Micro-oxygenation is a common winemaking practice for red wine, that consist in a continuous addition of small amounts of oxygen into the wine in order to improve its color, aroma, texture and conservation time. Object of our project was to study wine micro-oxygenation through LC-MS untargeted metabolomic profiling. Eight different theses, differentiated by the dose of oxygen (four levels) and iron (two levels) were applied in a Sangiovese wine, before and after the malolactic fermentation. All other conditions were strictly standardized and monitored, while every thesis was carried out in triplicate. The metabolomics untargeted analysis was performed by a Synapt UPLC-MS QTof system, both in negative and positive electrospray ionization, and every sample was injected twice. Raw data were converted to CDF format and processed by XCMS, for feature extraction, grouping and alignment. The candidate list for separating the various levels of metal and oxygen was identified by using a Support Vector Machine algorithm. Analysis and advanced data analysis has been performed by Matlab. Based in the standard data set of our laboratory and information from the literature regarding the metabolite profile of wines it was possible to annotate more than 250 compounds. The use of supervised and unsupervised multivariate methods point out both known candidate biomarkers and metabolites never considered before as possible biomarkers of wine micro-oxygenation. Between the known candidate biomarkers were identified various pigments and tannins. Additional and novel information obtained by correlation of oxygen and metal doses with various primary and secondary metabolites. These outcomes could be useful for developing analytical tools able to help researchers and winemakers for a more appropriate use of micro-oxygenation.