Improvement of a two-stage fermentation process for docosahexaenoic acid production by Aurantiochytrium limacinum SR21 applying statistical experimental designs and data analysis

Statistical screening experimental designs were applied to identify the significant culture variables for biomass production of Aurantiochytrium limacinum SR21 and their optimal levels were found using a combination of Artificial Neural Networks, genetic algorithms and graphical analysis. The biomas...

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Detalles Bibliográficos
Autores principales:	Rosa, S.M., Soria, M.A., Vélez, C.G., Galvagno, M.A.
Formato:	JOUR
Materias:	Artificial neural networks Aurantiochytrium Docosahexaenoic acid Statistical designs Two-stage fermentation Deep neural networks Fermentation Genetic algorithms Neural networks Statistics Biomass productions Inoculum productions Statistical design Statistical experimental design Statistical screening Two-stage fermentations Unsaturated fatty acids carbon docosahexaenoic acid nitrogen artificial neural network biomass bioreactor eukaryote fermentation genetic algorithm graphical method optimization article Aurantiochytrium limacinum data analysis microbial growth microorganism nonhuman priority journal statistical analysis biotechnology culture medium growth, development and aging metabolism methodology microbiology physiology reproducibility statistical model statistics Biomass Bioreactors Biotechnology Culture Media Docosahexaenoic Acids Eukaryota Models, Statistical Reproducibility of Results Statistics as Topic
Acceso en línea:	http://hdl.handle.net/20.500.12110/paper_09608524_v101_n7_p2367_Rosa
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

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spelling	todo:paper_09608524_v101_n7_p2367_Rosa2023-10-03T15:53:54Z Improvement of a two-stage fermentation process for docosahexaenoic acid production by Aurantiochytrium limacinum SR21 applying statistical experimental designs and data analysis Rosa, S.M. Soria, M.A. Vélez, C.G. Galvagno, M.A. Artificial neural networks Aurantiochytrium Docosahexaenoic acid Statistical designs Two-stage fermentation Deep neural networks Fermentation Genetic algorithms Neural networks Statistics Aurantiochytrium Biomass productions Docosahexaenoic acid Inoculum productions Statistical design Statistical experimental design Statistical screening Two-stage fermentations Unsaturated fatty acids carbon docosahexaenoic acid nitrogen artificial neural network biomass bioreactor eukaryote fermentation genetic algorithm graphical method optimization article artificial neural network Aurantiochytrium limacinum biomass bioreactor data analysis fermentation microbial growth microorganism nonhuman priority journal statistical analysis biotechnology culture medium eukaryote growth, development and aging metabolism methodology microbiology physiology reproducibility statistical model statistics Biomass Bioreactors Biotechnology Culture Media Docosahexaenoic Acids Eukaryota Fermentation Models, Statistical Reproducibility of Results Statistics as Topic Statistical screening experimental designs were applied to identify the significant culture variables for biomass production of Aurantiochytrium limacinum SR21 and their optimal levels were found using a combination of Artificial Neural Networks, genetic algorithms and graphical analysis. The biomass value obtained (40.3 g cell dry weight l-1) employing the selected culture conditions agreed with that predicted by the model. Subsequently, two significant culture conditions for docosahexaenoic acid (DHA) production were determined, finding that an inoculum of 10% (v/v), obtained from the previous (statistically optimized) stage, should be used in a DHA production medium having a molar C:N ratio of 55:1, to reach a production of 7.8 g DHA l-1 d-1. The production step was thereafter scaled in a 3.5 l bioreactor, and DHA productivity of 3.7 g l-1 d-1 was obtained. This two-stage strategy: statistically optimized inoculum production (fist step) and a DHA production step, is presented for the first time to optimize a bioprocess conducive to the obtention of microbial DHA. © 2009 Elsevier Ltd. All rights reserved. Fil:Rosa, S.M. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Soria, M.A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Vélez, C.G. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Galvagno, M.A. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. JOUR info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_09608524_v101_n7_p2367_Rosa
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	Artificial neural networks Aurantiochytrium Docosahexaenoic acid Statistical designs Two-stage fermentation Deep neural networks Fermentation Genetic algorithms Neural networks Statistics Aurantiochytrium Biomass productions Docosahexaenoic acid Inoculum productions Statistical design Statistical experimental design Statistical screening Two-stage fermentations Unsaturated fatty acids carbon docosahexaenoic acid nitrogen artificial neural network biomass bioreactor eukaryote fermentation genetic algorithm graphical method optimization article artificial neural network Aurantiochytrium limacinum biomass bioreactor data analysis fermentation microbial growth microorganism nonhuman priority journal statistical analysis biotechnology culture medium eukaryote growth, development and aging metabolism methodology microbiology physiology reproducibility statistical model statistics Biomass Bioreactors Biotechnology Culture Media Docosahexaenoic Acids Eukaryota Fermentation Models, Statistical Reproducibility of Results Statistics as Topic
spellingShingle	Artificial neural networks Aurantiochytrium Docosahexaenoic acid Statistical designs Two-stage fermentation Deep neural networks Fermentation Genetic algorithms Neural networks Statistics Aurantiochytrium Biomass productions Docosahexaenoic acid Inoculum productions Statistical design Statistical experimental design Statistical screening Two-stage fermentations Unsaturated fatty acids carbon docosahexaenoic acid nitrogen artificial neural network biomass bioreactor eukaryote fermentation genetic algorithm graphical method optimization article artificial neural network Aurantiochytrium limacinum biomass bioreactor data analysis fermentation microbial growth microorganism nonhuman priority journal statistical analysis biotechnology culture medium eukaryote growth, development and aging metabolism methodology microbiology physiology reproducibility statistical model statistics Biomass Bioreactors Biotechnology Culture Media Docosahexaenoic Acids Eukaryota Fermentation Models, Statistical Reproducibility of Results Statistics as Topic Rosa, S.M. Soria, M.A. Vélez, C.G. Galvagno, M.A. Improvement of a two-stage fermentation process for docosahexaenoic acid production by Aurantiochytrium limacinum SR21 applying statistical experimental designs and data analysis
topic_facet	Artificial neural networks Aurantiochytrium Docosahexaenoic acid Statistical designs Two-stage fermentation Deep neural networks Fermentation Genetic algorithms Neural networks Statistics Aurantiochytrium Biomass productions Docosahexaenoic acid Inoculum productions Statistical design Statistical experimental design Statistical screening Two-stage fermentations Unsaturated fatty acids carbon docosahexaenoic acid nitrogen artificial neural network biomass bioreactor eukaryote fermentation genetic algorithm graphical method optimization article artificial neural network Aurantiochytrium limacinum biomass bioreactor data analysis fermentation microbial growth microorganism nonhuman priority journal statistical analysis biotechnology culture medium eukaryote growth, development and aging metabolism methodology microbiology physiology reproducibility statistical model statistics Biomass Bioreactors Biotechnology Culture Media Docosahexaenoic Acids Eukaryota Fermentation Models, Statistical Reproducibility of Results Statistics as Topic
description	Statistical screening experimental designs were applied to identify the significant culture variables for biomass production of Aurantiochytrium limacinum SR21 and their optimal levels were found using a combination of Artificial Neural Networks, genetic algorithms and graphical analysis. The biomass value obtained (40.3 g cell dry weight l-1) employing the selected culture conditions agreed with that predicted by the model. Subsequently, two significant culture conditions for docosahexaenoic acid (DHA) production were determined, finding that an inoculum of 10% (v/v), obtained from the previous (statistically optimized) stage, should be used in a DHA production medium having a molar C:N ratio of 55:1, to reach a production of 7.8 g DHA l-1 d-1. The production step was thereafter scaled in a 3.5 l bioreactor, and DHA productivity of 3.7 g l-1 d-1 was obtained. This two-stage strategy: statistically optimized inoculum production (fist step) and a DHA production step, is presented for the first time to optimize a bioprocess conducive to the obtention of microbial DHA. © 2009 Elsevier Ltd. All rights reserved.
format	JOUR
author	Rosa, S.M. Soria, M.A. Vélez, C.G. Galvagno, M.A.
author_facet	Rosa, S.M. Soria, M.A. Vélez, C.G. Galvagno, M.A.
author_sort	Rosa, S.M.
title	Improvement of a two-stage fermentation process for docosahexaenoic acid production by Aurantiochytrium limacinum SR21 applying statistical experimental designs and data analysis
title_short	Improvement of a two-stage fermentation process for docosahexaenoic acid production by Aurantiochytrium limacinum SR21 applying statistical experimental designs and data analysis
title_full	Improvement of a two-stage fermentation process for docosahexaenoic acid production by Aurantiochytrium limacinum SR21 applying statistical experimental designs and data analysis
title_fullStr	Improvement of a two-stage fermentation process for docosahexaenoic acid production by Aurantiochytrium limacinum SR21 applying statistical experimental designs and data analysis
title_full_unstemmed	Improvement of a two-stage fermentation process for docosahexaenoic acid production by Aurantiochytrium limacinum SR21 applying statistical experimental designs and data analysis
title_sort	improvement of a two-stage fermentation process for docosahexaenoic acid production by aurantiochytrium limacinum sr21 applying statistical experimental designs and data analysis
url	http://hdl.handle.net/20.500.12110/paper_09608524_v101_n7_p2367_Rosa
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_version_	1807318335816204288

Improvement of a two-stage fermentation process for docosahexaenoic acid production by Aurantiochytrium limacinum SR21 applying statistical experimental designs and data analysis

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