We used Raman spectroscopy combined with multivariate analysis to study noninvasively naturally and artificially aged samples of silk and wool fibres dyed with turmeric and saffron dyes, following historical recipes. The Raman spectra obtained from all the samples using the two excitation lasers (785 and 1064 nm) showed the characteristic peaks of each dye and fibre. The 1064‐nm macro FT‐Raman offered more information regarding the degradation of the fibre substrate, whereas the μ‐Raman at 785‐nm spectra proved more suitable in the identification of dye characteristic peaks. We also performed colorimetric measurements to evaluate the colour change upon artificial ageing and compared them with the information obtained with Raman spectroscopy. The results suggest saffron has a higher lightfastness than turmeric and that silk fibres suffer higher fading and more evident degradation than wool. Raman spectroscopy informs about the chemical composition of the colourants and fibres and their changes upon ageing without sample pretreatment. Finally, we analysed a historical wool sample coming from a 19th‐century Persian carpet, and we identified turmeric.
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.
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