## 2 Results and Discussion
### Multivariate Data Analyses of Slylated Primary Metabolites  
Multivariate data analyses were further employed for specimens' classification of the primary metabolites dataset's analogues to the volatiles dataset. The PCA score plot (Figure 4A) revealed 2 confined clusters, with date pit specimens positioned to the right along PC1 (57% of variance) versus another cluster corresponding to coffee specimens exhibiting negative score values along PC1. Examination of the loading plot (Figure 4B) suggested that the MS signal of amino acid L-pyroglutamic acid (S26) accounted for the distant clustering of coffee specimens, whereas monosaccharide sugars S44 and S45 were more enriched in unroasted date pits. The tight clustering of roasted date pits (RS, and P1 and P2 samples) was attributed to cycloartenol triterpene (S62), along with fatty acids; viz., oleic (S31) and eicosanoic (S33) acids.  
Figure 4: GCMS-post silylation based principal component analysis (PCA) and hierarchical cluster analysis (HCA) of raw pits (RW), roasted pits (RS), pit product one (P1), pit product two (P2) and coffee product (C) (\(n=3\)). (**A**) Score plot of PC1 versus PC2. (**B**) Loading plot for PC1 and PC2. (**C**) HCA of RW, RS, P1, P2 and C (\(n=3\)); the model is colored according to marked groups.  
Compared to PCA, HCA allows interpretation of the results in a fairly intuitive graphical way. HCA retrieved a dendrogram with three clear clusters of three and 12 samples, referred to as groups 1 and 2, respectively (Figure 4C). Coffee specimens clustered together in group 1, while raw and roasted pit samples (RW, RS, and P1 and P2) clustered in group 2. Inspection of group 2 showed that (RS) samples are the most closely related to (P1) and (P2) specimens, being clustered together in a subdivision 2b. However, the clustering of raw pits (RW) with two specimens of roasted pits (RS and P1) in branch 2a suggests that HCA cannot clearly distinguish the impact of roasting on date pits. Consequently, a supervised OPLS-DA model was attempted to help classify specimens that failed to separate in HCA, and moreover, to confirm markers revealed from PCA, as in the case of volatile data.  
A model was constructed for roasted coffee against all roasted pit samples (RS, P1 and P2). The model showed one orthogonal component with \(R^{2}=0.98\) and \(Q^{2}=0.97\) (Supplementary Materials, Figure S3A). Compared with roasted pit samples (Supplementary Materials, Figure S3B), coffee is particularly enriched in L-pyroglutamic acid (S26). Pyroglutamic acid is a nonessential amino acid that possesses brain-boosting properties through encouraging the memory chemical acetylcholine's release in the brain [32]. To also assess roasting's impact on date pits, supervised OPLSDA was applied by modelling unroasted versus roasted date pits (Supplementary Materials, Figure S3C,D). The OPLS-DA score plot (Supplementary Materials, Figure S3C) explained 86% of the total variance (\(R^{2}=0.86\)), though with less prediction goodness parameter \(Q^{2}=0.76\) compared to Figure S3A. The S-plot derived from OPLS-DA (Supplementary Materials, Figure S3D) revealed that compared with unroasted, roasted samples contain more cycloartenol triterpene (S62), although it should be noted that it was found at only trace levels, thus cannot serve as a chemical marker for roasted pits, being a non-validated model component (Table 2).  
It can be concluded that analysis of both volatiles and primary metabolites justifies date's roasted pits' usage as a popular substitute to coffee beverages, in terms of its similar aroma, high nutritional value though lower sugar levels and no caffeine. However, to be conclusive, evaluation of coffee substitutes needs to be extended beyond metabolite analyses; e.g., to sensory profiles, toxicology and neuroactivity. To provide a first insight into the latter topics, first neuropharmacological bioassays will be discussed in the next sections.