Optimization of Fermentation Process Parameters for Ginsenoside Re Bioconversion by Plackett-Burman and Box-Benhnken Design
In this work, Plackett-Burman (PB) and Box-Behnken (BB) experiment design were used to investigate the medium components of fermentation forth of ginsenoside Re bioconversion to ginsenoside Rh1. Firstly, nine variables including cultivation time, temperature, pH, reactant concentration, inoculum’s s...
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2018-01-01
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Series: | MATEC Web of Conferences |
Online Access: | https://doi.org/10.1051/matecconf/201823804001 |
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doaj-d3a26d55e71f46b482b4606a88a257f62021-04-02T14:09:34ZengEDP SciencesMATEC Web of Conferences2261-236X2018-01-012380400110.1051/matecconf/201823804001matecconf_icnfm2018_04001Optimization of Fermentation Process Parameters for Ginsenoside Re Bioconversion by Plackett-Burman and Box-Benhnken DesignWu XiuhongZhang LinaSui AilinQu BoquWang ShaoyanIn this work, Plackett-Burman (PB) and Box-Behnken (BB) experiment design were used to investigate the medium components of fermentation forth of ginsenoside Re bioconversion to ginsenoside Rh1. Firstly, nine variables including cultivation time, temperature, pH, reactant concentration, inoculum’s size, peptone, MgSO4*7H2O, K2HPO4 and NaNO3 were screened. The most important effective factors were estimated according to the statistical regression. Subsequently, the steepest ascent experiment was used to approach the optimum response range for each effective factor. Then Box-Benhnken Design (BBD) was finally used to analyze the response surface of the screened factors for further optimization. The optimized conditions for the influential factors were: ginsenoside Re 1.0 g/L, pH 4.5 and 7 days. The bioconversion of ginsenoside Re to Rh1 could reach up to 51.76 %.https://doi.org/10.1051/matecconf/201823804001 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Wu Xiuhong Zhang Lina Sui Ailin Qu Boqu Wang Shaoyan |
spellingShingle |
Wu Xiuhong Zhang Lina Sui Ailin Qu Boqu Wang Shaoyan Optimization of Fermentation Process Parameters for Ginsenoside Re Bioconversion by Plackett-Burman and Box-Benhnken Design MATEC Web of Conferences |
author_facet |
Wu Xiuhong Zhang Lina Sui Ailin Qu Boqu Wang Shaoyan |
author_sort |
Wu Xiuhong |
title |
Optimization of Fermentation Process Parameters for Ginsenoside Re Bioconversion by Plackett-Burman and Box-Benhnken Design |
title_short |
Optimization of Fermentation Process Parameters for Ginsenoside Re Bioconversion by Plackett-Burman and Box-Benhnken Design |
title_full |
Optimization of Fermentation Process Parameters for Ginsenoside Re Bioconversion by Plackett-Burman and Box-Benhnken Design |
title_fullStr |
Optimization of Fermentation Process Parameters for Ginsenoside Re Bioconversion by Plackett-Burman and Box-Benhnken Design |
title_full_unstemmed |
Optimization of Fermentation Process Parameters for Ginsenoside Re Bioconversion by Plackett-Burman and Box-Benhnken Design |
title_sort |
optimization of fermentation process parameters for ginsenoside re bioconversion by plackett-burman and box-benhnken design |
publisher |
EDP Sciences |
series |
MATEC Web of Conferences |
issn |
2261-236X |
publishDate |
2018-01-01 |
description |
In this work, Plackett-Burman (PB) and Box-Behnken (BB) experiment design were used to investigate the medium components of fermentation forth of ginsenoside Re bioconversion to ginsenoside Rh1. Firstly, nine variables including cultivation time, temperature, pH, reactant concentration, inoculum’s size, peptone, MgSO4*7H2O, K2HPO4 and NaNO3 were screened. The most important effective factors were estimated according to the statistical regression. Subsequently, the steepest ascent experiment was used to approach the optimum response range for each effective factor. Then Box-Benhnken Design (BBD) was finally used to analyze the response surface of the screened factors for further optimization. The optimized conditions for the influential factors were: ginsenoside Re 1.0 g/L, pH 4.5 and 7 days. The bioconversion of ginsenoside Re to Rh1 could reach up to 51.76 %. |
url |
https://doi.org/10.1051/matecconf/201823804001 |
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