Material identification for complex products
Modern footwear and textiles consist of multi-layered material combinations and blended structures. Visual inspection alone is often insufficient to determine composition. At the same time, regulatory requirements and sustainability targets are increasing the demand for reliable and traceable material data. Mobile NIR spectroscopy enables on-site analysis and delivers results within seconds.
Applications in footwear production and recycling
The solution identifies common materials such as ethylene vinyl acetate, polyester and polyurethane in various shoe components. Measurements can be carried out during incoming goods inspection, in production, and in sorting or recycling processes. This improves material separation, supports quality control and enhances recycling readiness while reducing contamination risks and manual sorting efforts.
Analysis of textile blends
In the textile industry, the technology enables the identification of materials and blends such as cotton/polyester or viscose/polyester. For binary blends, fiber shares are quantified as percentages. Complex compositions can be analyzed and assessed on this basis.
The textile application will be available from May 2026. It is designed for textile and carpet manufacturers as well as sorting and recycling companies. It supports the validation of material declarations, improves sorting accuracy and facilitates closed-loop recycling.
Integrated solution for decentralized use
The system combines a handheld NIR spectrometer, a mobile application with cloud-based data analysis and a customer portal for documentation and data export. The handheld device enables fast, non-destructive measurements even under demanding conditions. The app guides users through the process and provides immediate, interpretable results.
Standardized interfaces allow integration into existing software environments. This supports data processing in the context of regulatory frameworks such as the Digital Product Passport and sustainability performance metrics.
