A spectroscopic dataset for known provenance and post-consumer textiles
Katarina E. Goodge, Alexander K. Landauer, Cecelia J. Vederman, Amanda L. Forster · 2026
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Description
Katarina E. Goodge and three colleagues released in 2026 a reference dataset to help sort textiles by fibre type with near-infrared light. Near-infrared spectroscopy reads chemical bonds quickly and without damage, making it attractive for identifying feedstock in recycling. Data with known provenance for textile fibres and fabrics have been scarce. Existing models usually rest on private libraries or home-built databases, so results across the industry cannot be compared.
The new dataset covers textile specimens for fibre content checks, system and software development, and testing of sorting systems. Its repository holds benchtop near-infrared spectra, handheld near-infrared spectra, and fabric-scale microscopy images, giving researchers matched measurements at different scales. For recyclers, instrument makers, and model builders, the value is a shared benchmark: methods can now be tested on the same known materials. Claims about sorting accuracy can be checked rather than asserted.
Catalog record
| CREATOR | Katarina E. Goodge, Alexander K. Landauer, Cecelia J. Vederman, Amanda L. Forster |
|---|---|
| YEAR | 2026 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 39 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |