In addition to the quality and comprehensiveness of the measurements, the underlying mathematical approach for approximating object and cluster dissimilarity greatly effects the computed measurement diversity. In this study, we considered traditional measures of dissimilarity for comparing retention indices and EI mass spectra, and extensions of traditional methods for comparing MS 2 collected with ESI-MS/MS and is-CID mass spectra collected with DART-MS. It is possible that a specialized approach for approximating the dissimilarity of fentanyl measurements will improve the computed measurement diversity and thus the ease with which compounds can be uniquely identified. A natural extension to this study is to characterize measurement diversity while leveraging replicate measurements per fentanyl analog. Having replicate measurements allows us to use a broader variety of mathematical tools , including machine learning . Additionally, it would be fruitful to evaluate measurement diversity for other classes of drugs and using measurements beyond retention indices and mass spectra.