Fusarium Head Blight (FHB) of wheat is a fungal disease caused mainly by Fusarium graminearum. It has been a persistent worldwide problem for years causing substantial economic losses. Efforts to breed resistance in wheat cultivars represent a practical way to manage this disease. However, there is still much to contribute on how the infection develops and what events make a cultivar resistant to the infection at a molecular level. A metabolite profiling time course strategy was applied to a wheat near isogenic lines (NIL), with contrasting resistant alleles, and three hard red spring wheat cultivars with various degrees of resistance. The analytical time window where no significant difference occurs from sample extraction to sample analysis was also determined adding robustness to this study. Results indicate a maximum analytical window time of 7:45 hours for a wheat extraction queued in an UHPLC auto-sampler at 25 ¡ãC. Combining UHPLC-QTOF/MS technology with statistical analyses resulted in 61 significant metabolites (p < 0.05; fold change ¡Ý 2). The NIL and wheat cultivars had profiles with common and unique molecules. Tentative identification was performed by using accurate mass search, tandem MS fragmentation data with internal and online databases. Taking into account the restriction of database identifications, results confirm the presence of hydroxycinnamic acid amides (HCAA) which have been shown to induce thickening of cell walls. These compounds were seen in the resistant and susceptible genotypes with no difference in their intensities but can appear as early or late occurring between 0 and 48 hours after inoculation. Compounds classified as resistant related induced and resistant related constitutive were found in the NIL resistant pair at 48hrs. ¡°Susceptibility indicator¡± molecules were also observed in the susceptible NIL pair. This suggest that for the NIL pair, HCAA were a normal part of host reaction, while potentially important metabolites for the host resistance may develop later than 48 hours after inoculation. It was possible to establish an analytical and data mining methodology to perform metabolite profiling in wheat florets utilizing a UPLC-QTOF/MS.