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Bacteriocin production optimization applying RSM and hybrid (ANN-GA) method for the indigenous culture of Pediococcus pentosaceus Sanna 14

Raje Siddiraju Upendra, Pratima Khandelwal, Mohammed Riyaz Ahmed.

Abstract
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 (4.08.0), agitation (120220 rpm), sucrose (2040 g/l), and peptone (520 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.

Key words: Keywords: Pediococcus pentosaceus, Bacteriocin, Response Surface Methodology design, Artificial neural network, Genetic Algorithms.






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The articles in Bibliomed are open access articles licensed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (https://creativecommons.org/licenses/by-nc-sa/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.