# Opportunities and Limitations in Recycling Fossil Polymers from Textiles
## 2 Sorting of Textiles: Techniques and Facilities
### The Advent of Near-Infrared Spectroscopy in Textile Sorting  
Near-infrared spectroscopy (NIR) has gained a lot of popularity during the last decades because of its in-line and in-field capabilities (i.e., it performs real-time and non-destructive measurements). Therefore, a wide range of applications have been reported in the literature, such as in food science, agriculture, medicine, and the petrochemical industry, among others [75]. NIR spectroscopy (as FT-MIR spectroscopy) studies the energy transitions (i.e., absorptions) yielded by the interaction of the light (in this case the NIR region of 800 nm to 2500 nm) between vibrational energy states that are specific to every molecule, so a distinctive pattern can be obtained that can be thought of as a unique fingerprint (NIR spectroscopy is considered a fingerprinting technology). The NIR region displays broad bands with overlapping wavelengths that mainly provide information about NH, OH, and CH bonds, making this technique an optimal tool for the study of organic compounds [76, 77]. Extensive literature can be found regarding the applications of NIR spectroscopy in textile sorting. In most of them, a portable NIR spectrometer working in diffuse reflectance mode is used and combined with powerful pattern recognition algorithms to sort and classify different textile materials (such as polyester, cotton, wool and polyamide, among others), reporting outstanding classification rates [78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88]. Such spectrometers offer the possibility of in-line implementation, but they rely on measurements performed on a single point of a sample, which could lead to misleading results when spectra are acquired from complex heterogeneous samples such as textile waste [79]. Hyperspectral imaging (HSI), which has been used for land observation remote sensing in satellites since the late 1960s, can provide both spectral and spatial information of the sample being tested; thus, heterogeneous samples are good candidates for this technology [80]. A hyperspectral image (also called hypercube) is a three-dimensional array that gathers two-dimensional spatial information (m \(\times\) n pixels) and one-dimensional spectral information (spectra of n wavelengths; Figure 6). In other words, for each pixel a full spectrum is collected (spectral/chemical information) while for each m \(\times\) n-plane an intensity map of a certain wavelength is obtained (spatial information) [81]. Successful results have also been reported on the use of HSI in the NIR range for textile applications. [PERSON] and [PERSON] [82] obtained classification results over 90% when using a SWIR HSI system for the identification of foreign matter in lint samples. Results on the classification of synthetic fibres (such as polyamide, polypropylene, etc.) using a SWIR HSI system and multivariate classification algorithms were reported by [PERSON] et al. [83] when using a low-cost mosaic based Visible-NIR (VNIR) HSI system. The VNIR range brings some advantages, since it offers higher spatial resolution, cheaper and more compact cameras than the traditional SWIR Range. Moreover, the VNIR range enables sorting of blue denim, a relevant component of textile waste, that could otherwise be difficult to distinguish by SWIR HSI systems [83]. The workflow process for an industrial throughput for conventional NIRS and HSI technologies at an industrial level is similar. Target waste is placed onto a conveyor belt in which the monitoring systems are placed (i.e., cameras, spectrometers, lights; usually above the conveyor) so spectral acquisitions can be acquired from the waste stream. Spectral measurements are then processed with chemometrics/machine learning models implemented into the monitoring system, providing the identification of the textiles that will be physically separated using sorting equipment.