile metabolites. OPLS-DA Analyses of Volatile Metabolites in Different Apricot Fruit Cultivars  
OPLS-DA modeling of apricot fruit cultivars aroma profile (Figure 5A) was attempted in order to assess the variation amongst different apricot fruit cultivars in context to both cultivar type (sweet or bitter seed) and soil type (muddy or sandy). Figure 5: GC–MS-based OPLS-DA score plot (**A**) derived from modeling volatile metabolites of different apricot fruit cultivars (\(n=3\)). The respective loading plots (**B**) showing the covariance \(p\) (1) against the correlation \(p\)(cor) (1) of the variables of the discriminating component of the OPLS-DA model. The performance of the developed classification model was validated by the computed parameters \"R2 (0.61)\" and \"Q2 (0.22)\", which indicated improved prediction power than PCA model (Supplementary Figure S4A). The observed segregation in the derived score plot (Figure 5B) was ascribed to the enrichment of sweet apricot fruit seed cultivar (AP-S2-F) in (E)-anethole (peak 42), \(\beta\)-ionone (peak 51) and dihydroactinidiolide (peak 62) versus abundance of the lactone, \(\gamma\)-decanolactone (peak 60) and methyl palmitate (peak 68) in bitter apricot fruit cultivars. \(\gamma\)-Decanolactone is a characteristic aroma compound in Rosaceae seeds and extend herein to include apricot [49]. Further and for better segregation of sweet apricot fruit cultivar (AP-S2-F) from bitter apricot fruit cultivars (AP-M3-F, AP-B1-F and AP-B2-F), supervised (OPLS-DA) score plot Supplementary Figure S4A was implemented. Model showed Q2 = 0.57 indicating model predictability of the OPLS score plot and 90% of the total variance coverage (R2 = 0.90).