## 3 Results and Discussion
### Relationship Between Composition and Collection Area  
The first step of variable selection was undertaken. Only variables statistically correlated with location (\(p<0.05\)) were retained using an ANOVA test. Aroma compounds were treated in another single ANOVA with the same condition. In addition, 3-methylbutyl acetate and Heptan-4-ol were not considered for statistical analysis, as they were present in only one out of three samples. Figure 9 shows the PCA results. The two principal components represent 89% of the total variance of the overall composition and 97% of the total variance for the aroma compounds. Figure 8: Venn diagram representing the number of aroma compounds identified in fruit from the different samples. It was possible to see that the projection of the individuals showed clusters composed of fruits from the same area, whether for the PCA carried out with global composition data (Figure 9a) or aromatic composition data (Figure 9b). Therefore, there was indeed significant regional variability. However, regarding the overall composition, there was a distinction between the Hula area, but little difference between the Malanje and Huambo areas. It is noteworthy that the aromatic profile showed a finer chemical imprint of the \"terroir\" differences. Figure 9a showed how the composition was influenced by the year of harvest. We can also note a difference between the year 2021 and 2022, which showed variability in the composition depending on various factors linked to the harvest year: ripeness, weather conditions, transport, and storage. The combined effects of graphic and climatic conditions on the biosynthesis and accumulation of aromatic compounds in fruits are well documented, particularly in grapes, with a significant impact on wine quality [76; 77]. Regarding the projected variables, it can be noticed in Figure 9a that the principal component 1 was highly positively correlated with the variables--flavonols, NDFs, ADFs, ADL, Total Ash, Mg, K, Mn, B, Zn, Na, Ca, and TA. The 2021 fruits were distinguished by higher contents of these compounds. On the contrary, the 2022 fruits from the Malanje and Huambo areas were anti-correlated with the 2021 fruits, with higher sugar and TCC and lower acids. The increase in sugar and pigments may be indicative of a slightly warmer 2022 or a minimal difference in the ripeness. As mentioned by [PERSON] et al. [78], altitude plays a crucial role in controlling plant metabolism through changes in temperature, light intensity, and the photoperiod, and therefore affects the final content of many metabolites in fruits. The principal component 2 was positively correlated with Cu, TSSs, dry matter (DM), and P. However, it was anti-correlated with anthocyanin and TPC. Factors such as high altitudes and low temperatures were directly related to anthocyanin synthesis in fruits, which may explain the difference between the Hula sample and the others. According to [PERSON] [79], areas located at high altitudes and with low temperatures allow for the accumulation of higher concentrations of monomeric anthocyanins in grapes, while in low-altitude areas, the effect is the opposite. However, [PERSON] et al. [80] presented a contrary thought when they reported that grapes grown in lower-altitude areas near the sea had higher concentrations of acylated anthocyanins and dihydroxylated flavonoids, due to higher minimum temperatures and reduced thermal amplitudes between day and night. Figure 9: Principal component analysis (PCA) performed on (**a**) overall composition related to the four samples studied; (**b**) quantified aroma compounds related to the three samples from 2022. Anthocyanins were anti-correlated with copper ions, as these were oxidizing ions that can react with these pigments and cause their degradation. This reaction was highlighted by [PERSON] et al. [81], who proved that anthocyanins are highly reactive to metals and can be oxidized by copper and iron in hibiscus extracts.