Spatially Resolved Mapping of Hidden Recycling Disruptors in Postconsumer Textiles by Near-Infrared Hyperspectral Imaging

Accurate characterization of heterogeneous postconsumer textiles remains a major challenge for textile-to-textile (T2T) recycling. Here, we demonstrate the utility of near-infrared hyperspectral imaging (NIR-HSI) for visualizing textile heterogeneity and detecting localized minor constituents that may represent recycling disruptors but are frequently overlooked by conventional characterization approaches. Unlike single-point or spatially averaged spectroscopic measurements, NIR-HSI provides spatially resolved chemical information throughout the imaged textile region, enabling the identification of localized fibers, trims, coatings, optically accessible layers, and other secondary constituents. NIR-HSI also enables the localization and differentiation of chemical contaminants that may compromise recycling feedstock quality. The resulting supervised material-distribution maps reveal compositional complexity not readily captured by bulk measurements and demonstrate how spatial averaging can obscure localized material heterogeneity and contaminant distributions. These findings highlight the value of NIR-HSI for improved textile characterization, feedstock assessment, and informed decision-making within emerging circular textile systems.