Here, we used Skyline to remove poor quality combinatorial matches (idotp > 0.9 for the first three isotopes) and visualise the glycan profile of three datasets which feature Nglycans and glycans released from glycosphingolipids (Fig ). In all three datasets, qualitatively identical plots of detected glycans were observed when these EICs were compared to base peak chromatograms (BPC) of the original raw files, demonstrating that GlyCombo is effective at glycan detection across these diverse datasets. As idotp is based on the isotopic distribution of a chemical formula, it informatively scores composition assignments but is unsuitable at discriminating glycan structures due to the isotopic distribution of isomers being identical. Although our software extracts and rapidly searches mzML files, instrument acquisition schemes can incorrectly identify the monoisotopic peak (known as off-by-one errors ), leading to incorrectly calculated neutral glycan masses. This is also exacerbated by our requirement of an MS2 scan for a given glycan composition to be detected. The use of glycan precursor isotopic distribution filtering ensures that these effects are mitigated, due to scoring the abundance of the first three isotopes of each glycan and subsequent removal of glycan compositions returning poor scores.