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This work explores the application of chemometric techniques to the analysis of lipidic paint binders (i.e., drying oils) by means of Raman and near-infrared spectroscopy. These binders have been widely used by artists throughout history, both individually and in mixtures. We prepared various model samples of the pure binders (linseed, poppy seed, and walnut oils) obtained from different manufacturers. These model samples were left to dry and then characterized by Raman and reflectance near-infrared spectroscopy. Multivariate analysis was performed by applying principal component analysis (PCA) on the first derivative of the corresponding Raman spectra (1800-750 cm(-1)), near-infrared spectra (6000-3900 cm(-1)), and their combination to test whether spectral differences could enable samples to be distinguished on the basis of their composition. The vibrational bands we found most useful to discriminate between the different products we studied are the fundamental ν(C=C) stretching and methylenic stretching and bending combination bands. The results of the multivariate analysis demonstrated the potential of chemometric approaches for characterizing and identifying drying oils, and also for gaining a deeper insight into the aging process. Comparison with high-performance liquid chromatography data was conducted to check the PCA results.

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A comprehensive dataset of vibrational spectra of different natural organic binding media is presented and discussed. The binding media were applied on a glass substrate and analyzed after three months of natural ageing. The combination of Raman and FT-NIR spectroscopies allows for an improved identification of these materials as Raman technique is more informative about the skeletal vibrations, while FT-NIR spectroscopy is more sensitive to the substituents and polar groups. The experimental results are initially discussed in the framework of current spectral assignment. Then, multivariate analysis (PCA) is applied leading to differentiation among the samples. The two major principal components allow for a complete separation of the different classes of organic materials. Further differentiation within the same class is possible thanks to the secondary components. The loadings obtained from PCA are discussed on the basis of the spectral assignment leading to clear understanding of the physical basis of this differentiation process.