In today’s textile industry, the escalating expenses associated with implementing eco-friendly practices and the consequent proliferation of false green claims in the market have resulted in a growing concern regarding the credibility of environmental claims made by suppliers. Many suppliers, in facts, choose to acquire limited quantities of organic or recycled materials, reserving them for the production of lots subject to controls, while standard production often relies on more cost-effective, non-organic, or non-recycled materials. This practice poses a challenge for both consumers and regulatory bodies, leading to a lack of trust in environmental claims and hindering the promotion of sustainability.

To confront this challenge, HYPERTEX proposes an innovative solution harnessing hyperspectral imaging techniques to safeguard the accuracy of data provided throughout the textile supply chain. Suppliers can utilize the system to certify the accuracy of the information they provide for the Digital Product Passport (DPP), while customers purchasing textiles can verify this information included, ensuring that environmental claims are accurate and trustworthy. This solution aims to enhance trust in environmental claims, combat false green declarations, and promote sustainability in the textile industry.

HYPERTEX consists of a portable vision station made of a hyperspectral camera capturing images of the textiles. This portable setup allows for flexibility in positioning, enabling operators to manually place textiles underneath the camera for analysis. Leveraging hyperspectral imaging technology, HYPERTEX captures detailed spectral information across a wide range of wavelengths, allowing for the detection of subtle variations indicative of specific materials, processes, and environmental characteristics. The captured images are processed in real-time using advanced algorithms to extract spectral signatures, which are then analysed by machine learning algorithms to identify key features and classify the material composition of the textile. The inspection is performed for different random samples of the same batch.