Principal Component Analysis of Spectral Data: A Contribution to the Knowledge of the Materials Constituting Works of Art

The use of totally non-destructive techniques such as image spectroscopy for diagnosing paintings makes it possible to obtain a large amount of spectral data that provides information concerning the composition of works of art. Here, we stress how statistical treatments, such as principal component analysis (PCA), applied to 2-D data, can contribute to a better knowledge of the work of art itself and of the distribution of the materials that constitute it.