| Title |
Circular economy of post-consumer textile waste: Classification through infrared spectroscopy |
| ID_Doc |
28700 |
| Authors |
Riba, JR; Cantero, R; Canals, T; Puig, R |
| Title |
Circular economy of post-consumer textile waste: Classification through infrared spectroscopy |
| Year |
2020 |
| Published |
|
| DOI |
10.1016/j.jclepro.2020.123011 |
| Abstract |
The textile and fashion industry is amongst the most resource-intensive and polluting industries, thus impacting the natural environment. During the last decades, there has been an increase in the manufacturing of textiles. Europe consumes large amounts of textiles and clothing due to the current "buy-and-throw-away" culture, so it is crucial to minimize the environmental footprint of the textile and fashion industry. To this end, fashion and textiles should be part of a circular economy, thus extending the life of textiles and clothes, while retaining textile fibers within a closed circuit. There is a need of increasing textile recycling and reuse to minimize the production of virgin textile fibers. However, textiles are mostly sorted manually, thus to process huge volumes of materials and reduce the associated costs, automated sorting systems are required. This paper presents an approach for the sensing and classifying parts of an automatic waste-textile-sorting machine. To this end, the infrared spectra of the textile samples is analyzed and, by applying suitable statistical multivariate methods specially designed to solve classification problems, 100% classification accuracy of unknown fiber samples is reached. The results allow predicting that textile-fibers can be automatically classified with 100% accuracy at high speed, with no need to apply any prior analytical treatment to the textile samples. (C) 2020 Elsevier Ltd. All rights reserved. |
| Author Keywords |
Textile fibers; Textile sorting; Multivariate analysis; Infrared spectroscopy; Classification; Pattern recognition |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000570238100005 |
| WoS Category |
Green & Sustainable Science & Technology; Engineering, Environmental; Environmental Sciences |
| Research Area |
Science & Technology - Other Topics; Engineering; Environmental Sciences & Ecology |
| PDF |
|