Abstract

Global textile production is mainly based on polyester and cotton fibers. A majority of textiles at the end of their lifecycle are currently landfilled or incinerated, but will be increasingly recycled in the future. Here, we discuss how the polyester content in blended textiles can be estimated based on hyperspectral near infrared imaging with the aim of developing machine vision for textile characterization and recycling. Differences in the textile samples were first visualized based on a principal component model and the polyester contents of individual image pixels were then predicted using image regression. The results showed average prediction errors of 2.24.5% within a range of 0-100% polyester and enabled visualizing the spatial changes in the polyester contents of the textiles. We foresee that digitalized tools similar to what we report here will be increasingly important in the future as more emphasis is placed on coordinated collection, sorting and reuse of waste textiles.

Keywords

Textiles; Cellulose; Polyester; Hyperspectral imaging; Near infrared; Regression

Published in

Resources, Conservation and Recycling
2020, Volume: 161, article number: 105007
Publisher: ELSEVIER

SLU Authors

• Mäkelä, Mikko

  • Department of Forest Biomaterials and Technology, Swedish University of Agricultural Sciences
    
  • Aalto University

Sustainable Development Goals

Ensure sustainable consumption and production patterns


UKÄ Subject classification

Textile, Rubber and Polymeric Materials
Computer Vision and Robotics (Autonomous Systems)


Publication identifier

DOI: https://doi.org/10.1016/j.resconrec.2020.105007

Permanent link to this page (URI)

https://res.slu.se/id/publ/108071