Classification of infrared spectra

Penchev, P. N., Andreev, G. N., Varmuza, K. Anal. Chim. Acta 388, 1999, 145-159. Automatic classification of infrared spectra using a set of improved expert-based features. 

The clustering calculations also provide other kinds of Information about chemical classification of Infrared spectra. These Include the effect of single substituents, carbon skeleton and multiple substitution. These effects were noticeable even under the more difficult circumstance wherein two classes were clustered simultaneously. 

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]. 

Penchev, P.N., Andreev, G.N., and Varmnza, K., Antomatic Classification of Infrared Spectra Using a Set of Improved Expert-Based Featnres, Anal. Chim. Acta, 388, 145, 1999. 

The denominator in equation (28) is the number of vector components which contain a 1 at Least in one of the patterns (Logical or) The numerator is the number of vector components with a 1 in both vectors (Logical and/ Table 2). Application of the Tanimoto distance to the classification of infrared spectra yielded better results than the Hamming distance C353T. 

This structure encoding method has been applied both for the classification of a data set comprising 31 corticosteroids, for which affinity data were available in the literature, binding to the corticosteroid-binding globulin (CBG) receptor, and for the simulation of infrared spectra [28, 29). 

M. Jackson, J. R. Mansfield, B. Dolenko, R. L. Somorjai, H. H. Mantsch and P. H. Watson, Classification of breast tumours by grade and steroid receptor status using pattern recognition analysis of infrared spectra. Cancer Detect. Prevent., 1999, 23, 245-253. 

MIR techniques have simplified obtaining infrared spectra of many materials important in packaging. These include rubber, plastics, laminations, and components of these materials that find use in pumps, sample packages, and devices. The combination of MIR and computerized pattern recognition techniques can be used for differentiating and classification of flexible packaging polymers such as polyvinyl chloride (PVC), polyvinylidene chloride (PVdC), acrylonitrile (Barex), and CTFE (Aclar) [22]. 

N.K. Shah and P.J. Gemperhne, Comhination of the Mahalanobis distance and residual variance pattern recognition techniques for classification of near-infrared reflectance spectra, Anal. Chem., 62, 465-470 (1990). 

Collision-induced electronic spectra have many features in common with rovibrotranslational induced absorption. In this Section, we take a look at the electronic spectra. We start with a historical note on the famous forbidden oxygen absorption bands in the infrared, visible and ultraviolet. We proceed with a brief study of the common features, as well as of the differences, of electronic and rovibrotranslational induced absorption. Recent work is here considered much of which was stimulated by the advent of the laser - hence the name laser-assisted collisions. The enormous available laser powers stimulated new research on laser-controlled, reactive collisions and interactions of supermolecules with intense radiation fields. In conclusion, we attempt a simple classification of various types of electronic collision-induced spectra. 

Many classifications of spectra exist those describing the spectral region involved (ultraviolet, infrared) the appearance of the spectra (line, band) the method of observation (absorption, emission) or the species producing the spectra (atoms, molecules). With respect to processes and properties of expls and proplnts, classification by species is most appropriate since information concerning reaction kinetics is frequently provided by spectroscopic techniques, From a spectroscopic viewpoint, it is convenient to divide the electromagnetic spectrum into a number of sections (see Fig 1). 

Gemperline, P.J. and Boyer, N.R., Classification of near-infrared spectra using wavelength distances comparison to the Mahalanobis distance and residual variance methods, Anal. Chem., 67, 160-166, 1995. 

In assessing the various sites, a surveyor now has available a wonderful array of tools to gather vital observations. An initial classification of observations can be made by the mode of analysis chemical or spectroscopic. Chemical denotes those observations in which atoms or nuclei are handled and sorted. Spectroscopic observations refer, of course, to acquisition and analysis of astronomical spectra. One of the joys of spectroscopic exploration today is the almost complete access to the electromagnetic spectrum allowing observations from the y-lines of nuclear deexcitation and decay to the infrared lines of the rotational transitions of molecules. 

Humin is commonly defined as the class of sedimentary humic matter that remains insoluble when sediments are treated with dilute alkali to extract the soluble humic and fulvic acids. Because of its insolubility and macromolecu-lar nature, humin has been the least studied of all humic fractions. The classification of humin as a separate class of humic substances was initially proposed at the turn of the century by Oden (1919), and this classification has been in use since then. Because of the many similar analytical characteristics (e.g., elemental compositions, functional group compositions, and infrared spectra) between humin and humic acids, and because of the known association of humin with inorganic clays, Khan (1945) and later Kononova (1966) regarded humin as being no more than a clay-humic acid complex. Consequently, Stevenson (1982) has recently questioned whether humin should be considered a separate class of humic substances. Treatment of humin with HF to destroy clays in many instances renders humin soluble in alkali (Stevenson, 1982). 

M. M. Mossoba, F. M. Khambaty, and F. S. Fry, Novel Application of a Disposable Optical Fihn to the Analysis of Bacterial Strains A Chemometric Classification of Mid-Infrared Spectra, Hp/r/. Spectrosc. 56, 32-736 (2002). 

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