Non-invasive identification of organic materials in wall paintings by fiber optic reflectance infrared spectroscopy: a statistical multivariate approach.

The aim of this study is to develop a method for the non-invasive and in situ identification of organic binders in wall paintings by fiber optic mid-FTIR reflectance spectroscopy. The non-invasive point analysis methodology was set-up working on a wide set of wall painting replicas of known composition and using statistical multivariate methods, in particular principal component analysis (PCA), for the interpretation, understanding, and management of data acquired with reflectance mid-FTIR spectroscopy. Results show that PCA can be helpful in managing and preliminary sorting of the large amount of spectra typically collected during non-invasive measurement campaigns and highlight further avenues for research. The developed PCA model was finally applied to the case of a Renaissance wall painting by Perugino assessing it predictability as compared to the interpretation of the single spectrum.