QualAnalysis, a new tool in environmental chemistry for a faster qualitative analysis

In environmental chemistry qualitative analysis can provide new perspectives to assess spatiotemporal trends in pollutant distribution and it allows to identify unknown feature in samples. In qualitative analysis there are two main approaches: suspect screening (SS) which is focused on, the detection of known compounds and non-target screening (NTS) which aims to identify unknown features. Both approaches are often time consuming as it is needed to assess multiple parameters for each feature detected, such as exact m/z, isotopic pattern, fragmentation spectra or predict chemical formulas to apply Van Krevelen and Kendrick mass defect diagrams. On these perspectives, QualAnalysis represents a useful tool in LC-HRMS because it allows to conduct a suspect screening and formula prediction with a user-friendly interface: for suspect screening it generates the isotopic clusters for a given list of suspect compounds, look for any matching within the sample run file and it applies a statistical approach to determine the presence of a suspect compound based on the evaluation of experimental parameters (e.g. m/z, peak shape, isotopic cluster abundance, etc.). If the p value for a given match is ​> ​0.05 the assignment is flagged as “putative identification” for further confirmation (e.g. fragmentation spectra) otherwise it is neglected. Instead for formula prediction, QualAnalysis can generate all molecular formulas for a given list of m/z values applying the “Seven Golden Rules”, moreover QualAnalysis considers different compound classes, multiple adducts, it assigns the best match based on mass accuracy and calculates all parameters needed for Van Krevelen and Kendrick mass defect. This tool is suitable for all users, independently of the programming background because of the intuitive user interface, and it reduces significantly the time needed for data analysis, and it allows the user to focus on the comparison of fragmentation spectra with main MS/MS databases (e.g. MassBank, Mass Bank of North America and GNPS).