Study Of Fluorescence Rejection In Raman Spectroscopy Measurement

Raman spectroscopy is widely used in chemistry, biomedicine, materials, environmental protection and other areas. However, Raman spectroscopy is often hampered by strong fluorescence background that can easily bury the much weaker Raman signal. It's necessary to take some proper fluorescence rejection measure in Raman detection. One efficient technique is the shifted-excitation Raman difference spectroscopy (SERDS), which incorporates two lasers to illuminate the sample alternatively. Two series of spectra are acquired to generate the difference spectrum in order to cancel out the fluorescence. SERDS can reject the fluorescence well, and it is applied to different samples.This paper proposed a low cost SERDS system with 532 nm and 526.5 nm lasers and a home-made holographic notch filter. A high optical density and narrow bandwidth holographic filter was made in order to reject the Rayleigh scattering. The optical path of excitation and collecting were designed carefully. The deconvolution model of reconstructing Raman spectrum from difference spectrum was derived. The result reconstructed by deconvolution had three disadvantages, large noise, negative and oscillation. Then multi-energy constrained iterative algorithm was proposed to overcome these disadvantages. The algorithm was simulated to show the advantages of high signal-noise ratio and convergence rate.Many common organic solvents such as methanol, alcohol and acetone were detected by this system. The measurement error of Raman shifts were within the experimental error. The Raman spectrum of methanol and acetonitrileare in strong fluorescence background were detected. Raman spectrum were well reconstructed by multi-energy constrained iterative algorithm. The results proved that the fluorescence can be effectively rejected by SERDS and Raman spectrum can be well reconstructed by the proposed algorithm. The fluorescent pesticide is also tried to detect, however, it is not successful. A theoretical model is built to analyze the main causes of the test failures.