Modeling Embedded Optimization Strategy for the Formulation of Bacterial Lipase-Based Biodetergent

Abstract

Lipase-based detergent formulations are a viable substitute for chemical detergents that pose health and environmental hazards to customers and society. In this study, the efficacy of Staphylococcus arlettae JPBW-1 lipase as an additive in laundry detergent was assessed for oil removal through modeling and optimization using a response-surface-methodology-integrated genetic algorithm. A three-level five-factorial central composite design was used to evaluate the interactive effects on oil removal percentage from cotton fabric of process conditions, namely, detergent concentration, lipase concentration, buffer pH, washing temperature, and washing time. The input space of the validated response surface methodology (RSM) model (R2 value of 97.7%) was utilized for genetic algorithm (GA) optimization. An optimum value of 79.6% oil removal was achieved with the GA-predicted process variables of 0.69% detergent, 47.37 U of lipase, buffer pH of 7.2, and washing temperature of 37.18 °C in 26.11 min, which was 27% more than the oil removal without lipase. Hence, lipase from S. arlettae JPBW-1 can be effectively used as an additive in laundry detergent for oil removal from soiled fabric and introduces a new lipase into the biobased detergent industry.