Challenges and spectra interpretability in textile sorting: NIR hyperspectral images and chemometrics

Area of Science:

• Materials Science
• Analytical Chemistry
• Environmental Science

Background:

• Textile waste poses a significant environmental challenge, necessitating advanced recycling solutions.
• Accurate sorting of diverse textile materials is essential for effective waste reduction and sustainability.
• Current textile sorting methods struggle with complex material compositions and minor component detection.

Purpose of the Study:

• To explore the potential of Near-Infrared (NIR) hyperspectral imaging (HSI) for advanced textile sorting.
• To assess NIR-HSI's ability to differentiate fiber types and detect minor components like elastane.
• To evaluate the integration of portable spectroscopic techniques and chemometric analysis for improved textile classification.

Main Methods:

• Spectral characterization of natural, synthetic, and blended textile fibers using NIR-HSI.
• Integration of portable spectroscopic techniques to enhance spectral data interpretation.
• Application of multivariate chemometric techniques (PCA, MCR) for data analysis and pattern identification.

Main Results:

• NIR-HSI demonstrated potential in differentiating textile compositions and identifying spatial distribution of components.
• The study identified challenges in detecting low concentrations of elastane within fiber blends.
• Variability in spectral features due to treatments and environmental factors was noted as a significant challenge.

Conclusions:

• NIR-HSI, combined with chemometrics, offers a promising approach for sustainable textile sorting and recycling.
• Standardized spectral databases are crucial for improving the reliability of NIR-HSI interpretation.
• Further research is needed to overcome challenges related to spectral variability and low-concentration component detection.