## 4 Discussion  
The pre-processing and data pre-treatment workflow proposed in this study for the volatile and non-volatile profile made it possible to extract the signal of a large number of metabolites with low deviation coefficients. The use of automatic pre-processing softwares (IPO-XCMS-CAMERA for LC-MS data and PARAFADISe implementing PARAFAC2 for GC-MS data) with low number of parameters required to be set, diminished the risk of modelling problems. The pre-treatment step included cleaning and filtering of the data (samples and metabolites), drift corrections based on QCs, removing molecular features with high RSD, missing value imputation, PQN normalization and autoscaling, and the quality of the data was checked by means of different PCAs (Figures 2, 4 and 5). The proposed workflow allowed us to adequate the data for the subsequent statistical analyses. The use of quality control samples (QCs) analysed along the analytical sequences, combined  
Figure 9: Circle correlation plot from the regularised generalised canonical correlation analysis (RGCCA) performed on the volatile, non-volatile and sensory data. A: alcohol, E: ester, M: miscellaneous, AA: amino acid, OA: organic acid. with PCA was demonstrated to be a strong tool to verify the quality of the final data. In our case, this methodology was especially useful for the non-volatile data since higher variability in those data was observed (Figures 4 and 5). In particular, LC-MS data acquired in negative ionization mode suffered an important drift that was revealed by two well separated clusters of QCs in the PCA (Figure 4). On this basis, we recommend the systematic use of QCs for metabolomics studies to both verify the quality of the data and to correct for drifts or batch effects. The independent (PLS-DA and PLSR) and integrative (sGCCA-DA and RGCCA) statistical analysis led to complementary results of modulation of the wine metabolome and the relationships with their sensory descriptors, obtaining robust potential markers of the fermentation strategy which had important contribution on the sensory characteristics of the wines. To study the link between wine metabolome and sensory descriptors, a previous selection of the all-relevant metabolites involved in the fermentation strategy was performed by means of PLS-DA and RF. This methodology reduced the complexity of the final PLSR and RGCCA models by focusing on the compounds that were modulated by yeasts, making it easier the interpretation. Interestingly, the markers selected from the sGCCA-DA approach were highly correlated with the sensory descriptors. Several metabolites such as 3-isopropyl-1-pentanol, isopentyl hexanoate, ethyl hepatomaate, pentyl-cyclopropane, ethyl trans-3-hexenoate, (E,E)-farnesol, 1-dodecanol and imidazole acetic acid were selected as markers in both the independent and integrative assessments (Figures 3, 6 and 8). However, others such as 1-undecanol (A3) and shikimic acid (OA6), which were very positively correlated with persistence of the wines and to a lower extent with taste intensity, and linalyl anthralinate (T3), ethyl 5-hexenoate (E3), hexyl acetate (E7) and proline betaine (A11), highly correlated with citrus fruit, balance, acidity, tree fruit and alcohol descriptors (Figure 9), were not selected during the independent assessment. Therefore, the integration of both data sets revealed hidden biological information, supporting this approach to complement the classical independent assessment. Several sensory descriptors displayed high correlation between predicted and observed sensory attribute scores in the PLSR1 assessment (Supplementary Figures S2 and S3), highlighting the quality of the metabolic data to make sensory predictions. The RGCCA also led to additional information hidden during the independent assessments with PLSR.