Database, Raman spectra

The Raman analysis confirmed this identification. In Figure 8 are reported the Raman spectrum of the blue pigment obtained and the one provided by the UCL database the superimposition of the two spectra is, in this case, about perfect. 

Spectral resolution is another critical parameter that can affect observed band wavenumbers due to the overlapping of close bands, which can cause erroneous identifications of the material when comparing with standard wavenumber tables and spectral databases. The technical requirements for the construction of a miniaturized Raman spectrometer suitable for extraterrestrial planetary exploration could force a rather low spectral resolution, and so new databases adapted to low resolution spectral data would then be necessary. Nevertheless, good quality spectra are now being obtained from miniaturised Raman spectrometers such as that shown in Figure 1-18, which weighs one kilo. An example of the Raman spectrum of an extremophile obtained from this instrument in our laboratories is shown in Figure 1-19. 

Spectral Database for Organic Compounds (SDBS) is an integrated spectral database system for organic compounds, which includes six different types of spectra, an electron-impact mass spectrum (EI-MS), a Fourier transform infrared spectrum (FT-IR), a H NMR spectrum, a NMR spectrum, a laser Raman spectrum, and an electron spin resonance (ESR) spectrum [72], SDBS is maintained by the National Metrology Institute of Japan (NMU) under the National Instimte of Advanced Industrial Science and technology (AIST). Currently, EI-MS spectrum, H NMR spectrum, C NMR spectrum, FT-IR spectrum, and the compound dictionary are... 

Because the Raman spectrum of any compound is its fingerprint, the successful applications of this technique has further increased the requirements for establishing a database of standard Raman spectra of minerals and related substances [62-65]. 

Databases of spectra of normal and diseased tissue need to be established, which will amply represent the spectral variance that may be encountered in practice. Because these databases will be established over long periods of time (and will most likely be updated on a regular basis), highly repeatable and reproducible instrument calibration (both wave number and intensity axis) is required. This will also facilitate the ability of data transfer from one instrument to another. On-line signal analysis techniques are needed to immediately characterize and/or classify tissue on the basis of its Raman spectrum. Various methods and techniques that can be applied for these purposes have been reviewed recently [8]. Therefore, their discussion will be omitted here. 

The building of Raman standard databases is not as easy as in x-ray diffraction (XRD) or IR spectra. The Raman spectrum of a solid depends not only on its composition and structure but also on the scattering geometry and the wavelength of the source. Moreover, samples are, in general, inhomogeneous and exhibit fluorescence. This means that any standard database must follow a chosen experimental schema, which ensures that at least the spectra are characteristic of each compound or material and are reproducible with enough precision. 

The goal of the spectrum search is the identification of unknown substances based on a reference spectra database. In other words, to perform a search you need a library suited to your problem. The OPUS demo version offers two libraries containing 350 IR spectra and 246 Raman spectra, respectively. 

Number of entry. This item is connected with the identification (chronological) number of the spectrum in the user-created library of the Broker IR-search program, modified for working with Raman spectra. This also serves as a chronological number in the original information database (using Microsoft Office )... 

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