Abstract
In order to add value to recycled textile material and to guarantee that the input material for recycling processes is of adequate quality, it is essential to be able to accurately recognise and sort items according to their material content. Therefore, there is a need for an economically viable and effective way to recognise and sort textile materials. Automated recognition and sorting lines provide a method for ensuring better quality of the fractions being recycled and thus enhance the availability of such fractions for recycling. The aim of this study was to deepen the understanding of NIR spectroscopy technology in the recognition of textile materials by studying the effects of structural fabric properties on the recognition. The identified properties of fabrics that led non-matching recognition were coating and finishing that lead different recognition of the material depending on the side facing the NIR analyser. In addition, very thin fabrics allowed NIRS to penetrate through the fabric and resulted in the non-matching recognition. Additionally, ageing was found to cause such chemical changes, especially in the spectra of cotton, that hampered the recognition.
Highlights
Reusing and recycling discarded textiles are, in general, preferable waste management options to incineration and landfilling
To add value to the recycled material and to guarantee that it has adequate quality as an input material for the subsequent recycling processes, it is essential to be able to recognise and sort the item according to its material content [2]
There are methods available for the identification of textile materials, such as ISO standardised quantification methods based on different dissolution behaviour (ISO 1833-1, etc.), morphological differences detected by microscopy [4], DNA
Summary
Reusing and recycling discarded textiles are, in general, preferable waste management options to incineration and landfilling. There are methods available for the identification of textile materials, such as ISO standardised quantification methods based on different dissolution behaviour (ISO 1833-1, etc.), morphological differences detected by microscopy [4], DNA recognition [5] and differences in thermal behaviour detected by differential calorimetry, thermogravimethic analysis and gas chromatography [6]. These are accurate, but require sample preparation and, as such, are too slow for automated recognition and sorting of textile materials needed for recycling. Hyperspectral near infrared imaging textile identification, for example detection of cotton in blend fabrics [7], analysing has material been used in identifying polyester content in blended textiles [11]
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