Response surface methodology (RSM) and artificial neural network (ANN) integrated optimization for lipase production by Bacillus holotolerans

Abstract

Response surface methodology (RSM) and artificial neural networks (ANN) are considered the most efficient way for optimization and modeling studies to design and develop various biosimilars. The primary objective of this study was to create empirical modeling and optimization of media parameters for producing B. halotolerans VSH 09 lipase using RSM and ANN. One-factor-at-a-time (OFAT) analysis revealed that triacylglycerols hydrolyzed by lipase manifest substantial activity. The subsequent screening for best carbon, nitrogen, and inducer was performed using the Placket–Burman design (PBD). The statistically significant variables were further examined for their optimum level using Box–Behnken design (BBD). The lipase production was optimized (26.04 IU/ml) under the ideal molasses (2.5%), peptone (2%), and salt (0.1% CaCO₃, 0.1% (NH₄)₂SO_4, and 0.1% MgSO₄.7H₂O). Both models revealed impeccable predictions; however, more interestingly, it was evaluated that ANN outperforms the RSM regarding data fitting and estimation capabilities.

Keywords

Lipase / B. halotolerans / OFAT / Placket–Burman design (PBD) / Box–Behnken design (BBD) / Response surface methodology / Artificial neural network

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