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

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...

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Autores principales: Badhwar, Parul, Kumar, Ashwani, Yadav, Ankush, Kumar, Punit, Siwach, Ritu, Chhabra, Deepak, Dubey, Kashyap Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7022329/
https://www.ncbi.nlm.nih.gov/pubmed/31936881
http://dx.doi.org/10.3390/biom10010124
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author Badhwar, Parul
Kumar, Ashwani
Yadav, Ankush
Kumar, Punit
Siwach, Ritu
Chhabra, Deepak
Dubey, Kashyap Kumar
author_facet Badhwar, Parul
Kumar, Ashwani
Yadav, Ankush
Kumar, Punit
Siwach, Ritu
Chhabra, Deepak
Dubey, Kashyap Kumar
author_sort Badhwar, Parul
collection PubMed
description 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.
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spelling pubmed-70223292020-03-09 Improved Pullulan Production and Process Optimization Using Novel GA–ANN and GA–ANFIS Hybrid Statistical Tools Badhwar, Parul Kumar, Ashwani Yadav, Ankush Kumar, Punit Siwach, Ritu Chhabra, Deepak Dubey, Kashyap Kumar Biomolecules Article 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. MDPI 2020-01-10 /pmc/articles/PMC7022329/ /pubmed/31936881 http://dx.doi.org/10.3390/biom10010124 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Badhwar, Parul
Kumar, Ashwani
Yadav, Ankush
Kumar, Punit
Siwach, Ritu
Chhabra, Deepak
Dubey, Kashyap Kumar
Improved Pullulan Production and Process Optimization Using Novel GA–ANN and GA–ANFIS Hybrid Statistical Tools
title Improved Pullulan Production and Process Optimization Using Novel GA–ANN and GA–ANFIS Hybrid Statistical Tools
title_full Improved Pullulan Production and Process Optimization Using Novel GA–ANN and GA–ANFIS Hybrid Statistical Tools
title_fullStr Improved Pullulan Production and Process Optimization Using Novel GA–ANN and GA–ANFIS Hybrid Statistical Tools
title_full_unstemmed Improved Pullulan Production and Process Optimization Using Novel GA–ANN and GA–ANFIS Hybrid Statistical Tools
title_short Improved Pullulan Production and Process Optimization Using Novel GA–ANN and GA–ANFIS Hybrid Statistical Tools
title_sort improved pullulan production and process optimization using novel ga–ann and ga–anfis hybrid statistical tools
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7022329/
https://www.ncbi.nlm.nih.gov/pubmed/31936881
http://dx.doi.org/10.3390/biom10010124
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