2022
DOI: 10.1016/j.jasrep.2022.103382
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Gouache gilding on lead and wood objects studied by multivariate and graph analyses applied to XRF spectra

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Cited by 4 publications
(5 citation statements)
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“…The data obtained are examined using multivariate statistical analysis to study the relationships among elements and spectra detected by XRF analysis. The analysis and representation methods used are boxplot, Principal Component Analysis (PCA), Graph Analysis (GA), diagram and Hierarchical Cluster Analysis (HCA) [60][61][62][63][64].…”
Section: Instrumentation and Methodsmentioning
confidence: 99%
“…The data obtained are examined using multivariate statistical analysis to study the relationships among elements and spectra detected by XRF analysis. The analysis and representation methods used are boxplot, Principal Component Analysis (PCA), Graph Analysis (GA), diagram and Hierarchical Cluster Analysis (HCA) [60][61][62][63][64].…”
Section: Instrumentation and Methodsmentioning
confidence: 99%
“…Indeed, though XRF averages out the composition of the whole volume investigated, different attempts have been made to retrieve the elemental distribution inside a multilayered sample. In particular, the ratio between different fluorescence lines (Kα/Kβ or Lα/Lβ intensities ratio) is often applied on metallic artifacts to measure the thickness of gildings [50][51][52][53][54][55][56] or the Monte Carlo simulations are used to create theoretical spectra of multi-layered samples, which are compared with the experimental data obtained on the unknown samples [57][58][59]; and finally, confocal micro XRF (CµXRF), employs capillaries to select a small volume of the sample to be analyzed, performing this task for more than twenty years [60][61][62][63][64][65].…”
Section: Angle-dependent Xrf Techniquesmentioning
confidence: 99%
“…PCA uses a set of observations (in our case measurement points) that are characterized by many variables (in our case net area counts of chemical elements or unprocessed full spectrum in the energy range from 0 to 47 keV with 4096 acquisition channels) and processes a transformation of the original variables into a new set of uncorrelated variables, called principal components (PCs). The aim is to visualize possible clusters among the observations in the score plot so that their characteristics of similarity or difference emerge [24]. The loading plot is a useful graph to understand the contribution of each original variable in defining the PCs, while the biplot is the view of score plot and loading plot together.…”
Section: X-ray Fluorescence (Xrf) and Scanning Macro-x-ray Fluorescen...mentioning
confidence: 99%
“…Quantitative analysis is also difficult because the measured intensities of the fluorescence lines related to an element depend on several factors such as the accompanying elements (matrix), the shape and thickness of the analyzed sample, and measurement conditions [23] but semi-quantitative results can give important information. XRF data can be subjected to various statistical processing methods such as principal component analysis (PCA) and the k-means method, which allow the identification of groupings using elemental concentrations, intensity of the fluorescence lines and full spectra [24,25].…”
Section: Introductionmentioning
confidence: 99%