Raman spectroscopic discrimination of pigments and tempera paint model samples by principal component analysis on first‐derivative spectra

Abstract

This work explores the application of principal component analysis (PCA) on first-derivative Raman spectra to investigate historical tempera paint model samples. Various paint model samples were prepared containing pure blue pigments (azurite, lapis lazuli and smalt), pure red pigments (cinnabar, minium and raw Sienna), pure white pigments (lead white, chalk and gypsum), pure egg yolk as binder and tempera model samples obtained by mixing each of the pigments with the binder, and further characterized by Raman spectroscopy. The corresponding Raman spectra were used to apply PCA in order to test whether spectral differences allowed discrimination of samples based on their composition. Multivariate analyses were performed separately on three data matrices, one for each color, namely, white, blue and red, corresponding to the model samples, and all containing the spectral data of the binder model sample. Different pretreatments, that is log and derivative spectra, were performed on the spectra since no pattern distributions were obtained when the original Raman spectra were analyzed. Nevertheless, the multivariate analysis of the original Raman spectra was able to track alterations of sensitive pigments due to laser interaction. Results showed the excellent ability of PCA, when applied to the derived Raman spectra, to discriminate model samples according to their differing compositions in the three groups of model samples tested. This is the first attempt to use this approach in the field of cultural heritage and demonstrates the potential benefits for identifying historical pigments and binders for purposes of conservation and restoration.

A Nondestructive Methodology for the Study of Colored Enamels: Insights into Manufacturing and Weathering Processes

“…In this sense, previous research has demonstrated the benefit of applying spectral data coupled with a multivariant analysis (e.g., Principal Component Analysis, PCA) to detect structural interactions and alteration processes in painting materials . However, very little research has been done on the combined use of these techniques in ancient glass and enamels.…”

Raman spectroscopy in art and archaeology

“…The work presented by Navas et al [17] explores the application of principal component analysis (PCA) on firstderivative Raman spectra to investigate historical tempera paint model samples. Nineteen model samples were prepared according to Old Master recipes to obtain egg yolk tempera painting standards (several pigments covering blue, red and white colours) similar to those used by medieval artists.…”

Vibrational Spectroscopies and Chemometry for Nondestructive Identification and Differentiation of Painting Binders

“…Recent authors have proposed the use of spectroscopic techniques, as vibrational and electronic spectroscopic methods, coupled with multivariate analysis for the discrimination of different painting binders (animal glue, whole egg, and linseed oil) [2,[4][5][6] and also of organic compounds of the same class, like lipidic binders [7][8][9][10], subjected to natural ageing.…”

“…These are obvious problems for the analysis of samples of historical interest [4,11,12]. Spectroscopic techniques have the potential to overcome these limitations, as they allow nondestructive and contactless measurements, giving molecular information on complex macromolecules, and could be performed in situ by means of portable devices.…”

“…Nevertheless, there are several different causes that can lead to random noise in Raman and FT-NIR spectra, thus making the identification of spectral bands more difficult, particularly in computational analyses, as in the present work. To identify small differences in the original spectra, first derivatives of the spectral data were used for PCs extraction [4,6,8,9,13]. As a result, an increase in sensitivity/specificity for differentiation capability and classification can be expected, due to the new dimensions added to the dataset [8,9].…”