Noise filtering is considered a crucial step for the proper interpretation of Raman spectra. In this work, we present a new denoising procedure which enhances the Raman information whilst reducing unwanted contributions from the most frequent noise sources, i.e. the shot noise and the fluorescence's baseline. The procedure increases the signal-to-noise ratio whilst preserving simultaneously the shapes, positions and intensity ratios of the Raman bands. The method relies on cubic penalized spline fitting and mathematical morphology and requires no user input. We describe the details of this method and include a benchmark to study the performance of the presented approach compared with the most commonly used denoising techniques. The method has been successfully applied to improve the signal quality of Raman spectra from artistic pigments. The reliable results that were obtained make the methodology a useful tool to help the analyst in the interpretation of Raman spectra from pigments in artworks. Copyright © 2017 John Wiley & Sons, Ltd.Peer ReviewedPostprint (author's final draft

A new method has been developed to automatically identify Raman spectra, whether they correspond to single- or multicomponent spectra. The method requires no user input or judgment. There are thus no parameters to be tweaked. Furthermore, it provides a reliability factor on the resulting identification, with the aim of becoming a useful support tool for the analyst in the decision-making process. The method relies on the multivariate techniques of principal component analysis (PCA) and independent component analysis (ICA), and on some metrics. It has been developed for the application of automated spectral analysis, where the analyzed spectrum is provided by a spectrometer that has no previous knowledge of the analyzed sample, meaning that the number of components in the sample is unknown. We describe the details of this method and demonstrate its efficiency by identifying both simulated spectra and real spectra. The method has been applied to artistic pigment identification. The reliable and consistent results that were obtained make the methodology a helpful tool suitable for the identification of pigments in artwork or in paint in general.