More than half of all clothing and textile products are considered suitable for recycling, yet a significant share never makes its way back into the supply chain. Large volumes of donated clothing, combined with the slow and labour-intensive nature of manual textile sorting, continue to create major barriers to efficient recycling and reuse.
To address this challenge, researchers at the U.S. National Institute of Standards and Technology (NIST) have developed a new research-grade test material designed to help the textile industry improve the identification and sorting of textile fibres.
AI Could Transform Textile Sorting
Artificial intelligence is increasingly being explored as a way to make textile sorting faster and more efficient. However, the industry needs reliable standards and testing materials to determine whether different technologies can accurately identify the fibres present in clothing and fabrics.
NIST’s new material is intended to provide researchers and industry with a common benchmark for testing fibre-identification methods. Improved identification could enable more accurate sorting, increase textile recovery and reduce the amount of material sent to landfill or incineration.
“This textile material will help validate sorting methods and make textile sorters’ measurements comparable from one center to another,” said NIST materials research engineer Amanda Forster. According to NIST, the initiative could help expand supply chains and recover more of the economic value embedded in discarded textiles.
How Textile Fibre Sorting Works
One of the commonly used technologies in textile sorting is near-infrared (NIR) spectroscopy. At recycling facilities, handheld scanners can direct light onto a textile and measure how the material absorbs, reflects or scatters that light.
The resulting spectral signature acts as a fingerprint that can help identify the fibre composition of the material. Workers can then separate textiles into appropriate categories for further processing.
NIR technology is also being incorporated into automated sorting systems. In these systems, textiles move along conveyor belts while cameras, sensors and algorithms analyse the materials and automatically direct them into different streams.
Other technologies include computer vision, which can identify textiles based on characteristics such as colour and appearance, and hyperspectral imaging, which combines imaging capabilities with spectral information.
However, recycling facilities may use different equipment, algorithms and measurement approaches. A common reference material can therefore help determine how accurately these systems identify textile fibres.
NIST’s New Textile Test Material
NIST has designated the new material as Research Grade Test Material (RGTM) 10279, Textiles for Feedstock Identification.
The material consists of five fabric squares measuring approximately 4 inches (10.2 cm) on each side. The samples are made using different fibres and include both dyed and undyed materials.
Unlike NIST’s traditional standard reference materials, research-grade test materials can be produced relatively quickly and distributed to laboratories for evaluation. Participating laboratories analyse the materials and provide results that help researchers determine whether the material is suitable for its intended applications.
For the textile recycling industry, RGTM 10279 could become a useful benchmark for evaluating fibre-sorting technologies and validating algorithms used in automated systems.
Tackling the Challenge of Fibre Blends
The need for reliable fibre identification is becoming increasingly important as textile products contain more complex fibre blends.
A garment may appear to be made from a particular fibre based on its labelling, while its actual composition could contain additional materials. Accurate identification can therefore improve recycling outcomes and provide greater transparency across the textile supply chain.
“This material also provides a way to detect things that aren’t reported on the label, which is important for recycling,” Forster said.
The technology could also be useful earlier in the textile value chain. Brands and manufacturers could potentially use such testing methods to verify whether fabrics purchased from suppliers contain the fibre composition specified in procurement documents.
For example, if a brand purchases fabric described as 100% cotton but the material actually contains polyester, an accurate fibre-identification system could detect the difference before the fabric is converted into finished garments.
Supporting AI-Enabled Recycling
AI has significant potential to transform textile recycling by analysing large volumes of materials much faster than manual sorting. Yet the effectiveness of AI systems depends heavily on the quality and reliability of the data used to train and validate them.
NIST researchers see standardised test materials as an important part of addressing this challenge.
“We’ve identified an industrywide measurement challenge,” said NIST researcher Michelle Seitz. She noted that standards such as RGTM 10279 can support improvements in textile identification and sorting while helping advance AI-enabled sorting technologies and U.S. manufacturing.
The development is particularly relevant as textile recyclers seek to move from labour-intensive manual processes towards automated systems capable of handling increasingly complex textile waste streams.
From Recycling to Quality Control
The potential applications of the test material extend beyond post-consumer textile recycling.
Laboratories and manufacturers could use the material as a benchmark for developing and comparing new fibre-identification technologies. It could also support quality-control processes within textile production.
The material may eventually help manufacturers verify fibre composition before garments are produced, enabling brands to detect discrepancies between purchased fabrics and supplier specifications.
NIST researchers also noted that fibre-identification technology could potentially have applications in areas such as fashion authentication, although this is not currently an active focus of the research team.
Next Step: Industry Validation
NIST is now working to determine how effectively RGTM 10279 performs in real-world industry applications.
As part of a study, laboratories, manufacturers and other organisations will use their own fibre-identification techniques to analyse the test material. The composition of the RGTM is not disclosed to participants, allowing researchers to assess how accurately different approaches identify the materials.
The feedback will remain anonymous and will be used by NIST researchers to develop a more thoroughly characterised reference material that reflects the needs of the textile recycling and manufacturing industries.
A Step Toward Smarter Textile Circularity
Accurate fibre identification is becoming a critical component of textile circularity. As the industry moves towards automated sorting, AI-based systems and higher-value recycling, the ability to reliably distinguish individual fibres and blends will become increasingly important.
NIST’s research-grade textile material represents a step towards establishing common benchmarks for these technologies. By improving measurement consistency and validating fibre-identification systems, such standards could help unlock more efficient textile recovery and support a more circular textile supply chain.