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A spectroscopic dataset for known provenance and post-consumer textiles
Katarina E Goodge1,2, Alexander K Landauer3, Cecelia J Vederman3,4
1National Institute of Standards and Technology, Material Measurement Laboratory, 100 Bureau Drive, Gaithersburg, MD 20899, USA. kg1024@georgetown.edu.
Scientific Data
|May 22, 2026
Summary
A new dataset of near-infrared (NIR) spectral data for textile fibers and fabrics is introduced. This resource aims to improve polymer identification and textile sorting system validation.
Area of Science:
- Analytical Chemistry
- Materials Science
- Textile Engineering
Background:
- Near-infrared (NIR) spectroscopy is valuable for chemical bond structure identification and polymer analysis.
- Limited availability of high-quality, known provenance NIR data hinders benchmarking in the textile industry.
- Proprietary or self-built databases restrict model performance evaluation for fiber classification and blend identification.
Purpose of the Study:
- To present a new, comprehensive dataset of textile specimens for NIR spectral analysis.
- To facilitate the development and validation of models for fiber classification and polymer blend identification.
- To support the advancement of textile sorting systems and industrial process validation.
Main Methods:
- Collection of benchtop and handheld NIR spectral data from textile specimens.
- Inclusion of fabric-scale microscopy images for complementary analysis.
- Organization of data into a structured repository for accessibility.
Main Results:
- A novel dataset addressing the scarcity of textile NIR spectral data has been created.
- The dataset supports applications in fiber content classification and textile sorting system validation.
- It provides a foundation for developing and benchmarking NIR-based analytical models.
Conclusions:
- The presented dataset enhances the capability for accurate fiber identification and polymer analysis in textiles.
- It promotes standardization and improved performance benchmarking for NIR spectroscopic methods in the textile industry.
- This resource is expected to accelerate innovation in automated textile sorting and quality control.
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