The identification of textile fibres from cultural property provides information about the object's technology. Today, microscopic examination remains the preferred method, and molecular spectroscopies (e.g. Fourier transform infrared (FTIR) and Raman spectroscopies) can complement it but may present some limitations. To avoid sampling, non-invasive fibre optics reflectance spectroscopy (FORS) in the near-infrared (NIR) range showed promising results for identifying textile fibres; but examining and interpreting numerous spectra with features that are not well defined is highly time-consuming. Multivariate classification techniques may overcome this problem and have already shown promising results for classifying textile fibres for the textile industry but have been seldom used in the heritage science field. In this work, we compare the performance of two classification techniques, principal component analysis–linear discrimination analysis (PCA-LDA) and soft independent modelling of class analogy (SIMCA), to identify cotton, wool, and silk fibres, and their mixtures in historical textiles using FORS in the NIR range (1000–1700 nm). We built our models analysing reference samples of single fibres and their mixtures, and after the model calculation and evaluation, we studied four historical textiles: three Persian carpets from the nineteenth and twentieth centuries and an Italian seventeenth-century tapestry. We cross-checked the results with Raman spectroscopy. The results highlight the advantages and disadvantages of both techniques for the non-invasive identification of the three fibre types in historical textiles and the influence their vicinity can have in the classification.

A concise insight into the outputs provided by the latest prototype of visible‐near infrared (Vis‐NIR) multispectral scanner (National Research Council‐National Institute of Optics, CNR‐INO, Italy) is presented. The analytical data acquired on an oil paintingMadonna of the Rabbitby É. Manet are described. In this work, the Vis‐NIR was complemented with X‐ray fluorescence (XRF) mapping for the chemical and spatial characterization of several pigments. The spatially registered Vis‐NIR data facilitated their processing by spectral correlation mapping (SCM) and artificial neural network (ANN) algorithm, respectively, for pigment mapping and improved visibility ofpentimentiand of underdrawing style. The data provided several key elements for the comparison with a homonymous original work by Titian studied within the ARCHive LABoratory (ARCHLAB) transnational access project.

We report on tests to remove naturally and artificially aged shellac varnish by laser and traditional chemical cleaning from the substrates of mural paintings. Optical tools were used for the evaluation of cleaning processes, in particular laser microprofilometry to assess the changes in the surface morphology and time-domain confocal optical coherence tomography (OCT) to evaluate varnish thickness. The cleaning assessment was integrated with molecular characterization provided by portable Fourier transform infrared spectroscopy operating in reflectance mode, and colorimetric measurements. This complete analytical approach led to optimized laser-based cleaning tests at 1.9 and 2.6 J/cm2 in the presence of isopropanol, which ensured controllable and safe partial removal of shellac varnish without alteration to the substrate morphology.

Raman spectroscopy (RS), for its robust analytical capabilities under constant development, is a powerful method for the identification of various materials, in particular pigments in cultural heritage. Characterization of the artist’s palette is of fundamental importance for the correct formulation of restoration intervention as well as for preventive conservation of artworks. Here we examine the number and variability of research studies exploiting Bravo handheld Raman spectrophotometer relying on the excitation of Raman signal with temperature-shifted diode lasers emitting at 852 and 785 nm. To this end, we explore the spectral features of common historical pigments examined as powders and in the paint layer. We show that some materials may exhibit slightly different spectra as concerns especially the relative intensity of Raman lines with 852 nm laser excitation wavelength as compared to the standard 785 nm. The aim is to provide the research community with a reference spectral database that facilitates the identification of unknown pigments using the 852 nm excitation source.