Qualitative and Quantitative Analysis of Chlorinated Solvents using Raman Spectroscopy and 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