Spectra-structure correlations for near infrared

R Goddu and D Delker. Spectra-Structure Correlations for Near-Infrared Region. Anal. Chem. 32 140-141, 1960. 

Goddu, R.F. and Delker, D.A., Spectra-structure correlations for near-infrared. Anal. Chem., 32, 140-141, 1960. 

Liu, Y., Cho, R.-K., Sakurai, K., Mima, T., and Ozaki, Y, Studies on specira/structure correlations in near-infrared spectra of proteins and polypeptides. Part 1 A marker band for hydrogen bonds, Appl. Spectrosc., 48(10), 1249-1253, 1994. 

R. F. Goddu and D. Delker. Aids for the analyst spectra-structure correlations for the near-infrared region. Ana/ Chem 32 140-141,1960. 

Chapter 5 summarizes the crystal field spectra of transition metal ions in common rock-forming minerals and important structure-types that may occur in the Earth s interior. Peak positions and crystal field parameters for the cations in several mineral groups are tabulated. The spectra of ferromagnesian silicates are described in detail and correlated with the symmetries and distortions of the Fe2+ coordination environments in the crystal structures. Estimates are made of the CFSE s provided by each coordination site accommodating the Fe2+ ions. Crystal field splitting parameters and stabilization energies for each of the transition metal ions, which are derived from visible to near-infrared spectra of oxides and silicates, are also tabulated. The CFSE data are used in later chapters to explain the crystal chemistry, thermodynamic properties and geochemical distributions of the first-series transition elements. 

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]. 

The energy level in a molecule is described as the sum of the atomic and molecular motions due to translational, rotational, vibrational, and electronic energies. Translational energy has no effect on molecular spectra, whereas the other motions do affect the spectral characteristics. Rotational energy is proportional to the angular velocity of rotation for each molecule. Electronic energy in molecules and their various quantum numbers are described via the Pauli principle and are beyond the scope of this work. We will restrict our discussion to the vibrational energy levels and use the application of what is learned in this model as a basis for our specific structure-correlation characterization of near-infrared (NIR) spectra. 

Interpretive spectroscopy provides a basis for the establishment of cause-and-effect relationships between spectrometer response and the chemical properties of the samples. While many books available on NIR cover a range of applications and topics from a broad perspective, most of them barely touch on structure correlation and interpretation of spectra. The first, and arguably the only, book to tackle this intriguing and challenging area, Practical Guide to Interpretive Near-Infrared Spectroscopy presents the most detailed discussion of the subject to date. 

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