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Qualitative and Quantitative Analysis of Chlorinated Solvents using Raman Spectroscopy and Machine Learning

ARAN - Access to Research at NUI Galway

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dc.contributor.author Madden, Michael G. en
dc.contributor.author Hennessey, Kenneth en
dc.contributor.author Leger, Marc N. en
dc.contributor.author Ryder, Alan G. en
dc.contributor.author Conroy, Jennifer en
dc.date.accessioned 2009-05-15T09:30:24Z en
dc.date.available 2009-05-15T09:30:24Z en
dc.date.issued 2005 en
dc.identifier.citation Qualitative and Quantitative Analysis of Chlorinated Solvents using Raman Spectroscopy and Machine Learning , Jennifer Conroy, Alan G. Ryder, Marc N. Leger, Kenneth Hennessy & Michael G. Madden, Proceedings of SPIE, the International Society for Optical Engineering, Vol. 5826, pp 131-142, 2005. en
dc.identifier.uri http://hdl.handle.net/10379/189 en
dc.description.abstract The unambiguous identification and quantification of hazardous materials is of increasing importance in many sectors such as waste disposal, pharmaceutical manufacturing, and environmental protection. One particular problem in waste disposal and chemical manufacturing is the identification of solvents into chlorinated or non-chlorinated. In this work we have used Raman spectroscopy as the basis for a discrimination and quantification method for chlorinated solvents. Raman spectra of an extensive collection of solvent mixtures (200+) were collected using a JY-Horiba LabRam, infinity with a 488 nm excitation source. The solvent mixtures comprised of several chlorinated solvents: dichloromethane, chloroform, and 1,1,1-trichloroethane, mixed with solvents such as toluene, cyclohexane and/or acetone. The spectra were then analysed using a variety of chemometric techniques (Principal Component Analysis and Principal Component Regression) and machine learning (Neural Networks and Genetic Programming). In each case models were developed to identify the presence of chlorinated solvents in mixtures at levels of ~5%, to identify the type of chlorinated solvent and then to accurately quantify the amount of chlorinated solvent. en
dc.format application/pdf en
dc.language.iso en en
dc.subject Raman spectroscopy en
dc.subject Hazardous materials en
dc.subject Chlorinated solvents en
dc.subject Non-chlorinated solvents en
dc.subject Chemometrics en
dc.subject Machine learning en
dc.subject.lcsh Raman spectroscopy en
dc.subject.lcsh Hazardous substances en
dc.subject.lcsh Solvents en
dc.subject.lcsh Chemometrics en
dc.subject.lcsh Machine learning en
dc.title Qualitative and Quantitative Analysis of Chlorinated Solvents using Raman Spectroscopy and Machine Learning en
dc.type Conference Paper en

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