Modeling and optimization of Newfoundland shrimp waste hydrolysis for microbial growth using response surface methodology and artificial neural networks.

The hydrolyzed protein derived from seafood waste is regarded as a premium and low-cost nitrogen source for microbial growth. In this study, optimization of enzymatic shrimp waste hydrolyzing process was investigated. The degree of hydrolysis (DH) with four processing variables including enzyme/substrate ratio (E/S), hydrolysis time, initial pH value and temperature, were monitored. The DH values were used for response surface methodology (RSM) optimization through central composite design (CCD) and for training artificial neural network (ANN) to make a process prediction. Results indicated that the optimum levels of variables are: E/S ratio at 1.64%, hydrolysis time at 3.59h, initial pH at 9 and temperature at 52.57°C. Hydrocarbon-degrading bacteria Bacillus subtilis N3-1P was cultivated using different DHs of hydrolysate. The associated growth curves were generated. The research output facilitated effective shrimp waste utilization.