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dc.contributor.authorFogelman, Shoshanaen_US
dc.contributor.authorZhao, Huijunen_US
dc.contributor.authorBlumenstein, Michaelen_US
dc.date.accessioned2017-04-24T08:20:31Z
dc.date.available2017-04-24T08:20:31Z
dc.date.issued2006en_US
dc.date.modified2010-08-26T07:35:50Z
dc.identifier.issn16182642en_US
dc.identifier.doi10.1007/s00216-006-0817-3en_AU
dc.identifier.urihttp://hdl.handle.net/10072/14408
dc.description.abstractIn this study, an investigation was undertaken to determine whether the predictive accuracy of an indirect, multiwavelength spectroscopic technique for rapidly determining oxygen demand (OD) values is affected by the use of unfiltered and turbid samples, as well as by the use of absorbance values measured below 200 nm. The rapid OD technique was developed that uses UV-Vis spectroscopy and artificial neural networks (ANNs) to indirectly determine chemical oxygen demand (COD) levels. It was found that the most accurate results were obtained when a spectral range of 190-350 nm was provided as data input to the ANN, and when using unfiltered samples below a turbidity range of 150 NTU. This is because high correlations of above 0.90 were obtained with the data using the standard COD method. This indicates that samples can be measured directly without the additional need for preprocessing by filtering. Samples with turbidity values higher than 150 NTU were found to produce poor correlations with the standard COD method, which made them unsuitable for accurate, real-time, on-line monitoring of OD levels.en_US
dc.description.peerreviewedYesen_US
dc.description.publicationstatusYesen_AU
dc.languageEnglishen_US
dc.language.isoen_AU
dc.publisherSpringer -Verlagen_US
dc.publisher.placeHeidelberg, Germanyen_US
dc.relation.ispartofstudentpublicationYen_AU
dc.relation.ispartofpagefrom1773en_US
dc.relation.ispartofpageto1779en_US
dc.relation.ispartofjournalAnalytical & Bioanalytical Chemistryen_US
dc.relation.ispartofvolume386en_US
dc.rights.retentionYen_AU
dc.subject.fieldofresearchcode250405en_US
dc.titleA rapid analytical method for predicting the oxygen demand of wastewateren_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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