Optimization of α-Amylase Synthesis by Bacillus velezensis: A Comparative Study of Taguchi Experimental Design and Box-Behnken Design for Enhanced Enzyme Production

This study explores the optimization of α-amylase synthesis using Bacillus velezensis, a vital enzyme with wideranging applications in industries such as pharmaceuticals, textiles, and biofuel production. Traditional optimization methods often rely on single-factor variations, which may not effectively address complex interactions among multiple parameters. To overcome this limitation, we employed a two-phase optimization approach that combines the Taguchi experimental design for initial screening of critical factors and the Box-Behnken Design (BBD) for refined optimization of the most influential parameters, creating a unified and systematic methodology for maximizing α-amylase production. In the first phase, the Taguchi experimental design was utilized to screen and evaluate thirteen significant parameters, including pH, temperature, agitation, inoculum size, aeration, carbon and nitrogen sources, and various nutrients. Using Taguchi’s orthogonal array L27 (313), we efficiently navigated this complex parameter space, identifying the most influential factors for α-amylase production. The initial α-amylase activity of 2.8 U/mL was significantly enhanced under optimized conditions, achieving a maximum α-amylase production of 1097.31 U/mL and a total protein of 1230 mg/mL. The optimal conditions identified were pH 5, temperature 34°C, 4% moong husk as the carbon source, and 2% soybean cake as the nitrogen source. Validation experiments confirmed a 31.2% enhancement in α-amylase production compared to unoptimized conditions, demonstrating the effectiveness of the Taguchi design in narrowing down critical factors. In the second phase, the BBD was employed to refine the optimization process by focusing on the four most influential factors identified in the Taguchi phase: pH, temperature, carbon source (moong husk), and nitrogen source (soybean cake). The BBD approach, using a 24-factorial design, allowed for a detailed exploration of the interactions between these factors and their optimal levels. Under refined conditions of pH 5, temperature 34°C, 4% moong husk, and 2% soybean cake, a peak α-amylase concentration of 1092.92 U/mL was achieved. The key process parameters were found to be statistically significant (P < 0.0001), and the BBD model effectively captured the interactions between these factors, leading to a substantial improvement in α-amylase activity. This integrated optimization approach – combining the Taguchi design for initial screening and the BBD for refined optimization – provides a comprehensive framework for maximizing α-amylase production. The Taguchi phase efficiently screened a wide range of parameters, whereas the BBD phase fine-tuned the optimization by focusing on the most critical factors and their interactions. This unified methodology not only highlights the potential of statistical optimization in enzyme production but also provides a robust framework for scaling up the process for industrial applications. The findings underscore the importance of utilizing agricultural residues, such as moong husk and soybean cake, for sustainable and cost-effective enzyme production.