X-ray fluorescence (XRF) spectrometry has proven to be a core, non-destructive, analytical technique in cultural heritage studies mainly because of its non-invasive character and ability to rapidly reveal the elemental composition of the analyzed artifacts. Being able to penetrate deeper into matter than the visible light, X-rays allow further analysis that may eventually lead to the extraction of information that pertains to the substrate(s) of an artifact. The recently developed scanning macroscopic X-ray fluorescence method (MA-XRF) allows for the extraction of elemental distribution images. The present work aimed at comparing two different analysis methods for interpreting the large number of XRF spectra collected in the framework of MA-XRF analysis. The measured spectra were analyzed in two ways: a merely spectroscopic approach and an exploratory data analysis approach. The potentialities of the applied methods are showcased on a notable 18th-century Greek religious panel painting. The spectroscopic approach separately analyses each one of the measured spectra and leads to the construction of single-element spatial distribution images (element maps). The statistical data analysis approach leads to the grouping of all spectra into distinct clusters with common features, while afterward dimensionality reduction algorithms help reduce thousands of channels of XRF spectra in an easily perceived dataset of two-dimensional images. The two analytical approaches allow extracting detailed information about the pigments used and paint layer stratigraphy (i.e., painting technique) as well as restoration interventions/state of preservation.

Macroscopic X-ray fluorescence analysis (MA-XRF) is a non-destructive analytical technique that allows for the rapid and thorough investigation of paintings; therefore, it is nowadays increasingly involved in relevant studies. In the present work, a state-of-the-art MA-XRF set-up is utilized to identify the painting materials and techniques and document the state of preservation of an early 19th-century AD Greek religious panel painting (“icon”). The artifact in consideration has received extensive restoration interventions in the past and is considerably decayed; for these reasons, the interpretation of the relevant MA-XRF elemental intensity distribution maps is challenging. In this framework, it is demonstrated how the elemental distribution maps can be explored and interpreted to lead to a thorough investigation of the painting in consideration. In particular, the MA-XRF data interpretation allowed for the identification of the original palette that includes the rather rarely employed–in icon painting-yellow lead-antimonate pigment, led to the spotting of an invisible inscription, to the documentation of the preparation/gesso layer and the preliminary drawing. Additionally, it was possible to discriminate between the original painting and the later interventions, while the collected data revealed that the painting originally had two cross-bars attached to its back. Ultimately, the presented case study can be utilized as a guide for the proper interpretation of MA-XRF data from decayed and altered icons.

An X-ray fluorescence handheld spectrometer (hh-XRF) is adapted for real-time qualitative and quantitative elemental analysis in scanning mode for applications in cultural heritage. Specifically, the Tracer-5i (Bruker) is coupled with a low-cost constructed computer-controlled x–y target stage that enables the remote control of the target’s movement under the ionizing X-ray beam. Open-source software synchronizes the spectrometer’s measuring functions and handles data acquisition and data analysis. The spectrometer’s analytical capabilities, such as sensitivity, energy resolution, beam spot size, and characteristic transition intensity as a function of the distance between the spectrometer and the target, are evaluated. The XRF scanner’s potential in real-time imaging, object classification, and quantitative analysis in cultural heritage-related applications is explored and the imaging capabilities are tested by scanning a 19th-century religious icon. The elemental maps provide information on used pigments and reveal an underlying icon. The scanner’s capability to classify metallic objects was verified by analyzing the measured raw spectra of a coin collection using Principal Components Analysis. Finally, the handheld’s capability to perform quantitative analysis in scanning mode is demonstrated in the case of precious metals, applying a pre-installed quantification routine.

Scanning micrο X-ray fluorescence (μ-XRF) and multispectral imaging (MSI) were applied to study philately stamps, selected for their small size and intricate structures. The μ-XRF measurements were accomplished using the M6 Jetstream Bruker scanner under optimized conditions for spatial resolution, while the MSI measurements were performed employing the XpeCAM-X02 camera. The datasets were acquired asynchronously. Elemental distribution maps can be extracted from the μ-XRF dataset, while chemical distribution maps can be obtained from the analysis of the multispectral dataset. The objective of the present work is the fusion of the datasets from the two spectral imaging modalities. An algorithmic co-registration of the two datasets is applied as a first step, aiming to align the multispectral and μ-XRF images and to adapt to the pixel sizes, as small as a few tens of micrometers. The dataset fusion is accomplished by applying k-means clustering of the multispectral dataset, attributing a representative spectrum to each pixel, and defining the multispectral clusters. Subsequently, the μ-XRF dataset within a specific multispectral cluster is analyzed by evaluating the mean XRF spectrum and performing k-means sub-clustering of the μ-XRF dataset, allowing the differentiation of areas with variable elemental composition within the multispectral cluster. The data fusion approach proves its validity and strength in the context of philately stamps. We demonstrate that the fusion of two spectral imaging modalities enhances their analytical capabilities significantly. The spectral analysis of pixels within clusters can provide more information than analyzing the same pixels as part of the entire dataset.

We report on the ability to identify wood specimens by utilizing 30 fs Laser Induced Breakdown Spectroscopy (LIBS) in conjunction with machine learning techniques. Ten different wood specimens have been studied. The spectral features were assigned to atomic/ionic and diatomic molecular transitions. The origin of the latter has been explored by investigating the dynamics of the created plume in ambient and argon atmosphere. Principal Component Analysis (PCA) was employed for dimensionality reduction based on the primary LIBS analysis. The principal components formation is grounded on the CN, Ca II, Ca I, and Na, LIBS data. Furthermore, applying the weighted k nearest neighbor (kNN) algorithm led to an accurate identification of the investigated specimens, since the evaluation metrics of specificity value were found to be in the range of 0.96–1.00, while that of accuracy was within 0.93–1.00.