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New Fabric “Fingerprint” Technology To Transform Textile Recycling And Supply Chains

• By  Textile Excellence
• July 28, 2026

A new research breakthrough could help solve one of the biggest challenges facing the global textile industry - identifying, sorting and recycling used fabrics efficiently.

Researchers at the National Institute of Standards and Technology (NIST) have developed a new textile reference material designed to improve fibre identification technologies, strengthen recycling systems and help bring more discarded textiles back into the manufacturing supply chain.

The development comes as the industry faces growing pressure to reduce textile waste. Studies estimate that around 56% of clothing and textiles can be recycled or recovered, but a large share never returns to the production cycle due to difficulties in sorting fabrics accurately and cost-effectively.

NIST’s new Research Grade Test Material (RGTM) 10279 – Textiles for Feedstock Identification is designed to help laboratories, recycling facilities and manufacturers validate their textile identification methods. The material consists of five fabric swatches made from different fibres, including dyed and undyed samples.

“Textile sorters need reliable reference materials to ensure measurements are comparable across facilities,” said NIST materials research engineer Amanda Forster. The new material could help improve sorting accuracy, expand recycling supply chains and recover more economic value from discarded textiles.

AI needs better textile data

The textile industry is increasingly adopting artificial intelligence (AI) and automated sorting technologies to improve recycling efficiency. However, these systems require accurate data and reliable standards to identify different fibres.

Today, recycling facilities use technologies such as near-infrared (NIR) spectroscopy, computer vision and hyperspectral imaging to analyse fabrics. NIR scanners identify fibres by measuring how light interacts with textile materials, while automated systems use cameras, sensors and algorithms to sort fabrics on conveyor lines.

However, mixed-fibre fabrics remain a major challenge. Many modern textiles contain blends such as cotton-polyester combinations that are difficult to identify, affecting recycling efficiency.

NIST’s reference material could help verify fibre composition, test sorting algorithms and improve quality control across the textile value chain.

Supporting brands, manufacturers and recycling

The new material could also help brands and manufacturers ensure they receive the exact fabrics they purchase. For example, if a fabric sold as 100% cotton contains polyester, the reference system could help detect the mismatch.

It may also support future applications in textile authentication and quality verification.

The initiative is particularly relevant as the industry moves towards circularity, with manufacturers seeking better ways to recover fibres and reduce dependence on virgin raw materials.

“Standards like this help improve textile identification and sorting, supporting advances in AI-enabled sorting and manufacturing,” said NIST researcher Michelle Seitz.

NIST is now inviting laboratories, manufacturers and industry organisations to test the material and provide feedback. The findings will help develop a more comprehensive reference standard for textile identification.

For the textile industry, improved fibre recognition technology could become a critical enabler for circular manufacturing, allowing discarded textiles to be sorted faster, recycled more efficiently and converted back into valuable raw materials.

“Standards like this help improve textile identification and sorting, supporting advances in AI-enabled sorting and manufacturing,” said NIST researcher Michelle Seitz. NIST is now inviting laboratories, manufacturers and industry organisations to test the material and provide feedback. The findings will help develop a more comprehensive reference standard for textile identification. For the textile industry, improved fibre recognition technology could become a critical enabler for circular manufacturing, allowing discarded textiles to be sorted faster, recycled more efficiently and converted back into valuable raw materials.