## 3 Results
### Partial Least Squares (PLS) Regression  
The relationships between the sensory attributes and the metabolite composition of wines was studied by means of partial least squares (PLS) regression. To that end, PLS1 and PLS2 regression was computed for each analytical platform (LC-MS and GC-MS), which were used as independent variables to predict the response of each sensory attribute  
Figure 8: Loading contribution barplot on components 1 and 2 for the different blocks of the sGCC-DA. Colour indicates the class for which the compound has a maximal mean value. Bar length represents the multivariate regression coefficient with either a positive or negative sign for that particular feature of each component, i.e., the importance of each variable in the model. Figure 7: Pearson correlation plot obtained from the sparse generalised canonical correlation analysis discriminant analysis (sGCC-DA) model. (dependent variables). The models were fitted using the all-relevant variables previously selected by means of the MUVR package implemented in R (Supplementary Figure S1). A total of 74 and 247 metabolites for GC and LC platforms were, respectively, selected as relevant, which were obtained by combining the results from a PLS-DA and random forest (RF) models. For both platforms, a better performance was observed for PLS-DA over RF to discriminate and select a major number of relevant compounds. The root mean squares error (RMSE) and R\({}^{2}\) were used to optimise the number of component of the PLS models. Using PLS1 regression, the sensory descriptors balance, black fruit and citrus fruit were satisfactory predicted by the volatile and non-volatile profiles with R\({}^{2}\) in cross validation higher than 0.72, while good predictions of the descriptors complexity and tree fruit were also obtained using the non-volatile data (Supplementary Figures S2 and S3). To study the correlation between the sensory data and the volatile profile, PLS2 regression was subsequently performed. The model fitted with the volatile and sensory data was optimised for two components, which explained a 66% of the total variance found in the X matrix. In Supplementary Figure S4, the correlation loadings plot from the PLS2 highlights the contribution of each variable to each component. Greater distances from the origin implies stronger correlations. Thus, variables falling within the inner circumference displayed light associations. In addition, if variables are represented as vectors sourcing from the origin, acute angles between vectors of two variables indicate positive correlations, while obtuse angles indicate negative correlations. The sensory attributes balance, citrus fruit, acidity, tree fruit and alcohol were highly correlated with 3-isopropyl-1-pentanol. Meanwhile, red fruit, complexity and black fruit were positively correlated with 3-methylpentanol, methyl 4-decenoate, 3-ethoxy-1-propanol, (E)-nerolidol, ethyl decanoate, ethyl 2-methylpropanoate, trans-3-hexen-1-ol and 1-dodecanol. Finally, positive correlations were observed between the sensory descriptor persistence and isopentyl hexanoate, ethyl heptanoate and pentyl cyclopropane. Only two sensory descriptors were poorly correlated with the volatile profile data (taste intensity and scent intensity). Only scent intensity fell within the inner circumference, meaning weak correlations of this descriptor with the volatile data. For the LC-MS data, a PLS regression was optimized for 2 components, which explained 80% of the total variance in the X matrix. The sensory attributes balance, citrus fruit, acidity and alcohol were slightly and positively correlated with several amino acids (alanine, glutamic acid, 2-Phenylglycine, L-Alanine, N-propyl/L-Alloisoleucine/L-Isoleucine/L-norleucine, L-leucyl-L-proline, N-acetyl-L-ornithine, N-acetylproline, phenylalanine, proline betaine and suberylglycine).