A Novel Methodology for AI-based Sorting of Post-Consumer Textile Using Spectrophotometer

The growth of the textile sector worldwide, coupled with the extensive utilisation of synthetic polymers, is exacerbating challenges related to the global plastic waste problem. To effectively tackle this problem, a crucial aspect during recycling is the accurate identification of the composition of textiles, to allow the most appropriate chemical and mechanical treatments to separate natural fibres from synthetic ones. In this work, we present preliminary results achieved by leveraging machine learning approaches on spectrophotometry information extracted from textile samples to identify cotton and polyester samples.