Development and Optimization of Near-infrared spectroscopy

Abstract: With the growing demand for sustainable options, the existing sorting capacities are limiting the potential for fiber-to-fiber recycling. With the help of near-infrared spectroscopy (NIRS), automated sorting of textiles with high accuracy is possible due to the easy access for polymer identification. Despite the effectiveness of NIRS, some limitations of the process still limit its full potential. Possible disruptors may interfere with and disturb the identification of polymer identities and compositions in different ways. In the following thesis, additives, treatments, and other environmental factors that may hinder fiber identification are further acknowledged. The key results of the thesis state that stains and factors due to wear and tear are the most common possible disruptors that could be identified from pre-sorted post-consumer end-of-life textiles. Further on, stains of ketchup, deodorant, and oil affect the polymer recognition by lowering the recognized fiber content. Water-repellent coatings on 100 % polyamide woven fabric were not detected correctly according to the NIR scanner, as the stated polymer composition was >90 %. Even though some investigated factors, e.g., material structures, were correctly identified by the NIR scanner, the internal deviation of the knitted polyester structure indicates that porous and loose structures hold the ability to interfere with the detection of polymers. To what extent the operating software has been developed is highly relevant to the outcome of how accurate textile sorting may be.