Rationale: Methods for multi-isotope analyses are gaining in importance in anthropological, archaeological, and ecological studies. However, when material is limited (i.e., archaeological remains), it is obligatory to decide a priori which isotopic system(s) could be omitted without losing information.
Methods: We introduce a method that enables feature ranking of isotopic systems on the basis of distance-based entropy. The feature ranking method is evaluated using Gaussian Mixture Model (GMM) clustering as well as a cluster validation index ("trace index").
Results: Combinations of features resulting in high entropy values are less important than those resulting in low entropy values structuring the dataset into more distinct clusters. Therefore, this method allows us to rank isotopic systems. The isotope ranking depends on the analyzed dataset, for example, consisting of terrestrial mammals or fish. The feature ranking results were verified by cluster analysis.
Conclusions: Entropy-based feature ranking can be used to a priori select the isotopic systems that should be analyzed. Consequently, we strongly suggest that this method should be applied if only limited material is available.
© 2019 The Authors. Rapid Communications in Mass Spectrometry published by John Wiley & Sons Ltd.