This work introduces the complementary use of μ‐Raman and μ‐Fourier transform infrared (IR) spectroscopy for the detection of specific carbon chains and cations for the identification of metal carboxylates within oil paint microsamples. Metal carboxylates (metal soaps) form naturally when free fatty acids react with metal cations and may also be found as additives or degradation products. Twenty‐two metal carboxylates were synthesised, and their spectra assembled in a reference database. Metal salts of cations commonly present in oil paintings were used, including lead, zinc, calcium, cadmium, copper and manganese. The fatty acids selected were the saturated acids palmitic (C16 : 0) and stearic (C18 : 0) and the polyunsaturated oleic acid (C18 : 1). Azelaic acid (C9 diacid), a product resulting from autoxidation of polyunsaturated acids, was also included. Metal carboxylates were characterised by Raman and IR spectroscopy, and their structures were confirmed by X‐ray diffraction. Raman and IR spectroscopy proved to be complementary techniques for a full identification of the metal carboxylates in complex aged paint. Raman enables the differentiation of the carbon chain length in the C–C stretching region (1120–1040 cm−1), and IR distinguishes the metal cation in the COO− stretching absorption region (1650–1380 cm−1). Principal component analysis was applied to the spectra in order to facilitate a fast and accurate method to discriminate between the different metal carboxylates and to aide in their identification. Finally, spectra from case studies were successfully projected in the principal component analysis models built, enabling a higher confidence level for the identification of copper palmitate and copper azelate in two 19th‐century Portuguese oil paintings. Copyright © 2014 John Wiley & Sons, Ltd.

Painters reproduce some spatial statistical regularities of natural scenes. To what extent they replicate their color statistics is an open question. We investigated this question by analyzing the colors of 50 natural scenes of rural and urban environments and 44 paintings with abstract and figurative compositions. The analysis was carried out using hyperspectral imaging data from both sets and focused on the gamut and distribution of colors in the CIELAB space. The results showed that paintings, like natural scenes, have gamuts with elongated shapes in the yellow-blue direction but more tilted to the red direction. It was also found that the fraction of discernible colors, expressed as a function of the number of occurrences in the scene or painting, is well described by power laws. These have similar distribution of slopes in a log-log scale for paintings and natural scenes. These features are observed in both abstract and figurative compositions. These results suggest that the underlying chromatic structure of artistic compositions generally follows the main statistical features of the natural environment.

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