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dc.contributor.authorFogelman, Shoshanaen_US
dc.contributor.authorBlumenstein, Michaelen_US
dc.contributor.authorZhao, Huijunen_US
dc.date.accessioned2017-04-24T08:20:36Z
dc.date.available2017-04-24T08:20:36Z
dc.date.issued2006en_US
dc.date.modified2010-08-26T07:35:47Z
dc.identifier.issn09410643en_US
dc.identifier.doi10.1007/s00521-005-0015-9en_AU
dc.identifier.urihttp://hdl.handle.net/10072/13804
dc.description.abstractA simple method based on the mathematical treatment of spectral absorbance profiles in conjunction with artificial neural networks (ANNs) is demonstrated for rapidly estimating chemical oxygen demand (COD) values of wastewater samples. In order to improve spectroscopic analysis and ANN training time as well as to reduce the storage space of the trained ANN algorithm, it is necessary to decrease the ANN input vector size by extracting unique characteristics from the raw input pattern. Key features from the spectral absorbance pattern were therefore selected to obtain the spectral absorbance profile, reducing the ANN input vector from 160 to 10 selected inputs. The results indicate that the COD values obtained from the selected absorbance profiles agreed well with those obtained from the entire absorbance pattern. The spectral absorbance profile technique was also compared to COD values estimated by a multiple linear regression (MLR) model to validate whether ANNs were better and more robust models for rapid COD analysis. It was found that the ANN model predicted COD values closer to standard COD values than the MLR model.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherSpringeren_US
dc.publisher.placeUnited Kingdomen_US
dc.relation.ispartofstudentpublicationYen_AU
dc.relation.ispartofpagefrom197en_US
dc.relation.ispartofpageto203en_US
dc.relation.ispartofjournalNeural Computing And Applicationsen_US
dc.relation.ispartofvolume15en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode250405en_US
dc.titleEstimation of Chemical Oxygen Demand by Ultraviolet Spectroscopic Profiling and Artificial Neural Networksen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
gro.facultyGriffith Sciences, Griffith School of Environmenten_US
gro.date.issued2006
gro.hasfulltextNo Full Text


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