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dc.contributor.authorRiba Ruiz, Jordi-Roger
dc.contributor.authorCantero Gómez, M. Rosa
dc.contributor.authorCanals, Trini
dc.contributor.authorPuig, Rita
dc.description.abstractThe 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.
dc.relation.isformatofVersió postprint del document publicat a:
dc.relation.ispartofJournal of Cleaner Production, 2020, vol. 272, p. 123011
dc.rightscc-by-nc-nd (c) Elsevier, 2020
dc.subjectTextile sorting
dc.subjectInfrared spectroscopy
dc.subjectTextile fibers
dc.subjectMultivariate analysis
dc.subjectPattern recognition
dc.titleCircular economy of post-consumer textile waste: Classification through infrared spectroscopy

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cc-by-nc-nd (c) Elsevier, 2020
Except where otherwise noted, this item's license is described as cc-by-nc-nd (c) Elsevier, 2020