Here we present, for the first time, an extension of spatially offset Raman spectroscopy to thin (tens of micrometers thick), highly turbid stratified media such as those encountered in paintings. The method permits the non-destructive interrogation of painted layers in situations where conventional Raman microscopy is not applicable due to high turbidity of the top layer(s). The concept is demonstrated by recovering the pure Raman spectra of paint sub-layers that are completely obscured by paint over-layers. Potential application areas include the analysis of paintings in art preservation and restoration avoiding the cross-sectional analysis used currently with this type of samples. The technique also holds promise for the development as a non-destructive subsurface tool for in situ analysis using portable instruments.
A recently developed variant of spatially offset Raman spectroscopy (SORS) for the non-invasive analysis of thin painted layers, micro-SORS, has been applied, for the first time, to real objects of Cultural Heritage -namely painted sculptures and plasters. Thin layers of paint originating from multiple restoration processes often applied over many centuries have been analysed non-destructively using micro-SORS to depths inaccessible to, or unresolvable into separate layers, by conventional confocal Raman microscopy. The concept has been demonstrated on several artistic artefacts of historical significance originating from Italy and dating from the medieval to the 18th century. The technique extends the depth applicability of Raman spectroscopy and with its inherently high chemical specificity that expands the portfolio of existing non-destructive analytical tools in Cultural Heritage permitting to avoid cross-sectional analysis often necessitated with this type of samples with conventional Raman microscopy. Currently, the method is non-invasive only for artworks that can be placed under Raman microscope although there is a prospect for its use in a mobile system with largely removed restrictions on sample dimensions.
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