Fernandes, R. and Eley, Y. and Brabec, M. and Lucquin, A. and Millard, A. and Craig, O. (2018) 'Reconstruction of prehistoric pottery use from fatty acid carbon isotope signatures using Bayesian inference.', Organic geochemistry., 117 . pp. 31-42.
Carbon isotope measurements of individual fatty acids (C16:0 and C18:0) recovered from archaeological pottery vessels are widely used in archaeology to investigate past culinary and economic practices. Typically, such isotope measurements are matched with reference to food sources for straightforward source identification, or simple linear models are used to investigate mixing of contents. However, in cases where multiple food sources were processed in the same vessel, these approaches result in equivocal solutions. To address this issue, we tested the use of a Bayesian mixing model to determine the proportional contribution of different food sources to a series of different mixed food compositions, using data generated both by simulation and by experiment. The model was then applied to previously published fatty acid isotope datasets from pottery from two prehistoric sites: Durrington Walls, near Stonehenge in southern Britain and Neustadt in northern Germany. We show that the Bayesian approach to the reconstruction of pottery use offers a reliable probabilistic interpretation of source contributions although the analysis also highlights the relatively low precision achievable in quantifying pottery contents from datasets of this nature. We suggest that, with some refinement, the approach outlined should become standard practice in organic residue analysis, and also has potential application to a wide range of geological and geochemical investigations.
|Full text:||(AM) Accepted Manuscript|
Available under License - Creative Commons Attribution Non-commercial No Derivatives.
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|Publisher Web site:||https://doi.org/10.1016/j.orggeochem.2017.11.014|
|Publisher statement:||© 2017 This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/|
|Date accepted:||30 November 2017|
|Date deposited:||01 December 2017|
|Date of first online publication:||07 December 2017|
|Date first made open access:||07 December 2018|
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