Chitin from seafood waste: particle swarm optimization and neural network study for the improved chitinase production
Nisha Suryawanshi, Satya Eswari
Abstract
Abstract BACKGROUND The current system in the processing of seafood leads to accumulation of many waste products, such as shells, tails, heads and bones. This seafood waste can be exploited for the extraction of chitin, with numerous applications in different fields. Seafood waste treatment can produce some valuable products. For the valorization of chitin, its degradation is an important step that can be achieved using the chitinase enzyme. Interestingly, chitin can also be used as a significant substrate for chitinase production. In this study, chitinase activity was enhanced by optimizing the fermentation medium, and chitin was used as the substrate. The polynomial model obtained by central composite design was employed in a particle swarm optimization algorithm and artificial neural network to optimize the final optimal concentration factors. The optimization results were compared for the better activity of chitinase. From the authors' best knowledge, the optimization of fermentation medium for chitinase production by particle swarm optimization was performed for the first time. RESULTS The highest activity optimized by particle swarm optimization and artificial neural network/ Bayesian regularization algorithm) was 115.8 and 124.78 U L –1 , respectively, with the optimized variables. CONCLUSION This study concluded that particle swarm optimization and artificial neural network are the best optimization methods for medium optimization. Among the multilayer feed‐forward algorithms in the artificial neural network, the Bayesian regularization algorithm was useful in optimizing medium components. © 2020 Society of Chemical Industry