Investigating the production provenance of iron artifacts with multivariate methods

The quest for suitable data, data treatments and statistical methods for identifying the provenance of iron artifacts has led to a variety of analytical strategies. Researchers working on the problem have been slow to develop or adopt the use of multivariate statistical techniques, despite their successful implementation in other archaeomaterials sourcing frameworks. This paper explores the analytical potential of a comprehensive multivariate statistical strategy for identifying the primary production origins of bloomery iron artifacts using bulk chemical analyses of bloomery smelting slag and slag inclusions in iron artifacts. This strategy includes a multivariate model for identifying distinct slag inclusion types introduced during smelting and refining. Principal component analysis and linear discriminant analysis are then applied to smelting slag training sets to create multivariate provenance fields, the chemical distributions of which are defined by kernel density estimation. Single and multi-group evaluation methods are examined. Appropriate data transformations are discussed to facilitate the projection of the chemistry of “unknown” slag inclusions into the multidimensional space generated by the smelting slag groups of known provenance. The efficacy of this strategy is demonstrated through its application to a previously examined data set derived from three iron production experiments and a published archaeological example. Results indicate that an appropriately designed multivariate strategy can be an effective tool for evaluating provenance hypotheses for bloomery iron artifacts.