- The input database is expected to contain only the compounds without adduct information (example) and not also the adduct related information (example).
- The code computing the matching was rewritten in C++ for performance reasons and also performs the computation of expected mz value for a compound-adduct-pair (see here)
- The RT-based clustering as the second step of feature grouping has been reworked as described here
- The clustering by this new method results in a finer grouping of peaks into more individual clusters, see the example image below.

- The peaks corresponding to the isotopes of a given compound are detected based on several criteria:
- mass-to-charge ratio
- mass defect,
- peak intensity,
- retention time.
- For each annotated feature a peak table is filtered to find peaks that:
- lie in the same RT cluster as the feature,
- have RT within
rt_tolerancefrom the feature's RT, - have mass defect within
mass_defect_tolerancefrom that of the feature,
- After such peaks are identified, their intensities are compared to theoretical expected intensities of corresponding isotopes.
Peaks that differ in intensities more than
intensity_deviation_tolerance(relative scale) from expected values are filtered out. - Lastly, the detected isotopic peaks are appended to the annotation table