The classification model provides a good alternative approach for the detection of isotopologues, requiring no information on the molecular formula or arbitrary thresholds. However, it should be noted that the classification model is unable to distinguish between isotopologues coming from different chemicals or signals with the same monoisotpic mass. This would require prior separation such as chromatography. Besides the reduction of total number of features for identification, correct isotopologue identification can also assist in accurate molecular formula assignment. When multiple formula's are possible for a monoisotopic mass, the isotopic patterns can be predicted and compared with the detected isotopologues masses to eliminate less likely candidates. Lastly, the model was built based on isotopic distributions with a tree depth of six, meaning that it might not be able to correctly classify ions with more than 6 isotopologues if these ions would be detected at all due to their low occurrence probabilities. However, if required, the EMDforIso package enables the user to retrain the classification model using different training sets and parameters.