Improved chemometric approach for XRF data treatment: application to the reverse glass paintings from the Lipari collection

Abstract

XRF data of a glass collection from Lipari Museum were processed by multivariate analysis by means of Principal Component Analysis (PCA) and Hierarchical Cluster Analysis (HCA).

Combining Multispectral Imaging and XRF Analysis to Examine San Patroba predica ai fedeli di Pozzuoli by Massimo Stanzione

“…As a result of the measurement, the software's qualitative analysis packages were used to identify the chemical elements in the spectrum and, in particular, PyMca software was used for the best comparison of spectra and for calculating net area counts (deconvolution of the characteristic fluorescence peaks). The interpretation of the data was supported using MATLAB_R2019a software with specific statistical methods such as principal component analysis (PCA) [37][38][39], hierarchical clustering analysis (HCA) [40,41], and k-means clustering [42][43][44].…”