During the early autumn of 2014 a field-based near infrared (NIR) spectroscopy study was carried out at Swedish and Norwegian Stone Age rock painting sites. This article presents results from one of them, namely Flatruet, Härjedalen, Sweden. Here, field-based NIR measurements were conducted using the 908-1676 nm wavelength range to gather 479 spectra: 427 of rock paintings and 52 of local lithology background. The whole dataset was analysed using principal component analysis (PCA) and four principal components were extracted explaining 98.5% (PC1), 1.4% (PC2), 0.06% (PC3) and 0.04% (PC4). The PCA results showed that there was a large spread in the spectra of both background and red paint objects, but also some evidence of clustering could be seen where background and paintings could be separated. An improvement in separation was achieved with partial least squares discriminant analysis (PLS-DA) using the background and paint as categorical variables. The most important components of the PLS-DA model showed a better separation in the score plot. A small test set of 10 paint and 10 background samples showed that one of the paint samples and two of the background samples were misclassified. One conclusion is that there is a large spread in background due to varying precipitation of secondary iron oxides. It was also decided to look deeper into local models of painted elks and their pigments alone. This was done using local PCA models and soft independent modelling of class analogies showing that some painted elks could be separated from each other while others were quite similar, which is important for answering questions about origin, age and weathering.
This study proposes a method for the classification of lithic raw materials by means of hyperspectral imaging, a non-destructive fast analytical technique. The information potential of this approach was tested on a dwelling site dated to mid-late Mesolithic (7200-5800 BP) at Lillsjön, Ångermanland, Sweden. A dataset of lithic tools and flakes (2612 objects) made of quartz and quartzite, was analyzed using a shortwave infrared hyperspectral imaging system. The classification of the raw materials was performed applying multivariate statistical models. A random test set of 55 artefacts was selected, classified according to spectral signature and divided into categories corresponding to different geological materials. The same test set was analyzed with Energy Dispersive X-Ray Fluorescence (ED XRF) to validate the classification. The entire dataset of lithics collected on the site was then classified applying a SIMCA model. The distribution of items on the site was visualized in a 3D GIS platform according to their geological characteristics to highlight patterns that could indicate different use of the space and dynamics of raw materials supply over time.
We present a scheme for non-destructive provenancing of granite shafts dating from the Roman to the medieval period using a combination of visual, magnetic, and chemical determinations. Our results on two monumental medieval complexes in Europe, in Pisa and Lyon, shows both oriental provenance, most likely spolia, and shaft from the quarries within the influence zone of the Pisa Republic (Elba, Corsica and Sardinia) that possibly correspond to shafts newly obtained in the quarries, particularly for the large diameter shafts of Ainay in Lyon (from Corsica) and Piazza dei Miracoli (from Elba and Sardinia). pXRF appears to be an efficient way to discriminate Corsican from Egyptian grey coarse grained granite provenance.
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