## 3 Results and Discussion
### Chemical Patterns Characterizing Hazelnut Phenotype
In the current study, the metabolites coverage was similar; therefore, the UT data matrix, with 2D peaks % response, was submitted to hierarchical clustering (HC) and supervised exploration by partial least squares discriminant analysis (PLS-DA) to identify distinctive patterns for Tonda Gentile Trilobata vs. Anakiuri hazelnuts. Due to the concurrent presence of several confounding variables, (e.g., post-harvest drying E1 vs. E2 and storage U vs. C) observations were pre-filtered by Fisher-ratio score (F) to exclude UT features with a negligible role in discriminating the cultivar influence on the metabolome. Supplementary Figure S2 shows: (SF2A) HC based on Euclidean distances (Z-score normalization of the data) for % response with 56 UT peaks (F \(\geq\) 4), and (SF2B) PLS-DA scores plot on targeted analytes with meaningful variations between Tonda Gentile Trilobata and Anakiuri. Results indicate that cultivar has a predominant role in the distinctive metabolites' signatures and that the harvest region has a secondary role. Tonda Gentile Trilobata harvested in Italy form an independent cluster (cluster \"a\" in SF2A) while the geographical origin, although relevant in the discrimination, has a secondary role (cluster \"b\" in SF2A). Within the most discriminant components, obtained by ranking the variable importance for the projections (VIPs), there were some amino acids (Val, Ala, Gly, and Pro); organic acids (citric, aspartic, malic, gluconic, threonine, and 4-aminobutanoic acids) and several sugars and polyols (maltose, xylulose, xylitol, turnose, mannitol, scyllo-inositol, and pinitol). Of them, Ala, Gly, and Pro have a high informational role since they may react with sugar degradation products (i.e., deoxyosones) within the Maillard reaction framework to form 2-acetyl and 2-propionylpyrroline, key-aroma compounds of hazelnuts [2]. Figure 2 shows univariate statistics, by box-plot visualization, for informative targets and their relative distribution in different samples. Pro and Ala, whose meaningful variations were discriminant between the two cultivars, also have coherent trends between the two pedoclimatic regions (Italy vs. Georgia), suggesting a predominant role of the genome on phenotypic expression. Scyllo-inositol, an inositol isomer, was close to the limit of detection for Anakiuri samples, citric acid, out of the discriminant analytes, has a higher amount in Georgian hazelnuts. Also of note, non-optimal storage conditions, conducted without relative humidity stabilization (U vs. C samples), resulted in higher amounts of several metabolites. Fructose derivatives, galactose, glucose, mannitol, erythroid, gluconic acid, succinic acid, fumaric acid, Ala, Val, and Leu showed meaningful variations (\(p<0.05\)) between U vs. C samples. These data suggest that enzymatic (endogenous and exogenous enzymes) activation most probably occurred by inducing the release of amino acids and sugars from storage deposits and cell metabolism activation. Evidence of kernel viability was confirmed in a previous study aimed at comprehensively mapping hazelnuts volatilome along 12 months of shelf-life [55]. Concerning the impact of drying conditions (E1 and E2), it was not possible to isolate, at this stage (i.e., at time 0 on fresh hazelnuts), a clear impact of drying although it is known to be decisive for long-term storage. In this case, and up to 18 months, low-temperature drying accompanied by low-temperature storage in an inert atmosphere, enables effective inactivation of enzymes and mold/bacteria development while reducing autoxidation of fats [55].