Multimodal\nHSI Combined with Multiblock Data Fusion:\nA New Tool for the Study of Time-Dependent Alteration Processes in\nDyed Textiles

The present study describes an innovative approach for\nthe study\nof time-dependent alteration processes. It combines an advanced hyperspectral\nimaging (HSI) system, to collect visible reflectance and fluorescence\nspectral data sets sequentially, with a tailored multiblock data processing\nmethod. This enables the modeling of chemical degradation maps and\nthe early, spatially resolved detection of dye alteration in textiles.\nA chemometric method based on data fusion and principal component\nanalysis was employed to identify spectral features of dye degradation,\ncombining and enhancing information from reflectance and fluorescence\nHSI data. The most significant spectral profiles extracted were used\nto develop an asymmetric Gaussian-based pixel-by-pixel fitting model\napplied to the HSI fluorescence data set, enabling the reconstruction\nof degradation maps for rapid and intuitive visualization. In particular,\nchanges in intensities and horizontal shift of dye emission peaks\nwere pixel-by-pixel evaluated and fitted for the reconstruction of the degradation maps. Artificially\naged wool samples tinted with indigo carmine (IC) dye served as a\ncase study. IC is extensively used in textiles, and it is notable\nfor its light sensitive. The results show that this approach effectively\nidentifies spatial variations and chemical changes in dyed wool fibers,\noffering potential for sustainable conservation of historical textiles\nand other types of time-dependent processes. Thus, by amplifying variation\nin spectral profiles induced over time by aging, even minimal changes\nat early stages can be easily detected and localized, offering powerful\ntools for future studies on food and drug shelf life and stability,\nas well as forensic trace analysis.