The serious issue of textile waste accumulation has raised attention on biodegradability as a possible route to support sustainable consumption of textile fibers. However, synthetic textile fibers that dominate the market, especially poly(ethylene terephthalate) (PET), resist biological degradation, creating environmental and waste management challenges. Because pure natural fibers, like cotton, both perform well for consumer textiles and generally meet certain standardized biodegradability criteria, inspiration from the mechanisms involved in natural biodegradability are leading to new discoveries and developments in biologically accelerated textile waste remediation for both natural and synthetic fibers. The objective of this review is to present a multidisciplinary perspective on the essential bio-chemo-physical requirements for textile materials to undergo biodegradation, taking into consideration the impact of environmental or waste management process conditions on biodegradability outcomes. Strategies and recent progress in enhancing synthetic textile fiber biodegradability are reviewed, with emphasis on performance and biodegradability behavior of poly(lactic acid) (PLA) as an alternative biobased, biodegradable apparel textile fiber, and on biological strategies for addressing PET waste, including industrial enzymatic hydrolysis to generate recyclable monomers. Notably, while pure PET fibers do not biodegrade within the timeline of any standardized conditions, recent developments with process intensification and engineered enzymes show that higher enzymatic recycling efficiency for PET polymer has been achieved compared to cellulosic materials. Furthermore, combined with alternative waste management practices, such as composting, anaerobic digestion and biocatalyzed industrial reprocessing, the development of synthetic/natural fiber blends and other strategies are creating opportunities for new biodegradable and recyclable textile fibers. Article Highlights: Poly(lactic acid) (PLA) leads other synthetic textile fibers in meeting both performance and biodegradation criteria. Recent research with poly(ethylene terephthalate) (PET) polymer shows potential for efficient enzyme catalyzed industrial recycling. Synthetic/natural fiber blends and other strategies could open opportunities for new biodegradable and recyclable textile fibers.

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.

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.