A SEM-EDS Study of Cultural Heritage Objects with Interpretation of Constituents and Their Distribution Using PARC Data Analysis

Abstract

Two cultural heritage objects studied with scanning electron microscopy-energy dispersive spectroscopy~EDS! are presented in this article:~1! archeological iron present in a soil sample and~2! a chip from a purple-colored area of an undisclosed 17th century painting. Novel PARC software was used to interpret the data in terms of quantitative distribution of mineral and organo-mineral phases as well as their chemical composition.The study serves to demonstrate the power of PARC rather than solving specific archeological issues. The observations on archeological iron potentially can assist in~1! studing the source of iron-metal and the style of forging,~2! learning about alteration processes of artifacts in the particular soil from which the sample originated, and~3! determining the nature of the fractures in the Fe-oxide envelope~desiccation of the sample after excavation, or as primary feature caused by volume change from oxidation!. In the paint chip, 11 consecutive layers can be distinguished using the PARC software. In general, each layer consists of a carrier supporting inorganic fragments. In the basal layer the fragments are dominant; in the superimposed layers the carrier usually is. Both organic and inorganic carriers appear to be present. Organic carriers can contain typically inorganic constituents~e.g., Pb, Al!, beyond the chemical spatial resolution of EDS~i.e., ,1 mm!.

Large-Area Phase Mapping Using PhAse Recognition and Characterization (PARC) Software

“…Generally this is a successful strategy with coarse-grained materials having phase domains of 10 μ m or larger, such as geological samples [1] and metallurgical slag. In full-automatic PARC mode, pixels within phases forming solid solutions with a broad chemical range can become assigned to separate phases.…”

“…Our PARC phase assignment models have been developed for various types of steel industry-related materials, such as iron-ore pellets [13], metallurgical slags [14][15][16], diverse process and environmental dusts, casting powder, and surface defects in steel products. In addition, PARC has been applied to geological investigations (experimental petrology [2,17] on hydrous andesite and gabbro), archeological and historical artifacts (Roman steel, seventeeth-century paintings [1]), and slags from several other processes, such as sewage-sludge incineration and fertilizer production.…”

“…Locked within the acquired SI data is spatially resolved chemical information that can be used to define phases, map their distribution and relative abundances, and determine the accurate chemistry of not only the individual phases but also the local bulk composition on the millimeter to centimeter scale. To unlock this potential at Tata Steel, we have developed the PhAse Recognition and Characterization (PARC) postprocessing software for SI data [1][2][3].…”

Phase Diagram Study of the CaO^|^ndash;CaF2 System

“…Details regarding the PARC technique can be found elsewhere. 15) Both SEM and optical microscopy (Imager Z1, Carl Zeiss, Germany) techniques were used for the phase distribution examination. X-Ray Diffraction (XRD, D8 Discover, Bruker, USA) was employed for the identification of crystal phases.…”

Evaluating X-Ray Microanalysis Phase Maps Using Principal Component Analysis

“…Phase composition averages are often difficult to interpret being affected by convoluted pixels at grain boundaries, where the limits of analytical resolution results in measured intensities consisting of a convolution of adjacent phases. van Hoek et al (2011) and Liebske (2015) showed how these 'bad' pixels can be eroded to give phase composition averages reflecting true compositions but this processing is not available in most phase mapping packages. Algorithms can be independently verified for reference samples against methods such as manual thresholding (Maloy & Treiman 2007) or EBSD phase maps (Statham et al 2013), but this does not ensure the algorithm works correctly for all samples and operating conditions (Munch et al 2015) In this study k-means clustering and k-nearest neighbour (KNN) algorithms are used to demonstrate some of the problems and show how, irrespective of the phase mapping algorithm, principal component analysis (PCA) can be used to assess the quality of phase classification.…”