Deriving Pseudo Pure-Component NIR Spectra for Textile Blend Classification: A Methodological Framework for Researchers Working Without Pure-Fibre Reference Samples

Near-infrared hyperspectral imaging (NIR-HSI) is an established approach for optical identification of textile fibre composition, with applications in automated sorting and recycling.Apersistent limitation in this domain is the absence of pure-component reference spectrafor minority fibres — in particular elastane — in available open-access datasets. Withoutpure-component anchors, standard endmember-based classification and spectral unmixingapproaches cannot be directly applied.This framework proposes a methodology for deriving a pseudo pure-component spectralsignature for a minority fibre using only blend samples, by formulating the problem asa linear spectral unmixing equation in which known endmembers (cotton, polyester) aresubtracted from observed blend spectra and the residual is scaled by the minority fibre’sknown composition fraction. The approach is demonstrated on elastane detection usingthe OpenTextile-NIR dataset (Sormunen et al., 2026), with the derived signature validatedagainst NIST NIR-SORT 2.0 chemically verified spectra (Goodge et al., 2026).The methodology is documented in sufficient generality to be replicated for any minorityfibre type on any hyperspectral dataset where pure-component spectra exist for dominantfibres and blend composition labels are available. Statistical significance testing is embeddedat each decision point. Known limitations are indexed and propagated formally through thederivation as error terms