Linked Life Data

19132557

Source:http://linkedlifedata.com/resource/pubmed/id/19132557

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2010-2-3
pubmed:abstractText
In this study, an artificial neural network (ANN) trained by backpropagation algorithm, Levenberg-Marquadart, was applied to predict the yield of enzymatic synthesis of dioctyl adipate. Immobilized Candida antarctica lipase B was used as a biocatalyst for the reaction. Temperature, time, amount of enzyme, and substrate molar ratio were the four input variables. After evaluating various ANN configurations, the best network was composed of seven hidden nodes using a hyperbolic tangent sigmoid transfer function. The correlation coefficient (R2) and mean absolute error (MAE) values between the actual and predicted responses were determined as 0.9998 and 0.0966 for training set and 0.9241 and 1.9439 for validating dataset. A simulation test with a testing dataset showed that the MAE was low and R2 was close to 1. These results imply the good generalization of the developed model and its capability to predict the reaction yield. Comparison of the performance of radial basis network with the developed models showed that radial basis function was more accurate but its performance was poor when tested with unseen data. In further part of the study, the feedforward backpropagation model was used for prediction of the ester yield within the given range of the main parameters.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
1559-0291
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
158
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
722-35
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Application of artificial neural network for yield prediction of lipase-catalyzed synthesis of dioctyl adipate.
pubmed:affiliation
Department of Chemistry, Faculty of Science, 43400 UPM, Serdang, Universiti Putra Malaysia, Serdang, Selangor Darul Ehsan, Malaysia. basya@science.upm.edu.my
pubmed:publicationType
Journal Article