Currently, the methods to identify leather materials have limitations, and identifying natural leather types is also relatively complex. In this research, the microstructures of four types of mammalian leathers (cattle leather, pig leather, sheep leather, and deer leather), three kinds of reptilian leathers (crocodile leather, lizard leather, and snake leather) and regenerated leather were characterized by scanning electron microscopy. The spectral curves (from 900 to 1700 nm) of these leather samples were extracted using a hyperspectral imaging system, and their spectral characteristics were analyzed. A method of leather identification by the hyperspectral imaging system combined with chemometrics was established. The results showed that the spectral curves of natural and regenerated leather differed in the number, position, and depth of the characteristic peaks, enabling the classification of regenerated leather by comparative analysis with the naked eye. The first-order derivative processing–principal component analysis–discriminant analysis model achieved a 98% correct classification rate, confirming the hyperspectral imaging system’s feasibility in the leather material identification field. We believe that his research is beneficial for the leather industry to understand the classifications scientifically, in order to protect consumer rights and further develop the leather testing industry.

Despite the enormous amounts of waste textiles produced by the world’s textile industry’s explosive growth, resource utilization rates are still poor. Cotton/polyester blended waste fabrics make up a sizable share, and sorting them precisely is essential to increasing recycling value and promoting the circular economy in the textile industry. Traditional mechanical and human sorting techniques are ineffective and inaccurate; current spectral analysis algorithms mainly concentrate on quantitative composition prediction and are insufficiently capable of differentiating between waste fabrics with comparable content gradients. To address these challenges, this paper proposes an improved 1DCNN model (Dual-1DCNN-Residual-SE) integrated with Near-Infrared (NIR) hyperspectral imaging technology. This model takes raw spectral data and Savitzky-Golay (SG) smoothing data as dual-channel inputs, introducing residual connections to capture subtle spectral differences between similar fabric categories, and employs SE attention mechanisms to adaptively enhance key features. Comparative experiments with four traditional algorithms—KNN, RF, SVM, and PLS—demonstrate that the proposed model achieves a classification accuracy of 95.94%, surpassing the best traditional algorithm SVM (88.12%) by 7.82%. Ablation experiments confirm each enhanced module’s efficacy. This study achieves high-precision classification of cotton/polyester blended waste fabrics, providing technical support for intelligent sorting of industrial waste fabrics.

In response to mounting resource and environmental pressures in the textile industry, this study investigates the feasibility of fiber-to-fiber closed-loop recycling for used firefighting protective clothing—a waste stream characterized by material homogeneity and large-scale disposal. Employing a mixed-methods approach combining stakeholder questionnaires, field investigations (n = 3650), and performance testing of retired aramid fabrics, this research systematically evaluates the technical, market, and systemic potential for circular regeneration. Results demonstrate strong multi-stakeholder support (over 89%) and significant consumer willingness to purchase recycled products (81.01–84% across categories), while material tests confirm the retained flame resistance and mechanical properties of the fabrics, enabling high-value applications. By constructing an integrated framework spanning technical, policy, market, and cultural dimensions, and proposing strategies of “targeted recycling” and “value reconstruction,” this work confirms the commercial viability and environmental benefit of recycling firefighting gear. It further offers a transferable model for advancing the circularity of other safety and protective textiles, with key innovations lying in its comprehensive full-chain assessment and the concurrent validation of stakeholder dynamics and material performance.