TY - CONF

T1 - Characterisation of post-consumer textiles using near-infrared spectrometers

AU - Stipanovic, Hana

AU - Bäck, Tanja

AU - Tischberger-Aldrian, Alexia

PY - 2023/8

Y1 - 2023/8

N2 - Textile waste is one of the most rapidly growing waste streams. European households consume a large amount of textile products. According to European Commission every person buys 15 kg of textile per year. Yearly 5.8 million tons of textiles are thrown away, about 11.3 kg per person. As it is expected to have continuous growth of textile waste, European Commission has identified textiles as a "priority product for the circular economy" and that by 1st January 2025, Member States shall set up a separate collection for textiles. Most textile waste is incinerated, landfilled or exported to developing countries. In order to reach circular economy goals, it is required to higher the textile recycling rates. One of the most critical issues in textile recycling is the identification of fibre typologies constituting the waste. NIR technology is imposed as the solution to the recognition problem of textile waste materials. Using both benchtop and portable spectrometers have been analysed 466 (88 kg) samples of post-consumer textile waste. All the samples obtained the original labels, which were used as a reference for analyses. As referred to on the labels, the used textiles comprise different material compositions, a maximum of five fibres in one material. For further analysis, samples of pure fibers (e.g. 100% polyester) and composits of two different fiber materials (e.g. cotton/polyester) were used. The spectra were acquired in the wavelength of 991-1677 nm when analysed with a NIR benchtop spectrometer and in the wavelength of 1600-2400 nm when analysed with a handheld NIR spectrometer. Acquired spectra had to go through signal pre-treatment, where signal correction methods are used to remove effects on spectroscopic data, which could affect the performance of the chemometric analysis. The resulting spectra of both benchtop NIR and handheld NIR were derived and normalised. The spectra were further processed using chemometric tools like PCA and classification tools using MATLAB (R2022b) with chemometric toolboxes. The obtained results showed promising results when separating most of the samples. When analysing pure materials, differences and possible separation can be examined between all the materials apart from cotton and linen. In the case of material composites, it is observed that the correct material characterisation depends on the fibre content of specific material in the composition.

AB - Textile waste is one of the most rapidly growing waste streams. European households consume a large amount of textile products. According to European Commission every person buys 15 kg of textile per year. Yearly 5.8 million tons of textiles are thrown away, about 11.3 kg per person. As it is expected to have continuous growth of textile waste, European Commission has identified textiles as a "priority product for the circular economy" and that by 1st January 2025, Member States shall set up a separate collection for textiles. Most textile waste is incinerated, landfilled or exported to developing countries. In order to reach circular economy goals, it is required to higher the textile recycling rates. One of the most critical issues in textile recycling is the identification of fibre typologies constituting the waste. NIR technology is imposed as the solution to the recognition problem of textile waste materials. Using both benchtop and portable spectrometers have been analysed 466 (88 kg) samples of post-consumer textile waste. All the samples obtained the original labels, which were used as a reference for analyses. As referred to on the labels, the used textiles comprise different material compositions, a maximum of five fibres in one material. For further analysis, samples of pure fibers (e.g. 100% polyester) and composits of two different fiber materials (e.g. cotton/polyester) were used. The spectra were acquired in the wavelength of 991-1677 nm when analysed with a NIR benchtop spectrometer and in the wavelength of 1600-2400 nm when analysed with a handheld NIR spectrometer. Acquired spectra had to go through signal pre-treatment, where signal correction methods are used to remove effects on spectroscopic data, which could affect the performance of the chemometric analysis. The resulting spectra of both benchtop NIR and handheld NIR were derived and normalised. The spectra were further processed using chemometric tools like PCA and classification tools using MATLAB (R2022b) with chemometric toolboxes. The obtained results showed promising results when separating most of the samples. When analysing pure materials, differences and possible separation can be examined between all the materials apart from cotton and linen. In the case of material composites, it is observed that the correct material characterisation depends on the fibre content of specific material in the composition.

KW - NIR handheld

KW - Near-infrared spectroscopy

KW - Textiles

M3 - Poster

T2 - NIR 2023

Y2 - 20 August 2023 through 24 August 2023

ER -