## 2 Results and Discussion
### Multivariate Data Analysis of Headspace SPME-GCMS Volatiles Dataset  
To better visualize the subtle volatile differences between roasted coffee and date pits in a rather holistic manner, multivariate data analyses were employed. Principal component analysis (PCA) is an unsupervised clustering process for identifying patterns in data, via reducing the number of dimensions. It can define a limited number of principal components which describe independent variation in the results [26]. In the present study, PCA was first applied on the GCMS volatiles abundance dataset to classify aroma profiles of roasted coffee versus date pits; i.e., R, and P\({}_{1}\) and P\({}_{2}\) (merged together as one class denoted P), were analyzed with respect to their chemical composition and to determine markers unique for each specimen. The volatilome clusters were located at different points in the two-dimensional space prescribed by two vectors; that is, principal component one (PC1) accounted for 34% of variation between samples, and PC2 explained 25% of the variance (Figure 2). Inspection of the score plot (Figure 2A) revealed that most of the roasted pit samples were placed on the right side of PC1 (positive score values), whereas coffee specimens were placed on the left side (negative score values). Examination of the loading plot (Figure 2B) revealed key aroma variables referring to MS signals of 2-acetyl-pyrrole (12), terpinyl-acetate (33) and the two aromatics; viz., 2-methoxy-phenol (35), 4-ethyl-guaiacol (37) and 4-vinyl-guaiacol (38) were all found enriched in roasted coffee. In contrast, furans/pyrans; viz., 3-furfurral (1), 3-hydroxy-2,3-dihydro-maltol (10) and benzyl-thiocyanate (41) were much more abundant in roasted pit samples (RS and P\({}_{2}\)), suggesting that the latter volatiles could be considered key odorants responsible for the roasted pits' malty, spicy and caramel like aromas [9; 13; 22].  
In spite of the clear separation observed in PCA analysis, metabolite markers were further confirmed by constructing a supervised orthogonal projection to latent structures-discriminant analysis (OPLS-DA). OPLS-DA has greater potential in the identification of markers by providing the most relevant variables for the differentiation between two sample groups. Coffee samples were modelled against all roasted pit samples; viz., RS, and P\({}_{1}\) and P\({}_{2}\) (merged together as one class denoted as P) using OPLS-DA. The derived score plot showed a clear separation between both samples (Supplementary Materials, Figure S1), with R\({}^{2}\) = 0.97 (explaining 97% of the total variance), and a prediction goodness parameter, \(Q^{2}\) = 0.85 (Supplementary Materials, Figure S1A). The corresponding derived S-plot compares the variable magnitude against its reliability, as displayed in Figure S1B, where axes plotted from the predictive component are the covariance p(1) against the correlation p(cor)(1). For the indication of plots with retention time m/z values, a cut-off value of \(p<\) 0.05 was used. Compared with coffee specimens, roasted pit samples were found to be particularly enriched in benzyl-thiocyanate (41) as a unique marker, typical of a pungent aroma and bitter taste [22], and in agreement with PCA results.