Rising volumes of textile waste necessitate the development of more efficient recycling systems, with a primary focus on the optimization of sorting technologies. Near-infrared (NIR) spectroscopy is a state-of-the-art method for fiber identification; however, its accuracy in quantifying textile blends, particularly common polyester/cotton blend textiles, still requires refinement. This study explores the potential and limitations of NIR spectroscopy for quantifying cotton content in post-consumer textiles. A lab-scale NIR sorter and a handheld NIR spectrometer in complementary wavelength ranges were applied to a diverse range of post-consumer textile samples to test model accuracies. Results show that the commonly assumed 10% accuracy threshold in industrial sorting can be exceeded, especially when excluding textiles with <35% cotton content. Identifying and excluding the range of non-linearity significantly improved the model’s performance. The final models achieved an RMSEP of 6.6% and bias of −0.9% for the NIR sorter and an RMSEP of 3.1% and bias of −0.6% for the handheld NIR spectrometer. This study also assessed how textile characteristics—such as color, structure, product type, and alkaline treatment—affect spectral behavior and model accuracy, highlighting their importance for refining quantification when high-purity inputs are needed. By identifying current limitations and potential sources of errors, this study provides a foundation for improving NIR-based models.

The rapid growth of the textile industry, driven by fast fashion trends, has significantly increased textile waste, particularly blends of polyester (PET) and cotton fibers, and efficient recycling of blended textiles requires effective fiber separation methods, yet current strategies face substantial limitations. This study investigates the performance of commercially available cellulase formulations for enzymatic depolymerization of cotton and suggests an optimized activity pattern for novel reactor systems for recycling of PET/cotton blends. Thirty-five cellulase formulations intended for biopolishing, stonewashing, or biomass degradation were extensively characterized through biochemical assays (protein quantification, reducing sugar content, SDS-PAGE, and enzyme-specific activity assays). The formulations were further categorized according to their physical state, optimal pH and temperature class, and use case (biopolishing, stonewashing, and degradation), and trends were observed between the categories. Ten formulations were selected for further practical evaluation in a controlled reactor system to determine cotton removal efficacy from a blended PET/cotton fabric based on cotton weight loss. Enzyme activity assays did not correlate directly with reactor performance, suggesting that conventional cellulase assays may inadequately predict real-world separation efficiency. Protein efficiency was introduced as a critical metric for evaluating enzyme efficacy and economic viability. Here this variable was defined as a specific activity, relating the fiber separation efficiency in the reactor to the protein content of the formulation to indicate how effective the proteins were at separation. Formulations exhibiting balanced high performance in both weight loss and protein efficiency predominantly contained high proportions of endoglucanase activity, supported by moderate cellobiohydrolase and minor beta-glucosidase activities.

Accurate quantification of cotton content is a key prerequisite for efficient textile recycling. However, it remains challenging due to material heterogeneity and technical limitations. Near-infrared spectroscopy (NIR) combined with advanced data analysis offers a rapid, non-destructive approach. However, systematic evaluations across instrument classes and analysis strategies for industrial textile sorting remain limited. In this study, a unique set of cotton/polyester blends from the same starting material with varying cotton content was analyzed using three NIR systems representing laboratory, handheld, and industrial sensor-based applications. Multiple spectral preprocessing strategies were systematically combined with partial least squares regression and advanced machine learning models. Model performance was evaluated using cross-validation and independent test sets. The benchtop NIR system delivered the highest and most consistent performance, achieving RMSEP values below 1.0% with advanced regression models. The handheld and imaging sensor system exhibited higher RMSEP values (1.2–1.6%), reflecting not only differences in preprocessing and model selection, but also intrinsic instrumental limitations. Overall, the results demonstrate that each NIR instrument class exhibits distinct strengths and limitations with respect to accuracy, sensitivity, and robustness. Consequently, instrument-specific preprocessing, models, and hyperparameters are required, and no universally transferable pipeline was identified.