## Materials and Methods
### Statistical Analysis  
The results for all parameters, except VOCs, were subjected to a two-way ANOVA analysis using Statgraphics Centurion XVII (Statistical Graphics Corporation 2014) with treatment. Landraces were employed as factors of the analyses. Each ripening (green and red fruit) was separately analyzed. The percentage data were arcsin-transformed before analyzing. The least significant difference (LSD) at a 0.05-probability level was used as the mean separation test.  
For VOCs, the statistical analysis was performed by the JMP software (SAS Institute Inc., Cary, NC, USA). The determination of ripening and the landrace effect on VOC composition was made by the following linear model:  
\[\text{y}_{\text{ijk}}=\upmu+\text{R}_{\text{i}}+\text{V}_{\text{j}}+\text{R}_ {\text{i}}\times\text{V}_{\text{j}}\,\text{e}_{\text{ijk}},\]where \(y_{ijz}=\) dependent variables; \(R_{i}=\) fixed effect of the \(i\)th ripening level (green; red); \(V_{j}=\) fixed effect of the \(j\)th pen (2 levels); \(\varepsilon_{ijk}=\) random residual.  
To comply with ANOVA assumptions, data were tested for normality by the Anderson-Darling test (\(p<0.05\)).  
Multiple comparisons among treatments were performed by [PERSON]'s least significant difference (LSD) at \(p\leq 0.05\).  
A multivariate statistical analysis was performed to analyze the VOCs profile by three complementary techniques to discriminate six groups (CG, VG, SG, CU, VU, SU): stepwise discriminant analysis (SDA); canonical discriminant analysis (CDA); discriminant analysis (DA) [46]. Analyses were run separately for the green and red fruit because the significant difference in the VOC profile between both two ripening levels would have made the grafting effect less evident.  
The minimum number of VOCs able to discriminate the six groups was obtained by the SDA, a statistical technique specifically considered to select the number of variables that better separate groups. Subsequently, CDA derives a set of new variables, called canonical functions (CAN), which are linear combinations of the original interval variables, as reported in the following equation:  
\[\text{CAN}=\text{d}_{1}\text{X}_{1}+\text{d}_{2}\text{X}_{2}+...+\text{d}_{n} \text{X}_{n},\]  
where \(\text{d}_{i}\) are the canonical coefficients (CC) that indicate the contribution of each variable in composing CAN, and \(\text{X}_{i}\) are the scores of the n original variables.  
CAN summarizes between-groups variation by highlighting their differences. In general, if k groups are involved in the study, k-1 CAN is extracted. Efficient separation between groups was calculated by the Mahalanobis distance and the corresponding [PERSON]'s T-square test [47]. The [PERSON]'s T-square test extends the Student's \(t\)-test to the multivariate domain [48].  
The ability of CAN to assign each sample to the six groups was calculated as the percent of correct assignment using DA [49]. The centroids of the six groups are calculated and, for each sample, the distances from the six centroids are evaluated. One sample is assigned to one of the six groups based on the shortest distance from the six groups' centroids [49].