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...
Saved in:
| Published in | Biotechnology, biotechnological equipment Vol. 29; no. 6; pp. 1128 - 1134 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Sofia
Taylor & Francis
02.11.2015
Taylor & Francis Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1310-2818 1314-3530 1314-3530 |
| DOI | 10.1080/13102818.2015.1063970 |
Cover
| 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 |
| Author_xml | – sequence: 1 givenname: Yu surname: Yang fullname: Yang, Yu organization: College of Food Science, Shenyang Agricultural University – sequence: 2 givenname: Ming surname: Gao fullname: Gao, Ming organization: College of Bioscience and Technology, Shenyang Agricultural University – sequence: 3 givenname: Xiaodan surname: Yu fullname: Yu, Xiaodan organization: College of Bioscience and Technology, Shenyang Agricultural University – sequence: 4 givenname: Yunhe surname: Zhang fullname: Zhang, Yunhe organization: College of Bioscience and Technology, Shenyang Agricultural University – sequence: 5 givenname: Shuxia surname: Lyu fullname: Lyu, Shuxia email: lushuxia@hotmail.com organization: College of Bioscience and Technology, Shenyang Agricultural University |
| BookMark | eNqNkctu1TAQhiNUJNrCIyBZYsMmxbckjtiAjrhJlbqBteXjjFsXX4LtUB3egLfGOSksugBWM_r1_aOZf86akxADNM1zgi8IFvgVYQRTQcQFxaSrUs_GAT9qTqvOW9YxfHLscbtCT5qznG8xHjAmw2nz82ou1tsfqtgYUDTIw2QXj3T0c8z2qJqYULmLbS4wIwPJQyh_-O-2KG8D2qG9yjChqqpUrLHaKocCLOlYqj99ba-hdlYj5a5jsuXGowL6JthvC-SnzWOjXIZn9_W8-fL-3efdx_by6sOn3dvLVnMiSqunfsQCGAg18IlSMwHReyOGsRs6QXgHlBjNzQBEkD0mZiKTAK64EWwvxMDOm36bu4RZHe6Uc3JO1qt0kATLNVD5O1C5BirvA63Gl5txTnFduEhvswbnVIC4ZEk7yqnoR9ZX9MUD9DYuKdSzJKV07Dkn3Up1G6VTzDmB-e9FXj_wabs9pCRl3T_dbza3DfWxXtXHuEkWdXAxmaSCtlmyv4_4BWMhvhM |
| CitedBy_id | crossref_primary_10_1515_cppm_2016_0053 crossref_primary_10_3389_fbioe_2020_00194 crossref_primary_10_1007_s11694_019_00095_7 crossref_primary_10_3390_foods11233823 crossref_primary_10_3390_pr10081595 crossref_primary_10_1016_j_biotechadv_2018_07_006 crossref_primary_10_3390_app7080756 crossref_primary_10_1080_13102818_2017_1379359 crossref_primary_10_3390_fermentation9121000 crossref_primary_10_1016_j_sajce_2021_03_006 crossref_primary_10_3390_fermentation10030154 crossref_primary_10_1016_j_lwt_2023_114509 |
| 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 |
| ContentType | Journal Article |
| Copyright | 2015 The Author(s). Published by Taylor & Francis. 2015 2015 The Author(s). Published by Taylor & Francis. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| Copyright_xml | – notice: 2015 The Author(s). Published by Taylor & Francis. 2015 – notice: 2015 The Author(s). Published by Taylor & Francis. This work is licensed under the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. |
| DBID | 0YH AAYXX CITATION 3V. 7QO 7ST 7XB 8FD 8FE 8FG 8FH 8FK 8G5 ABJCF ABUWG AEUYN AFKRA AZQEC BBNVY BENPR BGLVJ BHPHI C1K CCPQU DWQXO FR3 GNUQQ GUQSH HCIFZ L6V LK8 M2O M7P M7S MBDVC P64 PHGZM PHGZT PKEHL PQEST PQGLB PQQKQ PQUKI PTHSS Q9U SOI 7S9 L.6 ADTOC UNPAY |
| DOI | 10.1080/13102818.2015.1063970 |
| DatabaseName | Taylor & Francis Open Access CrossRef ProQuest Central (Corporate) Biotechnology Research Abstracts Environment Abstracts ProQuest Central (purchase pre-March 2016) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Journals ProQuest Central (Alumni) (purchase pre-March 2016) Research Library (Alumni) Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest One Sustainability ProQuest Central ProQuest Central Essentials Biological Science Collection ProQuest Central Technology Collection Natural Science Collection Environmental Sciences and Pollution Management ProQuest One Community College ProQuest Central Engineering Research Database ProQuest Central Student ProQuest Research Library SciTech Premium Collection ProQuest Engineering Collection ProQuest Biological Science Collection Research Library Biological Science Database Engineering Database Research Library (Corporate) Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition Engineering Collection ProQuest Central Basic Environment Abstracts AGRICOLA AGRICOLA - Academic Unpaywall for CDI: Periodical Content Unpaywall |
| DatabaseTitle | CrossRef Research Library Prep ProQuest Central Student Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College Research Library (Alumni Edition) ProQuest Natural Science Collection Environmental Sciences and Pollution Management ProQuest Central ProQuest One Applied & Life Sciences ProQuest One Sustainability ProQuest Engineering Collection Biotechnology Research Abstracts Natural Science Collection ProQuest Central Korea Biological Science Collection ProQuest Research Library ProQuest Central (New) Engineering Collection Engineering Database ProQuest Biological Science Collection ProQuest Central Basic ProQuest One Academic Eastern Edition ProQuest Technology Collection Biological Science Database ProQuest SciTech Collection Biotechnology and BioEngineering Abstracts ProQuest One Academic UKI Edition Materials Science & Engineering Collection Engineering Research Database ProQuest One Academic Environment Abstracts ProQuest One Academic (New) ProQuest Central (Alumni) AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | AGRICOLA Research Library Prep |
| Database_xml | – sequence: 1 dbid: 0YH name: Taylor & Francis Open Access url: https://www.tandfonline.com sourceTypes: Publisher – sequence: 2 dbid: UNPAY name: Unpaywall url: https://proxy.k.utb.cz/login?url=https://unpaywall.org/ sourceTypes: Open Access Repository – sequence: 3 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 1314-3530 |
| EndPage | 1134 |
| ExternalDocumentID | 10.1080/13102818.2015.1063970 10_1080_13102818_2015_1063970 1063970 |
| Genre | Article |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: 31370077 funderid: 10.13039/501100001809 |
| GroupedDBID | 0YH 23N 4.4 5GY 5VS 8G5 AAHBH ABJCF ABUWG ACGFS ADBBV ADCVX AENEX AEUYN AFKRA AFRVT ALMA_UNASSIGNED_HOLDINGS AQTUD AZQEC BCNDV BENPR BGLVJ BPHCQ CCPQU DU5 DWQXO EBS EJD GNUQQ GROUPED_DOAJ GUQSH H13 HZ~ KQ8 M2O M4Z M7P M7S O9- OK1 P2P PHGZM PHGZT PQGLB PQQKQ PROAC PTHSS RDKPK TDBHL TFW TR2 AAYXX CITATION PUEGO 3V. 7QO 7ST 7XB 8FD 8FE 8FG 8FH 8FK BBNVY BHPHI C1K FR3 HCIFZ L6V LK8 MBDVC P64 PKEHL PQEST PQUKI Q9U SOI 7S9 L.6 ABDBF ACUHS ADTOC C1A IPNFZ MET RIG UNPAY |
| ID | FETCH-LOGICAL-c418t-cd6908e3e8a74d22fde1cbf8795758145e21fc4f7e181b01fd1d8e4a4f83b8873 |
| IEDL.DBID | UNPAY |
| ISSN | 1310-2818 1314-3530 |
| IngestDate | Sun Sep 07 11:28:32 EDT 2025 Fri Oct 03 00:08:50 EDT 2025 Sat Sep 06 07:30:42 EDT 2025 Wed Oct 01 02:06:44 EDT 2025 Thu Apr 24 23:05:23 EDT 2025 Mon Oct 20 23:31:23 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 6 |
| Language | English |
| License | open-access: http://creativecommons.org/licenses/by/4.0/: This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c418t-cd6908e3e8a74d22fde1cbf8795758145e21fc4f7e181b01fd1d8e4a4f83b8873 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://proxy.k.utb.cz/login?url=https://doi.org/10.1080/13102818.2015.1063970 |
| PQID | 2229644156 |
| PQPubID | 3933317 |
| PageCount | 7 |
| ParticipantIDs | proquest_journals_2229644156 proquest_miscellaneous_2524286936 crossref_primary_10_1080_13102818_2015_1063970 unpaywall_primary_10_1080_13102818_2015_1063970 informaworld_taylorfrancis_310_1080_13102818_2015_1063970 crossref_citationtrail_10_1080_13102818_2015_1063970 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2015-11-02 |
| PublicationDateYYYYMMDD | 2015-11-02 |
| PublicationDate_xml | – month: 11 year: 2015 text: 2015-11-02 day: 02 |
| PublicationDecade | 2010 |
| PublicationPlace | Sofia |
| PublicationPlace_xml | – name: Sofia |
| PublicationTitle | Biotechnology, biotechnological equipment |
| PublicationYear | 2015 |
| Publisher | Taylor & Francis Taylor & Francis Ltd |
| Publisher_xml | – name: Taylor & Francis – name: Taylor & Francis Ltd |
| 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 |
| SSID | ssj0070017 |
| Score | 2.0991375 |
| 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... |
| SourceID | unpaywall proquest crossref informaworld |
| SourceType | Open Access Repository Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 1128 |
| 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 |
| SummonAdditionalLinks | – databaseName: ProQuest Central dbid: BENPR link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV1RT9swED6x8rDtYQK2iTJAnrTX0DpxGvdhmgCBEBLdhEDiLbIdeyC1SRkpiDfe97h_yC_hznVK-wJ7iuTYSpQ73118d98H8E1JbZzPD1JZjchML1KJ7kboqVTCFXdC03nHyaB3dC6OL9KLJRg0vTBUVtnYRG-oi8rQGXmHeKfJd6e9H-PriFijKLvaUGioQK1QfPcQY29gOSZkrBYs7x0Mfp02tpmSrJ5uBYOaiHCQmp4e2e3QGA1RuVeKQ5Tx6i54qwUs04WI9O2kHKv7OzUczjmnwxX4EKJKtjtVg1VYsuUavJ_DGvwIf3-icRiFrktWOUZZ9cmIUU15KNxi-FxW31URSn7MHNrs0Jjk599e1Wp0VbJ9Rp6vYDhKajdFoGCEi-kvvqr88eEfKib1RzI1_I2fsb4csRlc7M0nOD88ONs_igITQ2QEl3VkCvyJljaxUmWiiGNXWG60I6Jy_N_gIrUxd0a4zGLAoLvcFbyQVijhZKLRjCWfoVVWpV0HlqjMmQx1gurjMBzpWymk1mls-jp1XLZBNF88NwGmnNgyhjkPaKaNoHISVB4E1Yad2bLxFKfjtQX9eXHmtT8gcVM2kzx5Ze1mI_s8bPmb_FlB2_B1dhs3K2VgVGmrCc5JMSKSvX6Cczoznfm_F954-aFf4B3N9m2S8Sa06j8Tu4XxUq23wyZ4Av_LD78 priority: 102 providerName: ProQuest – databaseName: Taylor & Francis Open Access dbid: 0YH link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Ra9swEBale-j2UNZuY1nTokJfvUSWHMuPpbSEQtuXBronI8nSFkjsUNsL_Qn717uT5ZA8lA72FOLoEpM73Z18331HyIWS2jhfH0RYjUjNJFJcjyOIVIozxZzQ-Lzj7n4ynYnbp6RHE9YBVolnaNcRRXhfjZtb6bpHxI0Yx6jIPDArgUtYm4JT-7s4ZRka9vjHtHfGWFX181VAJEKZvonnta_ZCU875KU7KehBW67Uy1otFlvR6OYjOQxpJL3s9H5E9mx5TD5skQt-In8ewBssQ5slrRzFMnq7pAgiD0gtCr9Lm3UVgapX1IGTDp1Ifv3veaOW85JeUQx1BYWraGcd5QRFIkz_4mHkEdghtkNStfhZPc-bX0u6YYetP5PZzfXj1TQKgxciI5hsIlPAmVlabqVKRRHHrrDMaIdzyeF4wURiY-aMcKmF_ECPmStYIa1QwkmuwWvxL2S_rEr7lVCuUmdSMAGEw0H2kVkppNZJbDKdOCYHRPT_d24CKzkOx1jkLJCX9mrKUU15UNOAfN-IrTpajrcEsm1l5o1_HuK64SU5f0N22Gs-Dzu8znEOOuaSyWRAzjcfw97EgosqbdXCmgQSIDnJOKwZbSzm327423_c8Al5j299y2Q8JPvNc2tPIXdq9JnfHX8BIXQMvA priority: 102 providerName: Taylor & Francis |
| Title | Optimization of medium composition for two-step fermentation of vitamin C based on artificial neural network-genetic algorithm techniques |
| URI | https://www.tandfonline.com/doi/abs/10.1080/13102818.2015.1063970 https://www.proquest.com/docview/2229644156 https://www.proquest.com/docview/2524286936 https://doi.org/10.1080/13102818.2015.1063970 |
| UnpaywallVersion | publishedVersion |
| Volume | 29 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAFT databaseName: Open Access Digital Library customDbUrl: eissn: 1314-3530 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0070017 issn: 1310-2818 databaseCode: KQ8 dateStart: 20140101 isFulltext: true titleUrlDefault: http://grweb.coalliance.org/oadl/oadl.html providerName: Colorado Alliance of Research Libraries – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 1314-3530 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0070017 issn: 1310-2818 databaseCode: DOA dateStart: 20140101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: http://www.proquest.com/pqcentral?accountid=15518 eissn: 1314-3530 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0070017 issn: 1310-2818 databaseCode: BENPR dateStart: 20110101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVAUI databaseName: Routledge Open customDbUrl: eissn: 1314-3530 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0070017 issn: 1310-2818 databaseCode: RDKPK dateStart: 19860101 isFulltext: true titleUrlDefault: http://www.tandfonline.com/page/openaccess/openjournals providerName: Routledge – providerCode: PRVAWR databaseName: Taylor & Francis Open Access customDbUrl: eissn: 1314-3530 dateEnd: 99991231 omitProxy: true ssIdentifier: ssj0070017 issn: 1310-2818 databaseCode: 0YH dateStart: 19940101 isFulltext: true titleUrlDefault: https://www.tandfonline.com providerName: Taylor & Francis |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3db9MwELegfQAe-EYURmUkXtPFsZO4j2NaqZAoE6LS9hTZjg0VbVJtyabxxDuP_If8JdwlTtUioY2XfDh2ZDuXu7Pv7neEvFFSG9fYB9GtRqQmCRTXYQCSSnGmmBMa9zs-zJLpXLw_iU98sDrGwuzY72W4zzhKQNY4YcVQhHYoWKH3kxhU7x7pz2fHB6fNogr4CVZsr0XAYx52ETv_es-OLNpBKt3RN-_UxVpdXarlckv0TB6QWdfp1uPk26iu9Mh8_wvP8cajekjueyWUHrRU84jcssVjcm8LmvAJ-fkReMnKB2nS0lE0wtcrii7o3s-LwkBodVkGQChr6oDF-zimpv7FolKrRUEPKQrKnEIpUmkLWEERRrM5NU7ov3_8AjrGcEqqll_Ks0X1dUU36LLnT8l8cvT5cBr4xA2BEUxWgclhzS0tt1KlIo8il1tmtMO85rA8YSK2EXNGuNSCfqFD5nKWSyuUcJJr4Hr8GekVZWGfE8pV6kwKJITudKC9jK0UUus4MmMdOyYHRHSfMDMe1RyTaywz5sFPu7nOcK4zP9cDMto0W7ewHtc1GG_TR1Y1-ymuTX6S8Wva7nXElHkOcZ5hHnXUReNkQF5vHsO_jQYbVdiyhjoxKFAyGXOos78hwpt1-MV_t3hJ7uJtE2gZ7ZFedVbbV6BxVXpIboenUzjKybsh6b89mh1_Gja7F0P_B_4BW2cfvA |
| linkProvider | Unpaywall |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1Pb9MwFLfGdhgc0PinFQYYCY6hdew0zmFCbGzq2FYQ2qTdgu3YMKlNCk2pduPOcd9nH4ZPwnuuU9oL47JTJMdOory_9nvv9wh5qaQ2zscHMa1GpKYbKa47EVgqxZliTmg87zjud3un4v1ZcrZCrppaGEyrbHSiV9RFZfCMvI19p9F2J903o28Rdo3C6GrTQkOF1grFtocYC4Udh_ZiClu48fbBO6D3qzje3zvZ7UWhy0BkBJN1ZArYIErLrVSpKOLYFZYZ7bAJN_jSTCQ2Zs4Il1owhrrDXMEKaYUSTnINIsrhubfImuAig83f2s5e_-OnxhZgUNe3dwEnKkLcpaaGSHbaOIZDmF6WwBBG2DpL1nEJO3XJA16flCN1MVWDwYIx3N8gd4MXS9_O2O4eWbHlfXJnAdvwAfn1AZTRMFR50spRjOJPhhRz2EOiGIX30npaRcBpI-rARoRCKD__x3mthucl3aVoaQsKo8jmM8QLijic_uKz2H__vARBwHpMqgZfgGz11yGdw9OOH5LTG6HJI7JaVqXdJJSr1JkUeBDz8cD9yawUUuskNplOHJMtIpo_npsAi47dOQY5C-ipDaFyJFQeCNUir-fLRjNckOsWZIvkzGt_IONm3VNyfs3arYb2eVAx4_yvQLTIi_ltUA4Y8VGlrSYwJwEPTHYzDnPac575vw9-_O-XPifrvZPjo_zooH_4hNzGlb5EM94iq_X3iX0KvlqtnwWBoOTzTcvgH93OTRU |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3db9MwELdQkWB7QHxsWtkAI_GatY6dxn1Eg6p8DR6oBE-W7dhQqU2qNmXan7D_eneOU7UPqJP2VCnxtVHvfHfO_e53hLzT0lgf6oMIqxG5HSSam34CkUpzppkXBt93fLscjCfi86-sRROuIqwSz9C-IYoIvho396LwLSKuxzhGRRaAWRlcwtoUnNofZhLCG5h0__e4dcZYVQ3zVUAkQZm2ied_X7MTnnbIS3dS0MfrcqGvr_RsthWNRk_Jk5hG0veN3p-RB658Tg63yAVfkJvv4A3msc2SVp5iGX09pwgij0gtCr9L66sqAVUvqAcnHTuRwvp_01rPpyW9oBjqCgpX0c4aygmKRJjhI8DIE7BDbIekevanWk7rv3O6YYddHZHJ6OPPi3ESBy8kVjBZJ7aAM7N03EmdiyJNfeGYNR7nksPxgonMpcxb4XMH-YHpM1-wQjqhhZfcgNfix6RTVqU7IZTr3NscTADhcKCeoZNCGpOldmgyz2SXiPb_VjaykuNwjJlikby0VZNCNamopi4534gtGlqOfQLDbWWqOrwP8c3wEsX3yJ61mldxh68UzkHHXDIbdMnbzW3Ym1hw0aWr1rAmgwRIDoYc1vQ2FnO3B355jwd-Qx79-DBSXz9dfjklB3gndE-mZ6RTL9fuFaRRtXkdNsot3bcPUQ |
| linkToUnpaywall | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NTtwwELaq5VB66B-tupRWRuo1yzq2E-8RIRCq1G0PrASnyHbssmI3WUFSVE7cOfYN-yTMJM5qF6mCnpI4dmQ745mxZ-YbQr5oZaxv7IPoViNSm0Sam2EEkkpzppkXBs87vo2T44n4eipPQ7A6xsKs2e_VcI9xlICsccKSUIR2KNihbyQSVO8e2ZiMf-yfNZsq4CdYsb0XEZd82EXs_Os7a7JoDal0Td98XhcL_ftaz2YroufoFRl3nW49Ti4GdWUG9uYBnuOTR_WavAxKKN1vqeYNeeaKt-TFCjThFrn7DrxkHoI0aekpGuHrOUUX9ODnRWEgtLouIyCUBfXA4kMcU1P_17TS82lBDygKypxCKVJpC1hBEUazuTRO6H9v_wAdYzgl1bOf5eW0Op_TJbrs1TsyOTo8OTiOQuKGyAqmqsjmsOdWjjulU5HHsc8ds8ZjXnPYnjAhXcy8FT51oF-YIfM5y5UTWnjFDXA9_p70irJwHwjlOvU2BRJCdzrQXkZOCWWMjO3ISM9Un4juF2Y2oJpjco1ZxgL4aTfXGc51Fua6TwbLZosW1uOxBqNV-siq5jzFt8lPMv5I252OmLLAIa4yzKOOuqhM-mR3-RrWNhpsdOHKGupIUKBUMuJQZ29JhE_r8PZ_t_hINvGxCbSMd0ivuqzdJ9C4KvM5rLN7Z_AbVQ |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Optimization+of+medium+composition+for+two-step+fermentation+of+vitamin+C+based+on+artificial+neural+network%E2%80%93genetic+algorithm+techniques&rft.jtitle=Biotechnology%2C+biotechnological+equipment&rft.au=Yang%2C+Yu&rft.au=Gao%2C+Ming&rft.au=Yu%2C+Xiaodan&rft.au=Zhang%2C+Yunhe&rft.date=2015-11-02&rft.issn=1314-3530&rft.volume=29&rft.issue=6+p.1128-1134&rft.spage=1128&rft.epage=1134&rft_id=info:doi/10.1080%2F13102818.2015.1063970&rft.externalDBID=NO_FULL_TEXT |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1310-2818&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1310-2818&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1310-2818&client=summon |