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

Ella Mahlamäki1, Inge Schlapp-Hackl2, Tharindu Koralage2

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Near-infrared imaging spectroscopy can quickly and non-invasively detect elastane in cotton textiles, aiding textile recycling efforts. This method offers a faster alternative to current invasive techniques for a more circular economy.

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Area of Science:

  • Materials Science
  • Analytical Chemistry
  • Textile Engineering

Background:

  • Elastane fibers in textiles pose challenges for mechanical and chemical recycling processes.
  • Current methods for elastane detection are invasive and time-consuming, hindering efficient textile recycling.

Purpose of the Study:

  • To develop a fast, non-invasive method for detecting elastane in cotton fabrics.
  • To provide an alternative to existing time-consuming and invasive elastane detection techniques.

Main Methods:

  • Utilized near-infrared (NIR) imaging spectroscopy combined with class modeling.
  • Employed class-specific clustering for outlier identification and averaged pixel spectra to reduce uncertainty.
  • Applied randomized resampling for robust classification accuracy assessment.

Main Results:

  • Successfully detected 2-6% elastane in consumer cotton fabrics with high accuracy.
  • Achieved median test set true positive and true negative rates of 89-97% through randomized resampling.
  • Demonstrated the effectiveness of class modeling for identifying new material classes without exhaustive training data.

Conclusions:

  • Near-infrared imaging spectroscopy and class modeling offer a viable solution for rapid, non-invasive elastane detection in textiles.
  • This advancement moves closer to enabling a circular economy for textiles by improving recycling processes.
  • The developed method provides a significant improvement over traditional, labor-intensive detection techniques.