Abstract
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.
Highlights
Non-invasive methods such as external reflection FTIR and handheld portable NIR spectrometry—based on miniaturized technology that may impact on the instrumental performance [17]—proved suitable for the identification of fibres without sampling [18,19,20,21] as well as fibre optics reflectance spectroscopy (FORS) in the NIR region [22, 23]
The training and test sets results show that the main advantage of soft independent modelling of class analogy (SIMCA), in comparison with principal component analysis–linear discrimination analysis (PCA-linear discriminant analysis (LDA)), is the possibility to calculate a tailored model for a specific target class, by selecting the number of principal components (PCs) require to achieve the minimum error rate, thanks to the model calculation based only on sample that belong to the target class
Despite this good performance on model samples, the study of historical samples revealed complex and showed that it is necessary to calculate a model with a high sensitivity and a lower specificity in order to manage to include the variations present in the real case not considered in the initial model
Summary
Non-invasive methods such as external reflection FTIR and handheld portable NIR spectrometry—based on miniaturized technology that may impact on the instrumental performance (e.g. noisier detectors and lower spectral resolution) [17]—proved suitable for the identification of fibres without sampling [18,19,20,21] as well as fibre optics reflectance spectroscopy (FORS) in the NIR region [22, 23] The latter relies on the use of optic fibres to carry the reflected light (UV, Vis, and NIR), and generally employ better performance components (e.g. InGaAs detectors) [24]. Multilinear regressions (MLR), extreme learning machine (ELM), and partial least square (PLS) predicted efficiently the fibre content in blended yarns with both natural and synthetic fibres [32, 36, 37]
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