An analytical Raman spectroscopic study of an important english oil painting of the 18th Century

Investigation of Raman Spectroscopy (with Fiber Optic Probe) and Chemometric Data Analysis for the Determination of Mineral Content in Aqueous Infant Formula

This study investigated the use of Raman spectroscopy (RS) and chemometrics for the determination of eight mineral elements (i.e., Ca, Mg, K, Na, Cu, Mn, Fe, and Zn) in aqueous infant formula (INF). The samples were prepared using infant formula powder reconstituted to concentrations of 3%-13% w/w (powder: water) (n = 83). Raman spectral data acquisition was carried out using a non-contact fiber optic probe on the surface of aqueous samples in 50-3398 cm-1. ICP-AES was used as a reference method for the determination of the mineral contents in aqueous INF samples. Results showed that the best performing partial least squares regression (PLSR) models developed for the prediction of minerals using all samples for calibration achieved R2CV values of 0.51-0.95 with RMSECVs of 0.13-2.96 ppm. The PLSR models developed and validated using separate calibration (n = 42) and validation (n = 41) samples achieved R2CVs of 0.93, 0.94, 0.91, 0.90, 0.97, and 0.94, R2Ps of 0.75, 0.77, 0.31, 0.60, 0.84, and 0.80 with RMSEPs of 3.17, 0.29, 3.45, 1.51, 0.30, and 0.25 ppm for the prediction of Ca, Mg, K, Na, Fe, and Zn respectively. This study demonstrated that RS equipped with a non-contact fiber optic probe and combined with chemometrics has the potential for timely quantification of the mineral content of aqueous INF during manufacturing.

The efficiency of micro-Raman spectroscopy in the analysis of complicated mixtures in modern paints: Munch's and Kupka's paintings under study

Twenty one mock-up samples containing inorganic pigments primarily used at the turn of the 19th and 20th century were selected for comparative study and measured by micro-Raman and portable Raman spectrometers. They included pure grounds (chalk-based, earth-based and lithopone-based), grounds covered by resin-based varnish, and different paint layers containing mixtures of white, yellow, orange, red, green, blue and black pigments, usually in combination with white pigments (titanium, zinc and barium whites or chalk). In addition, ten micro-samples obtained from seven paintings of two world-famous modern painters Edvard Munch and František Kupka have been investigated. Infrared reflection spectroscopy (FTIR), portable X-ray fluorescence (XRF) and scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS) were used as supplementary methods. The measurements showed that blue pigments (ultramarine, Prussian blue and azurite), vermilion and ivory black in mixture with whites provided characteristic Raman spectra, while Co-, Cd- and Cr- pigments' bands were suppressed by fluorescence. The best success rate of micro-Raman spectroscopy has been achieved using the 780 nm excitation, however, the sensitivity of this excitation laser in a portable Raman instrument significantly decreased. The analyses of micro-samples of paintings by E. Munch and F. Kupka showed that micro-Raman spectroscopy identified pigments which would remain unidentified if analyzed only by SEM-EDS (zinc yellow, Prussian blue). On the other hand, chromium oxide green and ultramarine were not detected together in a sample due to overlap of their main bands. In those cases, it is always necessary to complement Raman analysis with other analytical methods.

Independent component analysis-based algorithm for automatic identification of Raman spectra applied to artistic pigments and pigment mixtures

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.