Analysis of volatile organic compounds (VOCs) for medical and industrial applications typically requires complex and expensive mass spectrometric systems to achieve the desired sensitivity and specificity. Raman spectroscopy enables specific compound identification based on spectral fingerprints with sharp features but traditionally has low sensitivity. We have developed a novel Raman system that provides sensitive VOC detection in the low ppm range. In this system, VOCs from the gas phase are absorbed and enriched in a thin polymer film coated on the surface of an optical sensor element. The VOC enriched polymer film is probed with evanescent wave excitation and employs optimized Raman signal collection. Our Raman sensor is low-cost, robust, readily manufacturable, and capable of providing high sensitivity without the need for nanostructured sensors used in techniques such as surface enhanced Raman spectroscopy (SERS), which often show poor reproducibility.<br/><br/>To suppress background noise and other interfering spectral features while improving our ability to resolve VOC mixture spectra, we applied two-dimensional (2D) correlation analysis to time series spectra acquired during VOCs absorption into the polymer film. We established the proof of principle through an analysis of known VOC biomarkers for selected bacterial pathogens. The system enabled VOC detection with significantly higher sensitivity than a commercial Raman probe. Good agreement between bulk reference spectra and VOC spectra obtained after 2D correlation analysis facilitated compound identification. Mixture spectra were further de-convoluted based on the off-diagonal peaks observable in the 2D Raman spectrum in conjunction with segmented data analysis. We are currently exploring commercially available thermal desorption materials and a variety of lens coating polymers to further enhance our sensitivity to low-level analytes.

TIR-Enhanced Raman system for sensitive and selective identification of volatile organic compounds

253rd National Meeting (2017)

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