Spectra-structure correlations near-infrared

This section provides correlation charts and operational information for the design and interpretation of ultraviolet-visible spectrophotometric (UV-Vis) measurements. While UV-Vis is perhaps not as information-rich as infrared or nuclear magnetic resonance, it nonetheless has value in structure determination and sample identification. Moreover, it is extremely valuable in quantitative work. Typical UV-Vis instruments cover not only the UV and visible spectrum, but the near-infrared as well. Although there is overlap among the ranges, the approximate breakdown is ... 

The near-infrared reflectance provides the response to plasmon oscillations of the electron gas (which are uniform excitations). This region of the spectrum is, however, not sensitive to the strength of the short-range coulombic interactions, which prevent conductivity in a Mott-Hubbard insulating state. This is illustrated by the frequency-dependent conductivity cx((o) measured in various salts exhibiting very different values of the conductivity at room temperature (Fig. 27). The peak of the conductivity at the frequency w0 correlates with the metallic character namely, a low frequency of the peak position corresponds to a high dc conductivity and vice versa. The structures below 0)o are attributed to the coupling with intramolecular modes. 

Figure 4.10 NIR spectrum of benzene (cf. SAQ 4.7). From Weyer, L. G. and Lo, S. C., Spectra-Structure Correlations in the Near-Infrared , in Handbook of Vibrational Spectroscopy, Vol. 3, Chalmers, J. M. and Griffiths, P. R. (Eds), pp. 1817-1837. Copyright 2002. John Wiley Sons Limited. Reproduced with permission.

Neural networks have been applied to infrared spectrum interpreting systems in many variations and applications. Anand introduced a neural network approach to analyze the presence of amino acids in protein molecules with a reliability of nearly 90% [37]. Robb used a linear neural network model for interpreting infrared spectra in routine analysis purposes with a similar performance [38]. Ehrentreich et al. used a counterpropagation (CPG) network based on a strategy of Novic and Zupan to model the correlation of structures and infrared spectra [39]. Penchev and colleagues compared three types of spectral features derived from infrared peak tables for their ability to be used in automatic classification of infrared spectra [40]. 

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