The present study optimized the submerged fermentation conditions of Pediococcus pentosaceus Sanna 14 culture to improve bacteriocin yield by applying response surface methodology (RSM) and hybrid artificial neural network-genetic algorithm (ANN-GA). A full factorial central composite design (CCD) of RSM was applied to assess the effect of four principle variables, i.e., pH (4.0–8.0), agitation (120–220 rpm), sucrose (20–40 g/l), and peptone (5–20 g/l), on the yield of bacteriocin. The RSM optimized the experimental results of pH (7.0), agitation (200), sucrose (40 g/l), and peptone (20 g/l), and supported a higher yield (2.4 g/l) of bacteriocin and was validated applying ANN-GA methodology. The RSM bacteriocin yield (2.4 mg/l) was found to match with the ANN-predicted yield (2.4 mg/l). GA results confirmed the genetic fitness of the culture of P. pentosaceus Sanna 14 during fermentation. The present study registered a sixfold increase in bacteriocin yield (2.4 mg/l) compared to the yield (0.4 mg/l) of the unoptimized process conditions.
Keywords: Pediococcus pentosaceus, Bacteriocin, Response Surface Methodology design, Artificial neural network, Genetic Algorithms.