Sorting textile waste by fibre composition is a harder problem than it might first appear – many fabrics look visually similar regardless of whether they're cotton, polyester, or a blend of the two, and dyes and finishes can further obscure the underlying material. Hyperspectral imaging addresses this by capturing data across a much wider range of the light spectrum than the human eye or a standard camera can perceive.

AUTOMATED SORTING OF TEXTILE WASTE

Each material reflects and absorbs light at specific wavelengths in characteristic ways – a kind of spectral fingerprint. Hyperspectral sensors capture this fingerprint across hundreds of wavelength bands for every point in their field of view, providing the data needed to distinguish between fibre types that would be indistinguishable to a conventional vision system, even when those fibres are blended within the same garment.

ARTIFICIAL INTELLIGENCE AND ARTIFICIAL VISION

The volume of data hyperspectral sensors generate is far too large to interpret manually, which is where artificial intelligence comes in – machine learning models trained on hyperspectral data can learn to recognise the spectral signatures of different fibre types and blends, translating raw sensor data into sorting decisions in real time. This combination of hyperspectral sensing and AI-based interpretation is what makes automated textile sorting by composition practically feasible.

MORE RECYCLING, MORE SUSTAINABILITY

The practical payoff of this technology is straightforward: textile waste that can be sorted by composition can be recycled into fibre-specific streams, rather than ending up as low-value mixed material or, worse, going to landfill or incineration. PICVISA's ECOSORT TEXTIL system uses this kind of advanced sensing to bring this level of sorting accuracy to textile recycling facilities, supporting the broader push towards a genuinely circular textile industry.

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