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Open AccessArticle

Improved Pullulan Production and Process Optimization Using Novel GA–ANN and GA–ANFIS Hybrid Statistical Tools

1
Microbial Process Development Laboratory, University Institute of Engineering and Technology, Maharishi Dayanand University, Rohtak-124001, Haryana, India
2
Optimization and Mechatronics Laboratory, Department of Mechanical Engineering, University Institute of Engineering and Technology, Maharishi Dayanand University, Rohtak-124001, Haryana, India
3
Bioprocess Engineering Laboratory, Department of Biotechnology, Central University of Haryana, Mahendergarh-123031, Haryana, India
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Biomolecules 2020, 10(1), 124; https://doi.org/10.3390/biom10010124 (registering DOI)
Received: 19 December 2019 / Accepted: 8 January 2020 / Published: 10 January 2020
(This article belongs to the Special Issue Biology, Biotechnology and Bioprospecting of Microbial Biomolecules)
Pullulan production from Aureobasidium pullulans was explored to increase yield. Non-linear hybrid mathematical tools for optimization of process variables as well as the pullulan yield were analyzed. The one variable at a time (OVAT) approach was used to optimize the maximum pullulan yield of 35.16 ± 0.29 g/L. The tools predicted maximum pullulan yields of 39.4918 g/L (genetic algorithm coupled with artificial neural network (GA–ANN)) and 36.0788 g/L (GA coupled with adaptive network based fuzzy inference system (GA–ANFIS)). The best regression value (0.94799) of the Levenberg–Marquardt (LM) algorithm for ANN and the epoch error (6.1055 × 10−5) for GA–ANFIS point towards prediction precision and potentiality of data training models. The process parameters provided by both the tools corresponding to their predicted yield were revalidated by experiments. Among the two of them GA–ANFIS results were replicated with 98.82% accuracy. Thus GA–ANFIS predicted an optimum pullulan yield of 36.0788 g/L with a substrate concentration of 49.94 g/L, incubation period of 182.39 h, temperature of 27.41 °C, pH of 6.99, and agitation speed of 190.08 rpm. View Full-Text
Keywords: Pullulan; genetic algorithm; artificial neural network; fermentation Pullulan; genetic algorithm; artificial neural network; fermentation
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MDPI and ACS Style

Badhwar, P.; Kumar, A.; Yadav, A.; Kumar, P.; Siwach, R.; Chhabra, D.; Dubey, K.K. Improved Pullulan Production and Process Optimization Using Novel GA–ANN and GA–ANFIS Hybrid Statistical Tools. Biomolecules 2020, 10, 124.

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