Neural network prediction of biomass digestibility based on structural features.
Identifieur interne : 003734 ( Main/Exploration ); précédent : 003733; suivant : 003735Neural network prediction of biomass digestibility based on structural features.
Auteurs : Jonathan P. O'Dwyer [États-Unis] ; Li Zhu ; Cesar B. Granda ; Vincent S. Chang ; Mark T. HoltzappleSource :
- Biotechnology progress [ 8756-7938 ]
Descripteurs français
- KwdFr :
- Algorithmes (MeSH), Biomasse (MeSH), Bois (MeSH), Cellulase (composition chimique), Chromatographie en phase liquide à haute performance (MeSH), Cristallographie aux rayons X (MeSH), Glucides (analyse), Hydrolyse (MeSH), Intelligence artificielle (MeSH), Langages de programmation (MeSH), Prévision (MeSH), Reproductibilité des résultats (MeSH), Trichoderma (enzymologie).
- MESH :
- analyse : Glucides.
- composition chimique : Cellulase.
- enzymologie : Trichoderma.
- Algorithmes, Biomasse, Bois, Chromatographie en phase liquide à haute performance, Cristallographie aux rayons X, Hydrolyse, Intelligence artificielle, Langages de programmation, Prévision, Reproductibilité des résultats.
English descriptors
- KwdEn :
- Algorithms (MeSH), Artificial Intelligence (MeSH), Biomass (MeSH), Carbohydrates (analysis), Cellulase (chemistry), Chromatography, High Pressure Liquid (MeSH), Crystallography, X-Ray (MeSH), Forecasting (MeSH), Hydrolysis (MeSH), Neural Networks, Computer (MeSH), Programming Languages (MeSH), Reproducibility of Results (MeSH), Trichoderma (enzymology), Wood (MeSH).
- MESH :
- chemical , analysis : Carbohydrates.
- chemical , chemistry : Cellulase.
- enzymology : Trichoderma.
- Algorithms, Artificial Intelligence, Biomass, Chromatography, High Pressure Liquid, Crystallography, X-Ray, Forecasting, Hydrolysis, Neural Networks, Computer, Programming Languages, Reproducibility of Results, Wood.
Abstract
Plots of biomass digestibility are linear with the natural logarithm of enzyme loading; the slope and intercept characterize biomass reactivity. The feed-forward back-propagation neural networks were performed to predict biomass digestibility by simulating the 1-, 6-, and 72-h slopes and intercepts of glucan, xylan, and total sugar hydrolyses of 147 poplar wood model samples with a variety of lignin contents, acetyl contents, and crystallinity indices. Regression analysis of the neural network models indicates that they performed satisfactorily. Increasing the dimensionality of the neural network input matrix allowed investigation of the influence glucan and xylan enzymatic hydrolyses have on each other. Glucan hydrolysis affected the last stage of xylan digestion, and xylan hydrolysis had no influence on glucan digestibility. This study has demonstrated that neural networks have good potential for predicting biomass digestibility over a wide range of enzyme loadings, thus providing the potential to design cost-effective pretreatment and saccharification processes.
DOI: 10.1021/bp070193v
PubMed: 18220407
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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Granda</name></author><author><name sortKey="Chang, Vincent S" sort="Chang, Vincent S" uniqKey="Chang V" first="Vincent S" last="Chang">Vincent S. Chang</name></author><author><name sortKey="Holtzapple, Mark T" sort="Holtzapple, Mark T" uniqKey="Holtzapple M" first="Mark T" last="Holtzapple">Mark T. 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O'Dwyer</name><affiliation wicri:level="2"><nlm:affiliation>Albemarle Corporation, Process Development Center, Gulf States Road, Baton Rouge, LA 70805, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Albemarle Corporation, Process Development Center, Gulf States Road, Baton Rouge, LA 70805</wicri:regionArea><placeName><region type="state">Louisiane</region></placeName></affiliation></author><author><name sortKey="Zhu, Li" sort="Zhu, Li" uniqKey="Zhu L" first="Li" last="Zhu">Li Zhu</name></author><author><name sortKey="Granda, Cesar B" sort="Granda, Cesar B" uniqKey="Granda C" first="Cesar B" last="Granda">Cesar B. Granda</name></author><author><name sortKey="Chang, Vincent S" sort="Chang, Vincent S" uniqKey="Chang V" first="Vincent S" last="Chang">Vincent S. Chang</name></author><author><name sortKey="Holtzapple, Mark T" sort="Holtzapple, Mark T" uniqKey="Holtzapple M" first="Mark T" last="Holtzapple">Mark T. 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The feed-forward back-propagation neural networks were performed to predict biomass digestibility by simulating the 1-, 6-, and 72-h slopes and intercepts of glucan, xylan, and total sugar hydrolyses of 147 poplar wood model samples with a variety of lignin contents, acetyl contents, and crystallinity indices. Regression analysis of the neural network models indicates that they performed satisfactorily. Increasing the dimensionality of the neural network input matrix allowed investigation of the influence glucan and xylan enzymatic hydrolyses have on each other. Glucan hydrolysis affected the last stage of xylan digestion, and xylan hydrolysis had no influence on glucan digestibility. This study has demonstrated that neural networks have good potential for predicting biomass digestibility over a wide range of enzyme loadings, thus providing the potential to design cost-effective pretreatment and saccharification processes.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">18220407</PMID><DateCompleted><Year>2008</Year><Month>06</Month><Day>03</Day></DateCompleted><DateRevised><Year>2019</Year><Month>12</Month><Day>10</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Print">8756-7938</ISSN><JournalIssue CitedMedium="Print"><Volume>24</Volume><Issue>2</Issue><PubDate><MedlineDate>2008 Mar-Apr</MedlineDate></PubDate></JournalIssue><Title>Biotechnology progress</Title><ISOAbbreviation>Biotechnol Prog</ISOAbbreviation></Journal><ArticleTitle>Neural network prediction of biomass digestibility based on structural features.</ArticleTitle><Pagination><MedlinePgn>283-92</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1021/bp070193v</ELocationID><Abstract><AbstractText>Plots of biomass digestibility are linear with the natural logarithm of enzyme loading; the slope and intercept characterize biomass reactivity. The feed-forward back-propagation neural networks were performed to predict biomass digestibility by simulating the 1-, 6-, and 72-h slopes and intercepts of glucan, xylan, and total sugar hydrolyses of 147 poplar wood model samples with a variety of lignin contents, acetyl contents, and crystallinity indices. Regression analysis of the neural network models indicates that they performed satisfactorily. Increasing the dimensionality of the neural network input matrix allowed investigation of the influence glucan and xylan enzymatic hydrolyses have on each other. Glucan hydrolysis affected the last stage of xylan digestion, and xylan hydrolysis had no influence on glucan digestibility. This study has demonstrated that neural networks have good potential for predicting biomass digestibility over a wide range of enzyme loadings, thus providing the potential to design cost-effective pretreatment and saccharification processes.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>O'Dwyer</LastName><ForeName>Jonathan P</ForeName><Initials>JP</Initials><AffiliationInfo><Affiliation>Albemarle Corporation, Process Development Center, Gulf States Road, Baton Rouge, LA 70805, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Zhu</LastName><ForeName>Li</ForeName><Initials>L</Initials></Author><Author ValidYN="Y"><LastName>Granda</LastName><ForeName>Cesar B</ForeName><Initials>CB</Initials></Author><Author ValidYN="Y"><LastName>Chang</LastName><ForeName>Vincent S</ForeName><Initials>VS</Initials></Author><Author ValidYN="Y"><LastName>Holtzapple</LastName><ForeName>Mark T</ForeName><Initials>MT</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2008</Year><Month>01</Month><Day>26</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Biotechnol Prog</MedlineTA><NlmUniqueID>8506292</NlmUniqueID><ISSNLinking>1520-6033</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D002241">Carbohydrates</NameOfSubstance></Chemical><Chemical><RegistryNumber>EC 3.2.1.4</RegistryNumber><NameOfSubstance UI="D002480">Cellulase</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="N">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D001185" MajorTopicYN="N">Artificial Intelligence</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018533" MajorTopicYN="Y">Biomass</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D002241" MajorTopicYN="N">Carbohydrates</DescriptorName><QualifierName UI="Q000032" MajorTopicYN="N">analysis</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D002480" MajorTopicYN="N">Cellulase</DescriptorName><QualifierName UI="Q000737" MajorTopicYN="N">chemistry</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D002851" MajorTopicYN="N">Chromatography, High Pressure Liquid</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D018360" MajorTopicYN="N">Crystallography, X-Ray</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005544" MajorTopicYN="N">Forecasting</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D006868" MajorTopicYN="N">Hydrolysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016571" MajorTopicYN="Y">Neural Networks, Computer</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D011381" MajorTopicYN="N">Programming Languages</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015203" MajorTopicYN="N">Reproducibility of Results</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D014242" MajorTopicYN="N">Trichoderma</DescriptorName><QualifierName UI="Q000201" MajorTopicYN="N">enzymology</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D014934" MajorTopicYN="N">Wood</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="pubmed"><Year>2008</Year><Month>1</Month><Day>29</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2008</Year><Month>6</Month><Day>5</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2008</Year><Month>1</Month><Day>29</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">18220407</ArticleId><ArticleId IdType="doi">10.1021/bp070193v</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>Louisiane</li></region></list><tree><noCountry><name sortKey="Chang, Vincent S" sort="Chang, Vincent S" uniqKey="Chang V" first="Vincent S" last="Chang">Vincent S. Chang</name><name sortKey="Granda, Cesar B" sort="Granda, Cesar B" uniqKey="Granda C" first="Cesar B" last="Granda">Cesar B. Granda</name><name sortKey="Holtzapple, Mark T" sort="Holtzapple, Mark T" uniqKey="Holtzapple M" first="Mark T" last="Holtzapple">Mark T. Holtzapple</name><name sortKey="Zhu, Li" sort="Zhu, Li" uniqKey="Zhu L" first="Li" last="Zhu">Li Zhu</name></noCountry><country name="États-Unis"><region name="Louisiane"><name sortKey="O Dwyer, Jonathan P" sort="O Dwyer, Jonathan P" uniqKey="O Dwyer J" first="Jonathan P" last="O'Dwyer">Jonathan P. O'Dwyer</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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