Riba Jordi-Roger, Cantero Rosa, Puig Rita
Electrical Engineering Department, Universitat Politècnica de Catalunya, Rambla Sant Nebridi 22, 08222 Terrassa, Spain.
Department of Computer Science and Industrial Engineering, Universitat de Lleida, Pla de la Massa 8, 08700 Igualada, Spain.
Polymers (Basel). 2022 Jul 29;14(15):3073. doi: 10.3390/polym14153073.
There is an urgent need to reuse and recycle textile fibers, since today, low recycling rates are achieved. Accurate classification methods for post-consumer textile waste are needed in the short term for a higher circularity in the textile and fashion industries. This paper compares different spectroscopic data from textile samples in order to correctly classify the textile samples. The accurate classification of textile waste results in higher recycling rates and a better quality of the recycled materials. The data fusion of near- and mid-infrared spectra is compared with single-spectrum information. The classification results show that data fusion is a better option, providing more accurate classification results, especially for difficult classification problems where the classes are wide and close to one another. The experimental results presented in this paper prove that the data fusion of near- and mid-infrared spectra is a good option for accurate textile-waste classification, since this approach allows the classification results to be significantly improved.
迫切需要对纺织纤维进行再利用和回收，因为目前的回收率很低。短期内需要准确的消费后纺织废料分类方法，以提高纺织和时尚行业的循环利用率。本文比较了来自纺织品样品的不同光谱数据，以便正确地对纺织品样品进行分类。对纺织废料进行准确分类可提高回收率，并提高回收材料的质量。将近红外光谱和中红外光谱的数据融合与单光谱信息进行了比较。分类结果表明，数据融合是一个更好的选择，能提供更准确的分类结果，特别是对于类别宽泛且相互接近的困难分类问题。本文给出的实验结果证明，近红外光谱和中红外光谱的数据融合是准确进行纺织废料分类的一个好选择，因为这种方法能显著提高分类结果。
