## 2 Results and Discussion
### Multivariate Statistical Data Analysis. Characterization of Spices  
To evaluate the ability of HS-SPME/GC-qMS data to characterize spices in terms of their volatilomic fingerprint, a statistical analysis of the chromatographic data matrix was performed using the MetaboAnalyst 5.0 web-based tool [30]. The PCA and PLS-DA were applied as multivariate analyses (Section 2.5). The PCA is an unsupervised method that was performed to visualize the difference/similarity among sample profiles and detect significant variables (i.e., VOMs) that contribute to these discrepancies. Figure 3A,B shows the PCA score plot and biplot from the analyzed spices. The variances of the first and second principal components (PC1 and PC2) were 24.4 and 22.1%, respectively, representing 46.5% of the total VOMs variability of data, allowing a good differentiation of the spices, except for saffron and cumin. Figure 3: Multivariate statistical analysis (MVSA) using principal component analysis (PCA) and partial least square-discrimination analysis (PLS-DA) of the volatile signature of spices. (**A**) PCA score plot and (**B**) biplot. (**C**) PLS-DA score plot and (**D**) selected volatile organic metabolites (VOMs) based on variable importance in the projection (VIP) score contributing to the variance observed in the PLS-DA model. The numbers in the VIP graph are described in Table 1.  
Black pepper, projected in PC1 and PC2 negative, was chiefly characterized by \(\beta\)-pinene (#22), (+)-3-carene (#25), \(\alpha\)-phellandrene (#28) and copaene (#62), whereas cinnamon samples were placed in the PC1 positive quadrant and PC2 negative quadrant and were characterized by \(\alpha\)-muurolene (#118), calamenene (#138), \(\alpha\)-calacorene (#142) and cinnamaldehyde (#150). The rest of the spices involved in the study (curry, sweet paprika, cumin and saffron) were projected in PC1 negative quadrant and PC2 positive quadrant. Curry was characterized by \(p\)-cymene (#42), citronello (#125), cuminaldehyde (#130) and dihydrojuneol (#161). On the other hand, the samples of sweet paprika were characterized by \(\gamma\)-terpinene (#36) and eugenol (#155). The VOMs responsible for the characterization of the cumin samples were \(p\)-cymene (#42), pinocamphone (#78) and cuminaldehyde (#130). Finally, saffron was characterized by (-)-\(\delta\)-calinene (#115), \(\beta\)-sesquiphellandrene (#128) and dihydrojuneol (#161). PLS-DA was used as a supervised clustering method and revealed differentiation among spices (Figure 3C). A total variance of 36.3% was obtained by the first two principal components obtained from PLS-DA. Furthermore, 15 differently expressed VOMs were found with a presented VIP score \(\geq\) 1.45, being the most relevant compounds and having the greatest discriminatory power to characterize the six spices studied (Figure 3D). Among these 15 significant VOMs, six sesquiterpenoids (\(\alpha\)-cubebene (#56), \(\beta\)-patchohene (#59), \(\beta\)-seliene (#116), \(\beta\)-calenene (#109), germacene B (#124) and valencene (#112)), two carbonyl compounds (3-methyl-butanal (#9) and 2-methyl-butanal (#8)), two hydrocarbons (2,4-dimethylheptane (#2) and 4-methyl decane (#14)), two esters (methyl salicate (133) and thymol acetate (137)), one terpenoid (\(\alpha\)-fenchene (#18)), one volatile phenol (\(\alpha\)-cresol (#152)) and one furanic compound (2-pentylfuran (35)), were found. The obtained \(p_{\text{values}}\) by one-way ANOVA with Fisher post-hoc test (\(p<0.001\)) indicated that 94 of 162 VOMs identified were significantly different among the investigated spices. Moreover, HCA was also performed using the 15 most significant VOMs identified in the spice samples obtained by ANOVA. The resulting dendrogram associated with the heat map was performed by Euclidean distance through [PERSON]'s clustering method (Figure 4), providing intuitive visualization of the data set, which along with the statistical analyses carried out previously, allows better identification of the inherent clustering patterns between each spice.