## 2 Results
### Multivariate Statistical Analysis  
A statistical analysis was carried out using the MetaboAnalyst 6.0 program to obtain the principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA) as described in Section 3.5. Figures 4 and 5 show the PCA score plot and PCA loading plot, which visually display the differences and similarities between the volatilomic fingerprint of the edible flowers analyzed, as well as the distribution of VOMs in the four quadrants of the graph. The variance of the first principal component (PC1) was 41.7%, while the second (PC2) was 19.5%, so the total of both components was 61.2% of the data variability, indicating differences between the edible flowers analyzed. A cluster can be seen among _Begonia_ spp., _Antitrinum majus_, and _Lobularia maritima_, indicating that these flowers have the most similar volatilomic fingerprints. All samples analyzed, except _Acmella oleracea_, are in the positive PC1. In the case of PC2, _Rosa_ spp. and _Viola tricolor_ are in the positive quadrant, while _Borago officinalis_, _Lobularia maritima_, _Anthirinum majus_, and _Begonia_ spp. are in the negative. _Acmella oleracea_ is located right in the middle of both quadrants. The positive PC1 and PC2 quadrant is characterized by the presence of VOMs such as 3-methyl-butanal (5), hexanal (13), limonene (22), eucalypt (23), 3-methyl-1-butanol (26), 3-octanone (31), allyl isothiamate (40), (Z)-3-hexen-1-ol (42), (E)-2-hexen-1-ol (44), 1-octen-3-ol (48), 1-heptanol (49), and \(\alpha\)-muurolene (80). The negative PC1 and PC2 quadrant includes \(\beta\)-myrcene (19), (Z)-2-hexenal (24), _trans_-\(\beta\)-ocimene (29), 6-methyl-5-hepten-2-one (38), fenchone (46), co-paene (59), \(\alpha\)-caryophyllene (78), and geraniol (95). Positive PC1 and negative PC2 include acetaldehyde (1), ethyl acetate (3), ethyl hexanoate (27), 3-hexen-1-ol acetate (35), 3-butenyl isothianate (51), ethyl nonanoate (63), 1-octanol (65), \(\alpha\)-cital (82), and phenylethyl alcohol (97). Finally, negative PC1 and positive PC2 include p-cymene (32), 1-hexanol (39), and (E)-4-hexen-1-ol (45), among others. A total variance of 57.7% was obtained by the first two principal components obtained from PLS-DA (37.4% of the PC1 and 20.3% of the PC2). Furthermore, 15 differently expressed VOMs were found with presented VIP scores \(\geq\) 1.25, being the most relevant VOMs and having the greatest discriminatory power to characterize the seven culinary flowers studied. Among these 15 significant VOMs, the majority were sesquiterpenes: cyclosativene (57), humelene (74), \(\alpha\)-muurolene (80), \(\beta\)-muurolene (94), \(\beta\)-amorphene (96), caryophyllene oxide (99), neronidol (100), t-cadinol (101), and carophylladienol I (102). These were followed by esters: ethyl hexanoate (27), ethyl nonanoate (63), and linalyl formate (69); monoterpenes; \(\beta\)-pinene (14), and 3-carene (17); and, finally, one sulfur compound, 3-butenyl isothiocyanate (51). The \(p\) values obtained by one-way ANOVA with Fisher post-hoc test (\(p<0.05\)) indicated that the 102 VOMs identified were significantly different among the investigated culinary flowers. Moreover, hierarchical cluster analysis (HCA) was also performed using the 15 most significant VOMs identified in culinary flower samples obtained by ANOVA. The resulting dendrogram associated with the heat map was performed by Euclidean  
Figure 4: Statistical analysis using PCA score plot (**a**) and loading plot (**b**) of the VOMs identified in the investigated edible flowers. The numbers correspond to the VOMs (see Table 1). Figure 5: Statistical analysis using PLS-DA score plot (**a**) and variable importance in the projection (VIPs) plot (**b**) of the VOMs identified in the analyzed edible flowers. The numbers correspond to the VOMs (see Table 1).