Characterization and subtraction of luminescence background signals in high-wavenumber Raman spectra of human tissue
Characterization and subtraction of luminescence background signals in high-wavenumber Raman spectra of human tissue
复制标题
DOI：
10.1002/jrs.5338
复制
发表时间：
2018-04-01
影响因子：
2.5
通讯作者：
Puppels, G. J.
中科院分区：
文献类型：
--
作者：
Barroso, E. M.;Schut, T. C. Bakker;Puppels, G. J.
Raman spectroscopy in the high-wavenumber spectral region (HWR) is particularly suited for fiber-optic in vivo medical applications. The most-used fiber-optic materials have negligible Raman signal in the HWR. This enables the use of simple and cheap single-fiber-optic probes that can be fitted in endoscopes and needles. The HWR generally shows less tissue luminescence than the fingerprint region. However, the luminescence can still be stronger than the Raman signal. Hardware- and software-based strategies have been developed to correct for these luminescence signals. Typically, hardware-based strategies are more complex and expensive than software-based solutions. Effective software strategies have almost exclusively been developed for the fingerprint region. First-order polynomial baseline fitting (PBF) is the most common background/luminescence estimation employed for the HWR. The goal of this study was to characterize the luminescence background signals of HW spectra of human oral tissue and compare the performance of two algorithms for correction of these background signals: PBF and multiple regression fitting (MRF). In the MRF method, we introduce here, prior knowledge of the range of Raman signals that can be obtained from the tissues of interest is explicitly used. MRF is more robust than PBF because it does not require an a priori choice of the polynomial order for fitting the background signal. This is important because, as we show, no single polynomial order can optimally characterize all backgrounds that are encountered in HW tissue spectra. We conclude that MRF should be the preferred method for background subtraction in the HWR.
