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Artificial intelligence approach for high level production of amylase using Rhizopus microsporus var. oligosporus and different agro‐industrial wastes
BACKGROUND Culture medium is a key element to be defined when biotechnologies are chosen for agro‐industrial wastes reutilization. This work aimed at definition of culture medium composition using four agro‐industrial wastes (wheat bran, type II wheat flour, soybean meal and sugarcane bagasse) in so...
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| Published in: | Journal of chemical technology and biotechnology (1986) 2017-03, Vol.92 (3), p.684-692 |
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| Main Authors: | , , , , , , |
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| Language: | English |
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| cited_by | cdi_FETCH-LOGICAL-c3664-c054f8932d5f5d4567a3fc735d80e7cfd5c75782f207ef61e47db72667b8ddd33 |
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| container_end_page | 692 |
| container_issue | 3 |
| container_start_page | 684 |
| container_title | Journal of chemical technology and biotechnology (1986) |
| container_volume | 92 |
| creator | Fernández Núñez, Eutimio Gustavo Barchi, Augusto Cesar Ito, Shuri Escaramboni, Bruna Herculano, Rondinelli Donizetti Mayer, Cassia Roberta Malacrida de Oliva Neto, Pedro |
| description | BACKGROUND
Culture medium is a key element to be defined when biotechnologies are chosen for agro‐industrial wastes reutilization. This work aimed at definition of culture medium composition using four agro‐industrial wastes (wheat bran, type II wheat flour, soybean meal and sugarcane bagasse) in solid‐state fermentation (SSF) of Rhizopus oligosporus, for high‐level production of amylases through approaches based on artificial intelligence (AI) or response surface methodologies (RSM). First, substrates were individually assessed. Then, I‐optimal mixture experimental designs were performed to determine the influence of two sets of ternary agro‐industrial waste mixtures on amylase and specific amylase activities.
RESULTS
The best individual substrate for amylases production was wheat bran (392.5 U g−1). As a rule, no significant interactions among substrates affecting amylase activities were observed for ternary systems and the approaches under consideration. A significant exception was the amylolytic activity for mixtures composed of wheat bran (91% w/w) and soybean meal (9% w/w). This finding was confirmed analytically by a combination of artificial neural network (ANN) and genetic algorithm (GA). The AI approach improved modelling quality with respect to RSM for production of fungal amylases in SSF.
CONCLUSION
The I‐optimal design in conjunction with ANN‐GA is suggested to optimize accurately culture medium to maximize amylase production by SSF. © 2016 Society of Chemical Industry |
| doi_str_mv | 10.1002/jctb.5054 |
| format | article |
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Culture medium is a key element to be defined when biotechnologies are chosen for agro‐industrial wastes reutilization. This work aimed at definition of culture medium composition using four agro‐industrial wastes (wheat bran, type II wheat flour, soybean meal and sugarcane bagasse) in solid‐state fermentation (SSF) of Rhizopus oligosporus, for high‐level production of amylases through approaches based on artificial intelligence (AI) or response surface methodologies (RSM). First, substrates were individually assessed. Then, I‐optimal mixture experimental designs were performed to determine the influence of two sets of ternary agro‐industrial waste mixtures on amylase and specific amylase activities.
RESULTS
The best individual substrate for amylases production was wheat bran (392.5 U g−1). As a rule, no significant interactions among substrates affecting amylase activities were observed for ternary systems and the approaches under consideration. A significant exception was the amylolytic activity for mixtures composed of wheat bran (91% w/w) and soybean meal (9% w/w). This finding was confirmed analytically by a combination of artificial neural network (ANN) and genetic algorithm (GA). The AI approach improved modelling quality with respect to RSM for production of fungal amylases in SSF.
CONCLUSION
The I‐optimal design in conjunction with ANN‐GA is suggested to optimize accurately culture medium to maximize amylase production by SSF. © 2016 Society of Chemical Industry</description><identifier>ISSN: 0268-2575</identifier><identifier>EISSN: 1097-4660</identifier><identifier>DOI: 10.1002/jctb.5054</identifier><language>eng</language><publisher>Chichester, UK: John Wiley & Sons, Ltd</publisher><subject>Amylase ; Artificial intelligence ; Culture ; Design analysis ; enzymes ; experimental design ; Genetic algorithms ; industrial microbiology ; mathematical modelling ; solid state fermentation ; Substrates ; waste treatment and waste minimization ; Wastes ; Wheat</subject><ispartof>Journal of chemical technology and biotechnology (1986), 2017-03, Vol.92 (3), p.684-692</ispartof><rights>2016 Society of Chemical Industry</rights><rights>Copyright © 2017 Society of Chemical Industry</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3664-c054f8932d5f5d4567a3fc735d80e7cfd5c75782f207ef61e47db72667b8ddd33</citedby></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fjctb.5054$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fjctb.5054$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>315,786,790,27957,27958,50923,51032</link.rule.ids></links><search><creatorcontrib>Fernández Núñez, Eutimio Gustavo</creatorcontrib><creatorcontrib>Barchi, Augusto Cesar</creatorcontrib><creatorcontrib>Ito, Shuri</creatorcontrib><creatorcontrib>Escaramboni, Bruna</creatorcontrib><creatorcontrib>Herculano, Rondinelli Donizetti</creatorcontrib><creatorcontrib>Mayer, Cassia Roberta Malacrida</creatorcontrib><creatorcontrib>de Oliva Neto, Pedro</creatorcontrib><title>Artificial intelligence approach for high level production of amylase using Rhizopus microsporus var. oligosporus and different agro‐industrial wastes</title><title>Journal of chemical technology and biotechnology (1986)</title><description>BACKGROUND
Culture medium is a key element to be defined when biotechnologies are chosen for agro‐industrial wastes reutilization. This work aimed at definition of culture medium composition using four agro‐industrial wastes (wheat bran, type II wheat flour, soybean meal and sugarcane bagasse) in solid‐state fermentation (SSF) of Rhizopus oligosporus, for high‐level production of amylases through approaches based on artificial intelligence (AI) or response surface methodologies (RSM). First, substrates were individually assessed. Then, I‐optimal mixture experimental designs were performed to determine the influence of two sets of ternary agro‐industrial waste mixtures on amylase and specific amylase activities.
RESULTS
The best individual substrate for amylases production was wheat bran (392.5 U g−1). As a rule, no significant interactions among substrates affecting amylase activities were observed for ternary systems and the approaches under consideration. A significant exception was the amylolytic activity for mixtures composed of wheat bran (91% w/w) and soybean meal (9% w/w). This finding was confirmed analytically by a combination of artificial neural network (ANN) and genetic algorithm (GA). The AI approach improved modelling quality with respect to RSM for production of fungal amylases in SSF.
CONCLUSION
The I‐optimal design in conjunction with ANN‐GA is suggested to optimize accurately culture medium to maximize amylase production by SSF. © 2016 Society of Chemical Industry</description><subject>Amylase</subject><subject>Artificial intelligence</subject><subject>Culture</subject><subject>Design analysis</subject><subject>enzymes</subject><subject>experimental design</subject><subject>Genetic algorithms</subject><subject>industrial microbiology</subject><subject>mathematical modelling</subject><subject>solid state fermentation</subject><subject>Substrates</subject><subject>waste treatment and waste minimization</subject><subject>Wastes</subject><subject>Wheat</subject><issn>0268-2575</issn><issn>1097-4660</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><recordid>eNqNkbtqHDEUhoWxIetLkTcQuEkza90ll87iKwsBY9eDVpdZLbPSRJrxsqnyCCnzfH4Sz-CkceXq_Bw-fs5_fgC-YjTHCJGLjelXc444OwAzjC5lxYRAh2CGiFAV4ZJ_AcelbBBCQhExA3-vch98MEG3MMTetW1oXDQO6q7LSZs19CnDdWjWsHUvroXj1g6mDynC5KHe7ltdHBxKiA18XIdfqRsK3AaTU-lSHvWLznOYRtv_Cx0ttMF7l13soW5yev39J0Q7lD5PZ-x06V05BUdet8Wd_Zsn4Pnm-mlxVy1_3N4vrpaVoUKwyoxRvbqkxHLPLeNCauqNpNwq5KTxlhvJpSKeIOm8wI5Ju5JECLlS1lpKT8C3d98x2M_Blb7ehmLGP-jo0lBqrBTDlGKMPoEKRTFickLPP6CbNOQ4BpkoJggnjIzUxTu1C63b110OW533NUb11GU9dVlPXdYPi6fvk6BvLDGYOA</recordid><startdate>201703</startdate><enddate>201703</enddate><creator>Fernández Núñez, Eutimio Gustavo</creator><creator>Barchi, Augusto Cesar</creator><creator>Ito, Shuri</creator><creator>Escaramboni, Bruna</creator><creator>Herculano, Rondinelli Donizetti</creator><creator>Mayer, Cassia Roberta Malacrida</creator><creator>de Oliva Neto, Pedro</creator><general>John Wiley & Sons, Ltd</general><general>Wiley Subscription Services, Inc</general><scope>7QF</scope><scope>7QO</scope><scope>7QQ</scope><scope>7QR</scope><scope>7SC</scope><scope>7SE</scope><scope>7SP</scope><scope>7SR</scope><scope>7T7</scope><scope>7TA</scope><scope>7TB</scope><scope>7U5</scope><scope>8BQ</scope><scope>8FD</scope><scope>C1K</scope><scope>F28</scope><scope>FR3</scope><scope>H8D</scope><scope>H8G</scope><scope>JG9</scope><scope>JQ2</scope><scope>KR7</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope></search><sort><creationdate>201703</creationdate><title>Artificial intelligence approach for high level production of amylase using Rhizopus microsporus var. oligosporus and different agro‐industrial wastes</title><author>Fernández Núñez, Eutimio Gustavo ; Barchi, Augusto Cesar ; Ito, Shuri ; Escaramboni, Bruna ; Herculano, Rondinelli Donizetti ; Mayer, Cassia Roberta Malacrida ; de Oliva Neto, Pedro</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3664-c054f8932d5f5d4567a3fc735d80e7cfd5c75782f207ef61e47db72667b8ddd33</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Amylase</topic><topic>Artificial intelligence</topic><topic>Culture</topic><topic>Design analysis</topic><topic>enzymes</topic><topic>experimental design</topic><topic>Genetic algorithms</topic><topic>industrial microbiology</topic><topic>mathematical modelling</topic><topic>solid state fermentation</topic><topic>Substrates</topic><topic>waste treatment and waste minimization</topic><topic>Wastes</topic><topic>Wheat</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Fernández Núñez, Eutimio Gustavo</creatorcontrib><creatorcontrib>Barchi, Augusto Cesar</creatorcontrib><creatorcontrib>Ito, Shuri</creatorcontrib><creatorcontrib>Escaramboni, Bruna</creatorcontrib><creatorcontrib>Herculano, Rondinelli Donizetti</creatorcontrib><creatorcontrib>Mayer, Cassia Roberta Malacrida</creatorcontrib><creatorcontrib>de Oliva Neto, Pedro</creatorcontrib><collection>Aluminium Industry Abstracts</collection><collection>Biotechnology Research Abstracts</collection><collection>Ceramic Abstracts</collection><collection>Chemoreception Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Corrosion Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Engineered Materials Abstracts</collection><collection>Industrial and Applied Microbiology Abstracts (Microbiology A)</collection><collection>Materials Business File</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Solid State and Superconductivity Abstracts</collection><collection>METADEX</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Copper Technical Reference Library</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Journal of chemical technology and biotechnology (1986)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Fernández Núñez, Eutimio Gustavo</au><au>Barchi, Augusto Cesar</au><au>Ito, Shuri</au><au>Escaramboni, Bruna</au><au>Herculano, Rondinelli Donizetti</au><au>Mayer, Cassia Roberta Malacrida</au><au>de Oliva Neto, Pedro</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Artificial intelligence approach for high level production of amylase using Rhizopus microsporus var. oligosporus and different agro‐industrial wastes</atitle><jtitle>Journal of chemical technology and biotechnology (1986)</jtitle><date>2017-03</date><risdate>2017</risdate><volume>92</volume><issue>3</issue><spage>684</spage><epage>692</epage><pages>684-692</pages><issn>0268-2575</issn><eissn>1097-4660</eissn><notes>ObjectType-Article-1</notes><notes>SourceType-Scholarly Journals-1</notes><notes>ObjectType-Feature-2</notes><notes>content type line 23</notes><abstract>BACKGROUND
Culture medium is a key element to be defined when biotechnologies are chosen for agro‐industrial wastes reutilization. This work aimed at definition of culture medium composition using four agro‐industrial wastes (wheat bran, type II wheat flour, soybean meal and sugarcane bagasse) in solid‐state fermentation (SSF) of Rhizopus oligosporus, for high‐level production of amylases through approaches based on artificial intelligence (AI) or response surface methodologies (RSM). First, substrates were individually assessed. Then, I‐optimal mixture experimental designs were performed to determine the influence of two sets of ternary agro‐industrial waste mixtures on amylase and specific amylase activities.
RESULTS
The best individual substrate for amylases production was wheat bran (392.5 U g−1). As a rule, no significant interactions among substrates affecting amylase activities were observed for ternary systems and the approaches under consideration. A significant exception was the amylolytic activity for mixtures composed of wheat bran (91% w/w) and soybean meal (9% w/w). This finding was confirmed analytically by a combination of artificial neural network (ANN) and genetic algorithm (GA). The AI approach improved modelling quality with respect to RSM for production of fungal amylases in SSF.
CONCLUSION
The I‐optimal design in conjunction with ANN‐GA is suggested to optimize accurately culture medium to maximize amylase production by SSF. © 2016 Society of Chemical Industry</abstract><cop>Chichester, UK</cop><pub>John Wiley & Sons, Ltd</pub><doi>10.1002/jctb.5054</doi><tpages>9</tpages></addata></record> |
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| identifier | ISSN: 0268-2575 |
| ispartof | Journal of chemical technology and biotechnology (1986), 2017-03, Vol.92 (3), p.684-692 |
| issn | 0268-2575 1097-4660 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1884133110 |
| source | Wiley-Blackwell Journals |
| subjects | Amylase Artificial intelligence Culture Design analysis enzymes experimental design Genetic algorithms industrial microbiology mathematical modelling solid state fermentation Substrates waste treatment and waste minimization Wastes Wheat |
| title | Artificial intelligence approach for high level production of amylase using Rhizopus microsporus var. oligosporus and different agro‐industrial wastes |
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