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<p>Chitosan properties are strongly governed by the degree of deacetylation (DDA) and molecular weight (Mw), which are inherently linked during alkaline deacetylation. This study presents a Pareto-based multi-objective optimization framework for simultaneously balancing DDA enhancement and Mw preservation in chitosan derived from Labeo rohita fish scales, an abundant and underutilized biopolymer source. Response surface methodology (RSM) models demonstrated strong predictive performance (DDA: R² = 0.9881, predicted R² = 0.8097; Mw: R² = 0.9873, predicted R² = 0.7968), enabling quantification of process-variable effects. Pareto-front analysis identified optimal conditions (51.79&#xa0;°C, 3.64&#xa0;h, 10.87% NaOH), yielding a DDA of 51.55% and an Mw of 159.17&#xa0;kDa, with high agreement between predicted and experimental values. Sequential deacetylation cycles further increased DDA to 76.02% while maintaining Mw above 80&#xa0;kDa. Structural characterization (FTIR, XRD, ¹H NMR, and solid-state ¹³C NMR) supports successful deacetylation and preservation of the polymer backbone. The optimized chitosan properties fall within literature-reported physicochemical ranges for various material applications. However, application-specific evaluation is beyond the scope of this study and is planned for future follow-up work. This study establishes a reproducible optimization strategy to control the DDA–Mw trade-off while promoting the sustainable utilization of fish-scale waste. </p>

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RSM–Pareto-Based Multi-Objective Optimization of the DDA–Mw Trade-Off in Chitosan Derived from Labeo rohita Fish Scales

  • 期刊论文

  • 发表日期：

    2026年5月29日

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  • Tangilal Dihan Chowdhury,
  • MD. Yasar Abdullah,
  • Md Enamul Hoque,
  • Husam Alrehaili

摘要

Chitosan properties are strongly governed by the degree of deacetylation (DDA) and molecular weight (Mw), which are inherently linked during alkaline deacetylation. This study presents a Pareto-based multi-objective optimization framework for simultaneously balancing DDA enhancement and Mw preservation in chitosan derived from Labeo rohita fish scales, an abundant and underutilized biopolymer source. Response surface methodology (RSM) models demonstrated strong predictive performance (DDA: R² = 0.9881, predicted R² = 0.8097; Mw: R² = 0.9873, predicted R² = 0.7968), enabling quantification of process-variable effects. Pareto-front analysis identified optimal conditions (51.79 °C, 3.64 h, 10.87% NaOH), yielding a DDA of 51.55% and an Mw of 159.17 kDa, with high agreement between predicted and experimental values. Sequential deacetylation cycles further increased DDA to 76.02% while maintaining Mw above 80 kDa. Structural characterization (FTIR, XRD, ¹H NMR, and solid-state ¹³C NMR) supports successful deacetylation and preservation of the polymer backbone. The optimized chitosan properties fall within literature-reported physicochemical ranges for various material applications. However, application-specific evaluation is beyond the scope of this study and is planned for future follow-up work. This study establishes a reproducible optimization strategy to control the DDA–Mw trade-off while promoting the sustainable utilization of fish-scale waste.