Other Journal Details
Mandatory Fields
Conroy, J.; Ryder, A.G.; Leger, M.N.; Hennessey, K.; Madden, M.G.
Proc SPIE - Int. Soc. Opt. Eng.
Qualitative and quantitative analysis of chlorinated solvents using Raman spectroscopy and machine learning
Optional Fields
Raman spectroscopy hazardous materials chlorinated solvents non-chlorinated solvents chemometrics machine learning.
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.
SPIE, Bellingham, WA, 2005
Byrne, Lewis, MacCraith, McGlynn, McLaughlin, O'Sullivan, Ryder, Walsh,
Grant Details
This work was funded via a grant from Enterprise Irelandís Commercialisation Fund Technology Development Programme (grant no: TD/03/212). The Raman instrumentation was provided by the National Centre for Biomedical Engineering Science as part of the Irish Higher Education Authorityís Programme for Research in Third Level Institutions. AR is supported by Science Foundation Ireland grant no. 02/IN.1/M231.
Publication Themes