## 3 Results and Discussion
### Multivariate Analysis of Coffee Powders and Brews  
To evaluate in depth the variability of the blends according to the respective chromatographic area of the 390 volatile compounds putatively identified, a PCA was built for each coffee sample matrix, with data scaling for attributing equal weight for all compounds (Figure 3, left). The two first PCA components of the brews explained a data variability of 71.5%, which was much higher than the variability explained by these two components in the powders' dataset (42.6%). This may indicate a higher heterogeneity of the volatile composition of the solid matrix. Independently of the matrix type, origin coffee blends (Blends 4-6) were separated from the others, appearing exclusively on the negative side of PC2 for coffee powders or on the positive side of PC2 for espresso coffee brews. In both cases, this separation was mainly related to the terpenic compounds and some esters (Figure 3, right), recognized by their pleasant notes (Table S1). Moreover, in coffee powders, the varietal terpenic compounds were predominantly distributed in positive loading1 and negative loading2, contributing to the distinction of African Blends 5 and 6 (Tanzania and Ethiopia, respectively) from the American Blend 4 (Jamaica), which was placed on the negative side of PC1 and PC2 (Figure 3a), thus allowing to distinguish these blends according to their geographical origin. However, for the brews (Figure 3b), the Ethiopian Blend 6 was placed in a PC1 positive position, separated from the others (both in PC1 negative) mainly due to the higher abundance of \(\alpha\)-humulene (Table S2). The distinctive aroma of Ethiopian brewed coffees in relation to Tanzanian ones is in accordance with literature [21]. Figure 3: Principal component analysis (PCA) of the 390 volatile compounds, grouped by chemical families, putatively identified in (**a**) capsule-coffee powders and (**b**) espresso coffee brews, presenting the distribution of the samples (scores plot, left) and compounds (loadings plot, right). The coffee powders of Blends 1 to 3 were separated from the others (positive PC1 and PC2), thus revealing a more similar volatile composition among them. According to the loadings plot (Figure 3a, right) this could be explained due to the higher GC \(\times\) GC peak areas of thiophene compounds, thiazoles, oxazoles, norisoprenoids, and pyrazines (Table S2), thus suggesting possibly different aroma characteristics when compared to the origin, decaffeinated, and supplemented blends. The coffee powders from Blend Sup and Blend Dec were also separated from the others (PC1 negative and PC2 positive--Figure 3a) due to their lower chromatographic areas in almost all volatile compounds determined (Table S2). This trend was also particularly evident in the espresso coffee brew of Blend Dec (Figure 3b, Table S2). Since Blend Sup had plant extracts, resulting in a smaller amount of coffee powder per gram of sample, its coffee content was lower when compared to those of the other coffee blends, which may explain its lower volatile intensity. Moreover, as the coffee beans of Blend Dec were decaffeinated before roasting and grinding, the conditions used for this process removed many other constituents present in the coffee beans besides caffeine, thus explaining its lower chromatographic areas for almost all the identified chemical families, in agreement with the literature [25; 68]. To deeply analyze the dataset concerning the volatile pattern of the eight coffee blends (Table S2), a heat map representation of the GC \(\times\) GC peak areas for each analyzed matrix was performed (Figure 4), highlighting the differences and/or similarities among the volatile components of coffee powders and espresso brews. The representation is based on a chromatic scale (from dark blue, low values, to dark red, high values) obtained after an autoscaling treatment of the data, intending to attribute equal importance/weight for all compounds.