Chemical Fingerprinting: Advanced Spectroscopy for Textile Material Verification
Technical deep dive into near-infrared (NIR) and Raman spectroscopy for rapid textile fiber identification, enabling automated sorting and DPP data validation.
Material verification is critical for circular economy claims, and advanced spectroscopy offers non-destructive, rapid analysis of textile fibers. Near-infrared (NIR) spectroscopy, commonly used in automated sorting facilities, identifies polymers by their unique absorption bands. For example, polyester has a distinct C-H overtone at 1650 nm, while cotton shows O-H absorption at 1450 nm. However, NIR struggles with blends and dark colors due to low signal-to-noise ratio.
[!IMPORTANT] For accurate blend analysis, a combination of NIR and Raman spectroscopy is recommended. Raman spectroscopy provides complementary information on molecular vibrations, excelling at identifying synthetic fibers like polyamide and elastane. The table below compares the two techniques:
| Parameter | NIR Spectroscopy | Raman Spectroscopy |
|---|---|---|
| Principle | Absorption of near-IR light | Inelastic scattering |
| Fiber types | Best for natural & semi-synthetic | Best for synthetic & dyes |
| Blend analysis | Limited (requires chemometrics) | Good (peak deconvolution) |
| Speed | <1 second per scan | 1-5 seconds |
| Cost | €15,000-30,000 | €30,000-80,000 |
| Portability | Handheld options available | Benchtop mostly |
To support DPP compliance, brands can embed a ‘material fingerprint’ in the passport using a hash of the spectral data. This allows recyclers to verify composition without destructive testing. The EU’s Horizon 2020 project ‘Fibersort’ demonstrated that NIR sorting can achieve 98% purity for single-fiber streams. For multi-material garments, automated disassembly remains a bottleneck, but spectroscopy can guide robotic sorting.