Search

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.)

Rationale: Due to the spatial heterogeneity of stable isotope ratios of single elements measured in attempts to georeference bioarchaeological finds, multi-isotope fingerprints are frequently employed under the assumption that similar isotopic signatures are indicative of similar shared environments by the individuals studied. The extraction of the spatial information from multi-isotope datasets, however, is challenging., Methods: Gaussian mixture clustering of six- to seven-dimensional isotopic fingerprints measured in archaeological animal and human bones was performed. Uncremated animal bones served for an isotopic mapping of a specific reference area of eminent archaeological importance, namely the Inn-Eisack-Adige passage across the European Alps. The fingerprints consist of 87 Sr/ 86 Sr, 208 Pb/ 204 Pb, 207 Pb/ 204 Pb, 206 Pb/ 204 Pb, 208 Pb/ 207 Pb, and 206 Pb/ 207 Pb ratios, and δ 18 O phosphate values in uncremated bone apatite, while the thermally unstable δ 18 O values of human cremations from this region were discarded., Results: The bone finds were successfully decontaminated. Animal and human isotope clusters not only reflect individual similarities in the multi-isotopic fingerprints, but also permit a spatial allocation of the finds. This holds also for cremated finds where the δ 18 O phosphate value is no longer informative. To our knowledge, for the first time Pb stable isotopes have been systematically studied in cremated skeletal remains and proved significant in a region that was sought after for its ore deposits in prehistory., Conclusions: Gaussian mixture clustering is a promising method for the interpretation of multi-isotopic fingerprints aiming at detecting and quantifying migration and trade., (© 2018 John Wiley & Sons, Ltd.)

Rationale: Modern methods in mass spectrometry permit fast accumulation of a huge amount of data. The analysis of multi-isotope data sets of archaeological remains is of increasing importance for the study of palaeobiodiversity. However, common bivariate isotopic data analysis fails to detect certain patterns in a multi-dimensional data set. This problem can be solved by cluster analysis., Methods: Gaussian Mixture Model (GMM) clustering was applied to a multi-isotope data set including 184 individual mass spectrometric measurements (δ(13) Ccollagen , δ(15) Ncollagen , δ(13) Ccarbonate , and δ(18) Ocarbonate values) of archaeological fish bones (n = 46) from the Viking Haithabu and medieval Schleswig sites in northern Germany. The number of components was first restricted to the expected number of three (freshwater, brackish, and marine environment). Subsequently, classification was conducted with respect to an optimal Bayesian Information Criterion (BIC)., Results: Restriction of the number of components to three clusters leads to the expected clustering results according to the gross ecological niches (freshwater, brackish, marine). The isotopic data of fish bone were, however, optimally clustered into four clearly separated, reasonable groups, namely a freshwater, a brackish, and two marine groups. The two marine clusters differ in their oxygen isotope ratios, indicating different water temperature and therefore probably imported fish. Restriction of the number of clusters resulted in better training and test results., Conclusions: The GMM clustering method is applicable to complex multi-dimensional stable isotope data sets established by isotope ratio mass spectrometry (IRMS). This exemplary application resulted in an identification of habitat preferences and non-local individuals. Depending on the scientific question to be solved, restriction of the cluster size could lead to a better reproducibility; however, with loss of dissolution. Copyright © 2016 John Wiley & Sons, Ltd., (Copyright © 2016 John Wiley & Sons, Ltd.)