In this project, we intend to advance in the development of omic strategies that will allow carrying out these studies of maximum complexity. We expect to make this progress in two complementary lines. Firstly, we aim to improve the metabolomic analytical methodology to enable a more comprehensive characterization of analyzed samples. We will specifically focus on the development of multidimensional liquid chromatography with detection by mass spectrometry (LCxLC-MS) and mass spectrometry imaging (MSI) methodologues. Secondly, we plan to improve the methodology available for the chemometric evaluation of these massive datasets. Main efforts will focus on the development of 1) compression methods for reducing data dimensionality without loss of information; 2) multivariate analysis methods for the simultaneous evaluation of multiple experimental factors; and 3) data fusion approaches considering both intraomic (datasets at the same omic level) and interomic (datasets at different omic levels).