## 3 Results and Discussion
### Impact of Strains on the Volatilome  
HS-SPME-GC/MS was used to dose 35 volatile compounds in our fermentations. The volatile composition of each sample is presented in detail in Supplementary Table S4. A one-way ANOVA with strain as main effect was performed for each compound. The results showed that 31 compounds out of 35 showed significant differences between yeast strains. The four compounds showing non-significant differences were nonanol, benzyl alcohol, nonanoic acid, and benzaldehyde. For our study, only the discriminant compounds were considered for further statistical analyses. The ester group was most represented, with 20 esters identified and quantified. PCA and HCA were applied to volatilome data and allowed for the differentiation of five groups of strains based on Euclidean distance and the Ward method (Figure 3). The PCA score plot of the first two components explained 52.5% of the variation (Figure 3), with the first principal component (Dim1) accounting for 30.6% and the second (Dim2) accounting for 21.9%. The five clusters of samples from the HCA were projected onto this PCA using different colors. The S8 and S2 strains appeared to form separate groups. The sluggish fermentation of S8 observed was also associated with a distinct volatilome and was in line with a contrasting metabolomic signature. This strain correlated with 1-hexanol andlinalol. S2 is associated with isoamyl acetate, ethyl myristate, and phenylethyl acetate. On the other  
Figure 3: Biplot of PCA (Dim1 vs. Dim2) analysis applied to significantly different volatile compounds found in the twelve fermentations carried out by the different strains of _Saccharomyces cerevisiae_. Colored samples represent different classes obtained from HCA. hand, other groups, like S5 and S7, were related to ethyl nonanoate and decanoate, while S1, S3, S6, and S12 were associated with three medium-chain fatty acids (MCFA) (hexanoic, octanoic, and decanoic acids). Finally, S4, S9, and S10 were correlated with higher alcohols, such as 1-octanol and 1-heptanol. Another representation made it possible to highlight that across our twelve strains, an overexpression of certain families of VOCs by some strains (Figure 4), in addition to the significant diversity of expression of volatile compounds, was observed. S5 and S7 overexpressed the same six esters, and S4 overexpressed mostly higher alcohols. S2 resulted from an adaptative evolution by orientation of the carbon flow towards the pentose phosphate pathway, overexpressing 8 of the 20 quantified esters, confirming previous studies [44; 45]. On the contrary, parental strain S1 predominantly overexpressed intermediates of these esters as MCFA. Indeed, [PERSON] et al., 2012 [44] reported an increase in the concen  
Figure 4: Heat map of volatile compounds produced by the different strains of _Saccharomyces cerevisiae_. Columns correspond to volatile compounds that presented significant differences between twelve strains. Colored samples represent different class obtained from HCA. tration of some acetate esters by strain S2 evolved from parental strain S1. For example, the concentration was 2.5, 3.2, and 3.2 times higher for isoamyl acetate, isobutyl acetate, and phenylethyl acetate, respectively. The initial composition of the matrix in nitrogenous sources [46], as well as the preferential nitrogenous sources [47], could lead to this modulation of the concentration of the volatile compounds according to the strain of _Saccharomyces cerevisiae_ involved. Furthermore, this variety of production of these compounds is subject to the expression of several genes and variations in the expression level of several transcription factors [48; 49; 50]. [PERSON] et al., 2020 [51] also recently highlighted the involvement of loci and genes in nitrogen metabolism and its assimilation, influencing the formation of these volatile compounds, using the QTL mapping approach.