Optimization of medium composition for two-step fermentation of vitamin C based on artificial neural network-genetic algorithm techniques

The production of 2-keto-L-gulonic acid (2-KGA) during the conversion from L-sorbose to 2-KGA in the two-step fermentation of vitamin C can be improved by using an efficient companion strain Bacillus subtilis A9 to facilitate the growth of Ketogulonicigenium vulgare and the production of 2-KGA. Two...

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Published inBiotechnology, biotechnological equipment Vol. 29; no. 6; pp. 1128 - 1134
Main Authors Yang, Yu, Gao, Ming, Yu, Xiaodan, Zhang, Yunhe, Lyu, Shuxia
Format Journal Article
LanguageEnglish
Published Sofia Taylor & Francis 02.11.2015
Taylor & Francis Ltd
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ISSN1310-2818
1314-3530
1314-3530
DOI10.1080/13102818.2015.1063970

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Abstract The production of 2-keto-L-gulonic acid (2-KGA) during the conversion from L-sorbose to 2-KGA in the two-step fermentation of vitamin C can be improved by using an efficient companion strain Bacillus subtilis A9 to facilitate the growth of Ketogulonicigenium vulgare and the production of 2-KGA. Two optimization models, namely response surface methodology (RSM) and artificial neural network (ANN), were built to optimize the medium components for mixed-culture fermentation of 2-KGA. The root mean square error, R 2 and the standard error of prediction given by the ANN model were 0.13%, 0.99% and 0.21%, respectively, while the RSM model gave 1.89%, 0.84% and 2.9%, respectively. This indicated that the fitness and the prediction accuracy of the ANN model were higher than those of the RSM model. Furthermore, using genetic algorithm (GA), the input space of the ANN model was optimized, predicting that the maximum 2-KGA production of 72.54 g·L −1 would be obtained at the GA-optimized concentrations of the medium components (L-sorbose, 92.5 g·L −1 ; urea, 10.2 g·L −1 ; corn steep liquor, 16 g·L −1 ; CaCO 3 , 3.96 g·L −1 ; MgSO 4 , 0.28 g·L −1 ). The 2-KGA production experimentally obtained using the ANN-GA-designed medium was 71.21 ± 1.53 g·L −1 , which was in good agreement with the predicted value. The same optimization process may be used to improve the production during bacterial mixed-cultures fermentation by changing the fermentation parameters.
AbstractList The production of 2-keto-L-gulonic acid (2-KGA) during the conversion from L-sorbose to 2-KGA in the two-step fermentation of vitamin C can be improved by using an efficient companion strain Bacillus subtilis A9 to facilitate the growth of Ketogulonicigenium vulgare and the production of 2-KGA. Two optimization models, namely response surface methodology (RSM) and artificial neural network (ANN), were built to optimize the medium components for mixed-culture fermentation of 2-KGA. The root mean square error, R² and the standard error of prediction given by the ANN model were 0.13%, 0.99% and 0.21%, respectively, while the RSM model gave 1.89%, 0.84% and 2.9%, respectively. This indicated that the fitness and the prediction accuracy of the ANN model were higher than those of the RSM model. Furthermore, using genetic algorithm (GA), the input space of the ANN model was optimized, predicting that the maximum 2-KGA production of 72.54 g·L⁻¹ would be obtained at the GA-optimized concentrations of the medium components (L-sorbose, 92.5 g·L⁻¹; urea, 10.2 g·L⁻¹; corn steep liquor, 16 g·L⁻¹; CaCO₃, 3.96 g·L⁻¹; MgSO₄, 0.28 g·L⁻¹). The 2-KGA production experimentally obtained using the ANN–GA-designed medium was 71.21 ± 1.53 g·L⁻¹, which was in good agreement with the predicted value. The same optimization process may be used to improve the production during bacterial mixed-cultures fermentation by changing the fermentation parameters.
The production of 2-keto-L-gulonic acid (2-KGA) during the conversion from L-sorbose to 2-KGA in the two-step fermentation of vitamin C can be improved by using an efficient companion strain Bacillus subtilis A9 to facilitate the growth of Ketogulonicigenium vulgare and the production of 2-KGA. Two optimization models, namely response surface methodology (RSM) and artificial neural network (ANN), were built to optimize the medium components for mixed-culture fermentation of 2-KGA. The root mean square error, R 2 and the standard error of prediction given by the ANN model were 0.13%, 0.99% and 0.21%, respectively, while the RSM model gave 1.89%, 0.84% and 2.9%, respectively. This indicated that the fitness and the prediction accuracy of the ANN model were higher than those of the RSM model. Furthermore, using genetic algorithm (GA), the input space of the ANN model was optimized, predicting that the maximum 2-KGA production of 72.54 g·L −1 would be obtained at the GA-optimized concentrations of the medium components (L-sorbose, 92.5 g·L −1 ; urea, 10.2 g·L −1 ; corn steep liquor, 16 g·L −1 ; CaCO 3 , 3.96 g·L −1 ; MgSO 4 , 0.28 g·L −1 ). The 2-KGA production experimentally obtained using the ANN-GA-designed medium was 71.21 ± 1.53 g·L −1 , which was in good agreement with the predicted value. The same optimization process may be used to improve the production during bacterial mixed-cultures fermentation by changing the fermentation parameters.
The production of 2-keto-L-gulonic acid (2-KGA) during the conversion from L-sorbose to 2-KGA in the two-step fermentation of vitamin C can be improved by using an efficient companion strain Bacillus subtilis A9 to facilitate the growth of Ketogulonicigenium vulgare and the production of 2-KGA. Two optimization models, namely response surface methodology (RSM) and artificial neural network (ANN), were built to optimize the medium components for mixed-culture fermentation of 2-KGA. The root mean square error, R2 and the standard error of prediction given by the ANN model were 0.13%, 0.99% and 0.21%, respectively, while the RSM model gave 1.89%, 0.84% and 2.9%, respectively. This indicated that the fitness and the prediction accuracy of the ANN model were higher than those of the RSM model. Furthermore, using genetic algorithm (GA), the input space of the ANN model was optimized, predicting that the maximum 2-KGA production of 72.54 g·L−1 would be obtained at the GA-optimized concentrations of the medium components (L-sorbose, 92.5 g·L−1; urea, 10.2 g·L−1; corn steep liquor, 16 g·L−1; CaCO3, 3.96 g·L−1; MgSO4, 0.28 g·L−1). The 2-KGA production experimentally obtained using the ANN–GA-designed medium was 71.21 ± 1.53 g·L−1, which was in good agreement with the predicted value. The same optimization process may be used to improve the production during bacterial mixed-cultures fermentation by changing the fermentation parameters.
Author Gao, Ming
Yang, Yu
Zhang, Yunhe
Lyu, Shuxia
Yu, Xiaodan
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Cites_doi 10.1007/s00253-008-1822-6
10.1016/j.jfoodeng.2005.11.025
10.1016/j.biortech.2008.03.038
10.1016/j.jbiotec.2014.04.027
10.1128/AEM.72.5.3367-3374.2006
10.1016/j.procbio.2009.11.016
10.1016/j.jbiotec.2013.01.019
10.1007/s11306-011-0392-2
10.1128/AEM.05123-11
10.1016/j.jbiotec.2013.10.027
10.1016/j.procbio.2012.05.010
10.1016/j.jfoodeng.2005.11.024
10.1016/j.carbpol.2010.04.029
10.3109/1040841X.2012.706250
10.1016/j.jbiotec.2012.05.015
10.1016/j.biortech.2006.03.012
10.1007/s00253-008-1828-0
10.1016/S0958-1669(02)00288-4
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References cit0011
cit0012
cit0010
Lyu SX (cit0021) 2014; 16
Zhang J (cit0001) 2008; 27
cit0019
cit0017
cit0018
cit0015
cit0016
cit0013
Gao M (cit0023) 2012; 14
cit0020
Jiang YY (cit0022) 1997; 13
Fang YY (cit0024) 2009; 4
Feng S (cit0004) 1998; 18
cit0008
cit0009
cit0006
Li Y (cit0014) 2002; 22
cit0007
Ai BL (cit0003) 2013; 15
cit0026
cit0005
cit0002
cit0025
References_xml – volume: 27
  start-page: 1
  issue: 5
  year: 2008
  ident: cit0001
  publication-title: J Food Sci Biotechnol
– ident: cit0013
  doi: 10.1007/s00253-008-1822-6
– ident: cit0017
  doi: 10.1016/j.jfoodeng.2005.11.025
– ident: cit0018
  doi: 10.1016/j.biortech.2008.03.038
– ident: cit0006
  doi: 10.1016/j.jbiotec.2014.04.027
– ident: cit0026
  doi: 10.1128/AEM.72.5.3367-3374.2006
– volume: 13
  start-page: 400
  issue: 4
  year: 1997
  ident: cit0022
  publication-title: Chin J Biotechnol
– ident: cit0002
  doi: 10.1016/j.procbio.2009.11.016
– ident: cit0005
  doi: 10.1016/j.jbiotec.2013.01.019
– ident: cit0011
  doi: 10.1007/s11306-011-0392-2
– ident: cit0007
  doi: 10.1128/AEM.05123-11
– ident: cit0010
  doi: 10.1016/j.jbiotec.2013.10.027
– ident: cit0012
  doi: 10.1016/j.procbio.2012.05.010
– ident: cit0015
  doi: 10.1016/j.jfoodeng.2005.11.024
– ident: cit0025
  doi: 10.1016/j.carbpol.2010.04.029
– volume: 22
  start-page: 26
  issue: 2
  year: 2002
  ident: cit0014
  publication-title: J Microbiol
– ident: cit0009
  doi: 10.3109/1040841X.2012.706250
– volume: 18
  start-page: 6
  issue: 1
  year: 1998
  ident: cit0004
  publication-title: Chin J Microbiol
– ident: cit0008
  doi: 10.1016/j.jbiotec.2012.05.015
– ident: cit0016
  doi: 10.1016/j.biortech.2006.03.012
– volume: 4
  start-page: 168
  year: 2009
  ident: cit0024
  publication-title: Sci Tech Food Ind
– ident: cit0019
  doi: 10.1007/s00253-008-1828-0
– volume: 16
  start-page: 1135
  issue: 6
  year: 2014
  ident: cit0021
  publication-title: Int J Agric Bio.
– volume: 14
  start-page: 235
  issue: 33
  year: 2012
  ident: cit0023
  publication-title: Sci Tech Food Ind
– volume: 15
  start-page: 1075
  issue: 6
  year: 2013
  ident: cit0003
  publication-title: Int J Agric Biol
– ident: cit0020
  doi: 10.1016/S0958-1669(02)00288-4
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Snippet The production of 2-keto-L-gulonic acid (2-KGA) during the conversion from L-sorbose to 2-KGA in the two-step fermentation of vitamin C can be improved by...
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SubjectTerms 2-Keto-L-gulonic acid
Algorithms
artificial neural network
Artificial neural networks
Ascorbic acid
B. subtilis A9
Bacillus subtilis
biotechnology
Calcium carbonate
corn steep liquor
Fermentation
genetic algorithm
Genetic algorithms
Ketogulonicigenium vulgare
medium optimization
mixed culture
Model accuracy
Neural networks
Optimization
prediction
Response surface methodology
Sorbose
Standard error
Urea
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Title Optimization of medium composition for two-step fermentation of vitamin C based on artificial neural network-genetic algorithm techniques
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