Analysis and Classification of Liquid Samples Using Spatial Heterodyne Raman Spectroscopy

Author:

Gojani Ardian B.1,Palásti Dávid J.23,Paul Andrea1,Galbács Gábor23,Gornushkin Igor B.1ORCID

Affiliation:

1. Federal Institute for Material Research and Testing (BAM), Berlin, Germany

2. Department of Inorganic and Analytical Chemistry, University of Szeged, Dóm Square, Hungary

3. Department of Materials Science, Interdisciplinary Excellence Centre, University of Szeged, Dugonics Square, Hungary

Abstract

Spatial heterodyne spectroscopy (SHS) is used for quantitative analysis and classification of liquid samples. SHS is a version of a Michelson interferometer with no moving parts and with diffraction gratings in place of mirrors. The instrument converts frequency-resolved information into a spatially resolved one and records it in the form of interferograms. The back-extraction of spectral information is done by the fast Fourier transform. A SHS instrument is constructed with the resolving power 5000 and spectral range 522–593 nm. Two original technical solutions are used as compared to previous SHS instruments: the use of a high-frequency diode-pumped solid-state laser for excitation of Raman spectra and a microscope-based collection system. Raman spectra are excited at 532 nm at the repetition rate 80 kHz. Raman shifts between 330 cm−1 and 1600 cm−1 are measured. A new application of SHS is demonstrated: for the first time, it is used for quantitative Raman analysis to determine concentrations of cyclohexane in isopropanol and glycerol in water. Two calibration strategies are employed: univariate based on the construction of a calibration plot and multivariate based on partial least squares regression. The detection limits for both cyclohexane in isopropanol and glycerol in water are at a 0.5 mass% level. In addition to the Raman–SHS chemical analysis, classification of industrial oils (biodiesel, poly(1-decene), gasoline, heavy oil IFO380, polybutenes, and lubricant) is performed using the Raman–fluorescence spectra of the oils and principal component analysis. The oils are easily discriminated showing distinct non-overlapping patterns in the principal component space.

Publisher

SAGE Publications

Subject

Spectroscopy,Instrumentation

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