This paper discusses the use of a small ultraportable hyperspectral camera in the VIS-NIR (Visible-Near Infrared) range for archaeological fieldwork and its hardware, data processing workflows, and spectral information that can be used for in situ screening. Hyperspectral imaging is a widespread, non-destructive analytical technique used in various disciplines for highlighting invisible patterns and mapping the spectral signatures of selected targets. In archaeology, it has mostly been applied for remote sensing satellite imagery to uncover information about features that are hidden underground. Targeted applications of hyperspectral imaging have been developed in the last few years, opening new perspectives for material analysis based on spectral mapping. Recent advances in portable instrumentation have led to the development of small and rugged cameras that can be used directly in the field for investigating different materials.

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.

An ultraportable hyperspectral camera operating in the Vis–NIR range (400–1000 nm) was used in this study for the non-invasive analysis of a selection of Italian maiolica wares and sherds from the Museo Nazionale del Bargello in Florence, Italy. The studied objects included authentic archaeological sherds, nineteenth-century forgeries, and ceramics of uncertain origin. The primary aim of this study was to evaluate the efficacy of hyperspectral imaging (HSI) spectroscopy combined with multivariate analysis for examining highly refractive and three-dimensional glazed ceramic artefacts within the constraints of a museum environment, including imperfect lighting conditions. Two data processing pipelines were tested: one based on principal component analysis (PCA) with score plots and the other on spectral angle mapper (SAM) classification obtained through the ENVI Spectral Hourglass Wizard (ENVI-SHW). The analyses enabled the discrimination between original parts and/or restored parts and additions. Among others, a complex assemblage of original sherds, restored parts, and non-ceramic parts was observed in a Renaissance pastiche vase. Furthermore, the method enabled the identification of the various chromophores providing clues on the artistic colour palette. In particular, the identification of chromium in the blue and green glazes of two objects of uncertain chronology suggested that they were not made before the nineteenth century. The acquisition of a larger and more detailed database of HSI data on maiolica glazes is strongly encouraged to improve the applicability of this method in authenticity, art-historical and archaeological studies.

The study of stone artifacts and their provenance is an important proxy for understanding the entangled relationship between humans and geological resources. In this paper, we explore the potentialities of an interdisciplinary approach combining in situ documentation of tool marks and characterization of stone types using a near infrared (NIR) portable probe. We argue that this protocol is useful for collecting screening data on objects that cannot be moved or sampled. NIR spectra describe textural and molecular features of the materials and can be used to achieve a preliminary characterization of raw materials. We present a case study from the territory of Montescaglioso (Basilicata, near Matera, Italy), where we combined the analysis of a calcarenite (limestone) quarry, in Masseria D’Alessio, which was exploited since the 6th century BC, as well as artifacts of the same chronology from surveys and excavations in the surroundings. The aim was to collect preliminary data about the distribution of the particular calcarenite extracted from the quarry and identify exploitation and trade patterns. The data were processed using multivariate statistics to highlight the relevant spectral information and perform supervised classification of spectral features. Documentation of tool marks and the process of stone working were combined with the spectral signature of the artifacts to link the stone types to the description of their extraction/carving methods.