Rapid identification of fibers from different waste fabrics using the near-infrared spectroscopy technique

Fiber identification is the primary task of waste textile recycling, which plays an important guiding role in the recovery and reuse of waste textiles. In this study, 186 pure spinning textiles with different fiber species were chosen as the raw materials, the near-infrared spectra were collected and the differences among various fibers species were also studied. The fast and accurate classification/identification model of textile fiber was established using the near-infrared spectral modeling technique. The soft independent modeling of class analogy method was used to construct the model. The results show that the model recognition rate can be up to 97% after selecting the wavenumber range of 6800–5300 cm –1 with the first derivative treatment on the spectra. It was found by external validation that the prediction accuracy of the model was 100% for polyester, polyamide, acrylic, silk and wool. The prediction accuracy of cotton fiber and polyester fabric was higher than 90%. The above result demonstrated that the textile fiber identification model established in this study can be used for fast and accurate identification and sorting of waste textiles.