Statistical Optimization and Assessment of L-Asparaginase
Production via Submerged Fermentation: A Potential Strategy
L-asparaginase has gained recognition in anti-cancer therapies
and acrylamide reduction in food processing. However, the
adverse effects of some microbial sources have driven the
exploration of alternative production methods. This study
concentrates on optimizing L-asparaginase production from a
fungal species. Optimization was conducted using Design Expert
9.0 software, adjusting variables such as pH, temperature, and
concentrations of carbon and nitrogen sources. The Central
Composite Design (CCD) was implemented to assess the
influence of these factors on enzyme production. The findings
revealed that CCD accurately modeled the data, particularly
within a quadratic framework, proving its effectiveness for
optimization. Further statistical validation, including ANOVA and
regression analysis, confirmed the robustness of CCD in
analyzing the complex interplay among the parameters. Overall
the results underscore CCD as a highly efficient approach for
enhancing L-asparaginase production from fungal sources,
offering promising avenues for sustainable and effective
strategies in medical and food industry applications. This
research advances efforts to leverage microbial enzymes while
overcoming the challenges tied to traditional sources.