Spectral imaging and a one-class classifier for detecting elastane in cotton fabrics

Abstract

Elastane detection is important for textile recycling as elastane fibers can hamper mechanical and chemical fiber recycling. Here, we report the use of near-infrared imaging spectroscopy and class modelling to detect 2-6% elastane in consumer cotton fabrics to provide alternatives to current detection methods, which are invasive and time-consuming. Our method automatically identified outlier fabrics and measurements with class-specific clustering and showed higher classification accuracies by averaging across individual pixel spectra to reduce sampling uncertainty. The final classification results showed median test set true positive and true negative rates of 89-97% based on randomized resampling. Class modelling offers clear benefits compared to commonly used discriminant classifiers as it allows modelling new classes using only a set of target samples without requiring representative training objects from all the other classes. Overall, these results open the possibility for fast non-invasive detection of small amounts of elastane in cotton, taking us a step closer to a circular economy of textiles.