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

Mostrar todas las versiones(2)

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...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Rosa, Silvina Mariana, Soria, Marcelo Abel, Vélez, Carlos Guillermo, Galvagno, Miguel Angel
Publicado:	2010
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:	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09608524_v101_n7_p2367_Rosa 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

id	paper:paper_09608524_v101_n7_p2367_Rosa
record_format	dspace
spelling	paper:paper_09608524_v101_n7_p2367_Rosa2023-06-08T15:57:45Z Improvement of a two-stage fermentation process for docosahexaenoic acid production by Aurantiochytrium limacinum SR21 applying statistical experimental designs and data analysis Rosa, Silvina Mariana Soria, Marcelo Abel Vélez, Carlos Guillermo Galvagno, Miguel Angel 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. 2010 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09608524_v101_n7_p2367_Rosa 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, Silvina Mariana Soria, Marcelo Abel Vélez, Carlos Guillermo Galvagno, Miguel Angel 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.
author	Rosa, Silvina Mariana Soria, Marcelo Abel Vélez, Carlos Guillermo Galvagno, Miguel Angel
author_facet	Rosa, Silvina Mariana Soria, Marcelo Abel Vélez, Carlos Guillermo Galvagno, Miguel Angel
author_sort	Rosa, Silvina Mariana
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
publishDate	2010
url	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_09608524_v101_n7_p2367_Rosa http://hdl.handle.net/20.500.12110/paper_09608524_v101_n7_p2367_Rosa
work_keys_str_mv	AT rosasilvinamariana improvementofatwostagefermentationprocessfordocosahexaenoicacidproductionbyaurantiochytriumlimacinumsr21applyingstatisticalexperimentaldesignsanddataanalysis AT soriamarceloabel improvementofatwostagefermentationprocessfordocosahexaenoicacidproductionbyaurantiochytriumlimacinumsr21applyingstatisticalexperimentaldesignsanddataanalysis AT velezcarlosguillermo improvementofatwostagefermentationprocessfordocosahexaenoicacidproductionbyaurantiochytriumlimacinumsr21applyingstatisticalexperimentaldesignsanddataanalysis AT galvagnomiguelangel improvementofatwostagefermentationprocessfordocosahexaenoicacidproductionbyaurantiochytriumlimacinumsr21applyingstatisticalexperimentaldesignsanddataanalysis
_version_	1768541997286555648

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

Ejemplares similares