Abstract
This study employs Taguchi design of experiments (DOE) to optimize biosurfactant yield by analyzing the impact of various input parameters. Signal-to-noise ratio analysis is utilized for optimization, corroborated by ANOVA findings. Regression equations depicts response behaviour and are validated through a confirmation test. Taguchi methodology identifies optimal conditions for maximum biosurfactant yield: agitation (180 rpm), inoculum size (2%), beef extract (5 g/L), diesel (20 ml/L), peptone (5 g/L), NaCl (7 g/L), incubation time (4 days), pH (7.9), and yeast extract (6 g/L). This yields an 8.33% increase to 1.53 g/L, with initial optimum parameters projecting 1.41 g/L. ANOVA ranks and quantifies control factor contributions, revealing agitation's significant (34.12%) impact on yield. The study underscores the viability of Taguchi's optimal conditions for substantial yield improvement within specific ranges. The strong alignment between expected and experimental yields affirms the reliability of developed models for optimal yield selection. This study underscores the power of statistical techniques like Taguchi DOE and ANOVA in systematically enhancing biosurfactant production by Bacillus aryabhattai SPS1001 and paves the way for future advancements in bioprocess optimization.