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Characterization of Near Infrared-Dye Colored Fabrics Using Hyperspectral Imaging.

Rajbir Kaur1, Muhammad Mudassir Arif Chaudhry1, Catherine Findlay1

  • 1Department of Biosystems Engineering, University of Manitoba, Winnipeg, MB, R3 T 5V6, Canada.

Applied Spectroscopy
|June 17, 2024
PubMed
Summary

Near-infrared (NIR) dyes were analyzed on cotton fabric using hyperspectral imaging. Visible-NIR (Vis-NIR) imaging successfully differentiated dye concentrations, crucial for effective camouflage textiles.

Keywords:
PCAPLS-DATextilesdetectabilitydiscriminationhyperspectral imagingnear-infrared dyespartial least squares discriminate analysisprincipal component analysis

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Area of Science:

  • Textile science and materials engineering
  • Spectroscopy and optical characterization
  • Military applications and defense technology

Background:

  • Near-infrared (NIR) dyes are vital for camouflage applications due to their light interaction properties.
  • Fabric dyeing parameters like concentration and temperature significantly affect color and fastness, impacting camouflage effectiveness.
  • Hyperspectral imaging offers a non-destructive method for characterizing dyed textiles.

Purpose of the Study:

  • To characterize cotton fabric dyed with NIR-absorbing dyes using visible-NIR (Vis-NIR) and short-wave infrared (SWIR) hyperspectral imaging.
  • To discern spectral changes related to dye concentration and dyeing temperature.
  • To evaluate the potential for spectral analysis in ensuring consistent camouflage performance.

Main Methods:

  • Cotton fabrics were dyed at varying NIR dye concentrations (2.5%, 5%, 10%) and temperatures (55°C, 85°C).
  • Visible-NIR (Vis-NIR) and short-wave infrared (SWIR) hyperspectral imaging were employed for spectral data acquisition.
  • Principal Component Analysis (PCA) and Partial Least Squares Discriminant Analysis (PLS-DA) were used for spectral data analysis and classification.

Main Results:

  • PCA models effectively distinguished fabrics based on dye concentration and dyeing temperature.
  • PLS-DA models achieved high classification accuracies (75-100%) in the Vis-NIR range for differentiating dye concentrations.
  • SWIR spectral data did not reveal differences in NIR dye concentrations, indicating potential for stealth applications.

Conclusions:

  • Visible-NIR hyperspectral imaging is a viable method for quality control of NIR-dyed camouflage fabrics.
  • The inability to detect concentration differences in SWIR aligns with the goal of creating indistinguishable camouflage under varying conditions.
  • Findings support the development of advanced stealth textiles for military applications by ensuring consistent NIR spectral properties.