Tailorlux wins patent for quantification of material blends

German traceability technology manufacturer, Tailorlux, has been granted a patent for its development of a sensor that can detect and measure fibre blends in products.

The device, which employs a spectroscopic signal and can also be used to assess recycled content claims, is a significant step toward more transparent supply chains, according to the company.

The patent was processed and given in record time, according to Tailorlux, because to the ‘Green Channel,’ which expedites applications for inventions that have an environmental benefit.

The patent GB2592691 involves the use of a composite spectroscopic signal to detect blends in materials and to quantify polymers and fibres in products. This is a significant step in the direction of more transparent supply chains. The patent was processed via the “Green Channel” for technologies with an environmental benefit.

The new invention is taking this technology one step further by allowing for the quantitative analysis of tagged objects using in-field and in-line sensors that are directly connected to a database.

Dr. Steffen Driever from Tailorlux, said that they can establish the proportion of marked material moving through the supply chain in order to have unambiguous proof concerning blends in fibres, yarn, and fabrics. This application can be used to determine recycled content claims in particular. After successful trials with a sensor prototype in 2021, which used NIR/VIS Spectroscopy and cameras to assess PET and Cotton content in a fabric, the patent is a logical next step.

The patent is in accordance with the timeline for the development of a new handheld gadget, which will be unveiled in 2022. This gadget will provide access to a VIS/NIR database of analysed items, allowing the composition of fabric to be determined even without the use of a tracer. The patent describes a system that contributes to blockchain-based solutions. During production, the sensor data and the data from the unique spectral composite signal are important for creating unique datasets.