A composition-based model for rapid prediction of pineapple leaf fibers fineness and tensile strength

Pineapple leaf fibers (PALFs) are sustainable resources with exceptional tenacity, yet their component-structure-property relationships remain underexplored, limiting high-value applications. This study establishes quantitative links between chemical composition (cellulose, hemicellulose, lignin) and mechanical properties of single PALFs, aiming to develop a predictive model for rapid fineness and strength assessment. Using stepwise chemical degumming, we generated 11 distinct fiber groups (SSD1–11) from Queen PALF and characterized > 600 fibers via standardized mechanical testing (GB/T5881–2024). Pearson and Mantel correlation analysis revealed a hierarchical component-function framework: cellulose governs PALF stiffness via crystalline microfibrils; hemicellulose modulates fineness and interfacial adhesion as bonding network; lignin enhances stretchability and strength via stress-transfer structure. Critically, we developed a computational model, termed Prediction of Fineness and Strength of Single PALF (PFS-PALF) for rapid assessment, which was experimentally validated to achieve 95 % similarity versus national standard measurements. This approach has potential to replace the conventional labor-intensive and tedious measurements on PALF’s mechanical behavior. In addition, PFS-PALF enables reverse regulation of PALFs’ composition through optimized degumming parameters and facilitates rapid selection of suitable PALF variety to meet application-specific mechanical requirements. • Established a composition – property framework to evaluate the mechanical behavior of pineapple leaf fibers (PALF). • The roles of key compositions of PALF were elucidated. • The composition-based model predicts PALF fineness and tensile strength with 95 % accuracy. • This work enables inverse optimization of degumming conditions for high-strength PALF composites or flexible textiles.