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dc.contributor.authorMadden, Michael G.
dc.contributor.authorRyder, Alan G.
dc.date.accessioned2016-11-28T15:19:54Z
dc.date.available2016-11-28T15:19:54Z
dc.date.issued2003-08-27
dc.identifier.citationMichael G. Madden ; Alan G. Ryder; Machine learning methods for quantitative analysis of Raman spectroscopy data. Proc. SPIE 4876, Opto-Ireland 2002: Optics and Photonics Technologies and Applications, 1130 (March 17, 2003); doi:10.1117/12.464039en_IE
dc.identifier.issn1996-756X
dc.identifier.urihttp://hdl.handle.net/10379/6182
dc.description.abstractThe automated identification and quantification of illicit materials using Raman spectroscopy is of significant importance for law enforcement agencies. This paper explores the use of Machine Learning (ML) methods in comparison with standard statistical regression techniques for developing automated identification methods. In this work, the ML task is broken into two sub-tasks, data reduction and prediction. In well-conditioned data, the number of samples should be much larger than the number of attributes per sample, to limit the degrees of freedom in predictive models. In this spectroscopy data, the opposite is normally true. Predictive models based on such data have a high number of degrees of freedom, which increases the risk of models over-fitting to the sample data and having poor predictive power. In the work described here, an approach to data reduction based on Genetic Algorithms is described. For the prediction sub-task, the objective is to estimate the concentration of a component in a mixture, based on its Raman spectrum and the known concentrations of previously seen mixtures. Here, Neural Networks and k-Nearest Neighbours are used for prediction. Preliminary results are presented for the problem of estimating the concentration of cocaine in solid mixtures, and compared with previously published results in which statistical analysis of the same dataset was performed. Finally, this paper demonstrates how more accurate results may be achieved by using an ensemble of prediction techniques.en_IE
dc.description.sponsorshipThe work was part assisted by the Irish Higher Education Authority, under its Programme for Research in Third Level Institutions.en_IE
dc.formatapplication/pdfen_IE
dc.language.isoenen_IE
dc.publisherSociety of Photo-optical Instrumentation Engineers (SPIE)en_IE
dc.relation.ispartofProc SPIE - Int. Soc. Opt. Eng.en
dc.subjectForensic scienceen_IE
dc.subjectNarcoticsen_IE
dc.subjectRegressionen_IE
dc.subjectRamanen_IE
dc.subjectSpectroscopyen_IE
dc.subjectMachine Learningen_IE
dc.subjectEnsembleen_IE
dc.subjectGenetic algorithmen_IE
dc.subjectNeural networken_IE
dc.titleMachine learning methods for quantitative analysis of Raman spectroscopy dataen_IE
dc.typeConference Paperen_IE
dc.date.updated2016-11-28T10:15:51Z
dc.identifier.doi10.1117/12.464039
dc.local.publishedsourcehttp:/dx.doi.org/10.1117/12.464039en_IE
dc.description.peer-reviewedNot peer reviewed
dc.contributor.funder|~|
dc.internal.rssid1158894
dc.local.contactAlan Ryder, School Of Chemistry, Room 213, Arts/Science Building, Nui Galway. 2943 Email: alan.ryder@nuigalway.ie
dc.local.copyrightcheckedNo
dc.local.versionPUBLISHED
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