During the pre-treatment step, the pre-processed data are filtered, cleaned, drift corrected, missing value imputed, normalized, transformed and/or scaled [15] with the aim to make it adequate for the subsequent statistical analysis. Finally, statistical analyses are performed on the data to discover the relevant biological information. Different computational and statistical methods have been developed for this task, comprising unsupervised and supervised analysis [11]. In metabolomics studies, unsupervised analyses are fundamentally used to explore and unravel the structure of the data, aiming at detecting sample clusters or systematic trends and errors. One of the most popular and powerful unsupervised statistics used in metabolomics studies is principal component analysis (PCA), based on dimensionality reduction [16]. Meanwhile, supervised learning methods are used for classification, prediction and biomarker discovering, dealing with datasets that have response variables either continuous (regression problems) or discrete (classification problems) [16]. Partial least squares (PLS) and its discriminant extension PLS-DA are one of the most popular methods used for regression and classification problems, respectively, in metabolomics studies due to its high performance and easy interpretation [17]. These models are useful when the data are acquired from the same omics platform (i.e., LC-MS or GC-MS or nuclear magnetic resonance (NMR), etc. ), since simple concatenation of different omics data ignores their heterogeneity and a single type of omics is mainly highlighted [18]. However, metabolomics studies usually involve measurements from different omics platforms that may contain latent biological relationships and the independent assessment of each biological domain may not reveal that information. To overcome this, several frameworks implementing multi-omics data integration have emerged [19], including the unsupervised regularised generalised canonical analysis (RGCCA) and its sparse discriminant version sGCC-DA (DIABLO), which are useful multiblock methods to study the relationships between blocks of data measured in different platforms and to identify the main subsets of variables involved in those relationships [18]. The aim of this study was to establish a data processing and statistical analysis pipeline to (1) evaluate the impact of different elaboration processes on the metabolome of wines, acquired by means of two high throughput analytical platforms in an untargeted approach: GC-MS (volatile metabolites) and LC-MS (non-volatile metabolites), and to (2) relate those changes with their sensory descriptors. An optimal pre-treatment and pre-processing of the data generated from both analytical platforms was proposed to ensure its quality. Variable selection procedures were used to reveal the metabolites more impacted by the different wine elaboration processes. Each single omics data set and the sensory data were independently assessed by means of univariate and multivariate statistical analyses to study the impact of the wine elaboration process. The relationships between each single analytical platform and the sensory descriptors were evaluated by means of partial least squares regression (PLSR) models. Finally, the data obtained from the three sources were treated as a whole by means of data integration models to reveal hidden correlations.