## 3 Results and Discussion  
### Mapping Hazelnut Metabolome by Chromatographic Fingerprinting  
The term \"fingerprinting\" has been described for metabolomics [48; 49] and refers to analytical processes capable of unraveling compositional differences between samples. Although spectroscopic techniques, e.g., mass spectrometry (MS), nuclear magnetic resonance (NMR), and Fourier transform infrared spectroscopy (FTIR), have been used for years in metabolomic fingerprinting, modern MDA platforms offer further possibilities to exploit the concept of fingerprinting [20]. In fact, 2D peak patterns generated by comprehensive two-dimensional gas chromatography (GC\(\times\)GC) can be considered as a sample's distinctive fingerprint, and the detected compounds as minutiae features to be annotated and tracked across multiple samples. The term \"minutiae\" derives from biometric fingerprinting used in forensic science [50], therein indicating ridge endings and bifurcations on fingertips whose relative position is unique in each individual. Just as for automatic biometric fingerprinting, the localization and extraction of analytical (meta)data from 2D peak pattern features of single chromatograms enable effective cross-comparisons with an intrinsic potential of deepening the knowledge on chemical composition and components distribution. The adoption of MS detection adds orthogonal information about analytes' identities, through distinctive spectral signatures, and amounts (relative or absolute). In this study, chromatographic fingerprinting was applied by comprehensively extracting peak features information (i.e., summed data for each component peak with associated metadata) including for untargeted and targeted components. To enable effective cross-comparative analysis, peak features pattern matching across multiple chromatograms was guided by mass spectral similarity with DMF \(\geq\) 800 [23; 51] and a second-order-polynomial retention-times transformation [52; 53]. For targeted components, the approach explicitly matches corresponding peak-features across chromatograms by the target name. For untargeted components, the software matches unidentified peaks across chromatograms by peak tracking and assigns unique identifiers (#n) based on reliable re-alignment of the data. Figure 1A shows the pseudocolor image corresponding to a QC sample with targeted peak features highlighted by green circles. Of the 1000, on average, detectable 2D peaks above a response threshold of 50 signal-to-noise ratio (SNR), 80 components were identified by an electron ionization (EI) fragmentation pattern DMF above 900 and \(I^{T}\) within a tolerance of \(\pm\)15. Where possible, putative identifications were confirmed by authentic standards available in authors' laboratories. Figure 1B highlights both the untargeted (red circles) and targeted (green circles) peak features. Untargeted peak features were consistently extracted after template-based alignment and cross-matching. The process is detailed in the Materials and Methods Section 2.6 and supported by pertinent literature for those interested in its application. Untargeted components (red circles) accounted for 334 2D peaks. Table 2 lists targeted (T) components together with their average retention times (\({}^{1}t_{R}\) min, \({}^{2}t_{R}\) sec), experimentally determined \({}^{1}\)D \(I^{T}\) values, and tabulated retention indices (NIST database [54]). Supplementary Figure S1 illustrates the UT peaks % response distribution across all samples by heat-map and Hierarchical Clustering based on Pearson correlation. Data was log-normalized before computation. The primary metabolome coverage was extensive and very informative, especially in that it was possible to target 80 compounds, and in particular, 15 amino acids/derivates, 16 sugars including mono- and di-saccharides, four sugar acids, seven polyols, and 24 organic acids involved in cell metabolism. Figure 1: Pseudocolor image of a quality control (QC) sample from the blend of raw and roasted hazelnuts.