Statistical Optimization of Pullulanase Production by Aspergillus flavus under Solid-State Fermentation Using Wheat Bran

Background: Pullulanase is an industrial starch-debranching enzyme whose production cost may be reduced through fungal solid-state fermentation on agro-industrial residues. This study evaluated initial pH, moisture content, wheat-bran quantity, and inoculum volume for pullulanase production by Aspergillus flavus. Methods: A four-factor, five-level central composite design containing 30 runs was constructed. Pullulanase activity was defined as units per gram of dry substrate, and a second-order polynomial model was used to estimate linear, interaction, and quadratic effects. Results: Measured pullulanase activity ranged from 12.4 to 46.9 U/gds across the 30 design runs. A reduced quadratic model retaining the significant linear and pure-quadratic terms was statistically significant (F8,21 = 11.42, p < 0.0001), with R² = 0.813, adjusted R² = 0.742, and adequate precision of 11.3. The model also showed a significant lack of fit relative to pure replicate error at the six centre points (F = 471.6, p < 0.0001), indicating that the fitted quadratic surface does not fully capture the response. Moisture content produced the strongest positive linear effect, followed by inoculum volume, initial pH, and substrate quantity; all pure-quadratic coefficients were negative. Numerical optimization within the tested region predicted a maximum of 41.5 U/gds at pH 5.85, 69.1% moisture, 8.87 g wheat bran, and 6.09 mL inoculum, but the single highest measured activity (46.9 U/gds) occurred at the highest-level factorial combination tested, suggesting the true optimum may lie at or beyond the upper boundary of the studied ranges. Conclusion: Moisture content, inoculum volume, and initial pH significantly influenced pullulanase production by A. flavus under solid-state fermentation, but the significant lack of fit and the corner-biased response indicate that the present central composite design has not yet bracketed the true process optimum. A follow-up design extending toward higher pH, moisture, substrate, and inoculum levels, together with confirmatory strain identification and aflatoxin-safety screening, is recommended before the process can be considered optimized.