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Updated: Dec 12, 2025

Multicolor Fluorescence Detection for Droplet Microfluidics Using Optical Fibers
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A Novel Approach to Using Spectral Imaging to Classify Dyes in Colored Fibers.

G M Atiqur Rahaman1,2, Jussi Parkkinen3, Markku Hauta-Kasari1,3

  • 1Computational Spectral Imaging Lab, School of Computing, University of Eastern Finland, FI-80101 Joensuu, Finland.

Sensors (Basel, Switzerland)
|August 9, 2020
PubMed
Summary
This summary is machine-generated.

Spectral imaging combined with machine learning accurately classifies textile dyes as natural or synthetic. This non-destructive method uses specific wavelengths in the short-wave infrared region for efficient screening.

Keywords:
SVMclassificationcultural heritagedyeslogistic regressionspectral imaging

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Area of Science:

  • Materials Science and Engineering
  • Cultural Heritage Preservation
  • Computational Spectroscopy

Background:

  • Analysis of dyes on historical textiles is crucial for conservation and display strategies in museums.
  • Existing diagnostic technologies for dye analysis are often destructive to valuable cultural heritage objects.
  • Spectral reflectance imaging offers a non-destructive, spatially resolved alternative for material analysis.

Purpose of the Study:

  • To investigate the capability of spectral imaging coupled with machine learning for non-destructive dye classification in textile fibers.
  • To differentiate between natural and synthetic dyes using spectral data.
  • To identify key spectral bands for accurate dye classification.

Main Methods:

  • Application of sparse logistic regression on spectral reflectance data to identify discriminating wavelength bands.
  • Utilizing support vector machine (SVM) algorithm for dye classification based on selected spectral bands.
  • Focusing on the short-wave infrared (SWIR) region (1000-2500 nm) for spectral analysis.

Main Results:

  • Nine selected spectral bands in the SWIR region achieved a dye classification accuracy of 97.4% (kappa 0.94).
  • Accurate dye classification was demonstrated using specific bands at 1480 nm, 1640 nm, and 2330 nm.
  • The study confirms the potential of spectral imaging and machine learning for preliminary dye screening.

Conclusions:

  • Spectral imaging with machine learning provides a highly accurate, non-destructive method for classifying textile dyes.
  • The identification of key spectral bands paves the way for developing portable, cost-effective screening devices.
  • This technique holds significant promise for the analysis and preservation of dyed cultural heritage textiles.