Infrared spectra prediction

W.R. Hruschka and K. Norris, Least squares curve fitting of near-infrared spectra predicts protein and moisture content in ground wheat, Appl. Spectrosc., 36, 261-265 (1982). 

Hruschka, W.R. and Norris, K., Least Squares Curve Fitting of Near-Infrared Spectra Predicts Protein and Moisture Content in Ground Wheat Appl. Spectrosc. 1982, 36, 261-265. 

Figure 7 Infrared spectrum prediction as described by Grzonka. Red = measured spectrum black = predicted spectrum...

Except in simple cases, it is very difficult to predict the infrared absorption spectrum of a polyatomic molecule, because each of the modes has its characteristic absorption frequency rather than just the single frequency of a diatomic molecule. However, certain groups, such as a benzene ring or a carbonyl group, have characteristic frequencies, and their presence can often be detected in a spectrum. Thus, an infrared spectrum can be used to identify the species present in a sample by looking for the characteristic absorption bands associated with various groups. An example and its analysis is shown in Fig. 3. 

Suppose the input to the network is an infrared spectrum, from which the network must determine whether the molecule whose spectrum is being assessed contains a carbonyl group. We could require that the network output a value of one if it believes that a carbonyl group is present in the molecule, and zero otherwise. It is very unlikely that the untrained network will generate exactly the correct output when it is presented with the first sample, so the error in its prediction will be nonzero. In that case, the connection weights in the network are modified (see below) to reduce the error, and thus, they make it more likely that the network will provide the correct answer the next time it sees this spectrum. 

The infrared spectrum in the carbonyl stretching region is very useful in characterizing these complexes (Table I). Three infrared active bands are predicted 18 however, limited solubility may preclude observation of the weaker bands. Dimer formation is easily detected by the presence of characteristic bands.12... 

An absorption (1030 nun) found in the near-infrared spectrum of this complex arises from a mixed valence transition. Light-induced meial-to-metal charge transfer was predicted by Hush56 for systems of this type before it was observed experimentally. Further, his theory relates the energy of absorption to that required for thenral electron transfer (hv = 4 x Ec) and from this it is possible to calculate the thermal electron transfer rale constant (5 x (08 s-1).57... 

The MP2 and B3LYP methods predict very similar vibrational fundamental frequencies and infrared intensities for the five intermolecular modes (v7/ v9, vW/ vn, and vYl). Moreover, the v7 mode is predicted to be one of the most intense bands in the infrared spectrum of the complex. The OH H202 radical complex is supported by the observation of these vibrational modes in the laboratory. 

The structures of 1-azirine 6 and its complexes with H+ and Li+ have been calculated using ab initio methods <1993JA11074>. An unsuccessful study aimed at matrix isolation of 2-azirine 7 used ab initio methods to calculate its infrared spectrum and predicted that 2-azirine 7 is 32.733.2 kcal mol1 higher in energy than 1-azirine 6 <1993CB2337>. 

Two level factorial designs are primarily useful for exploratory purposes and calibration designs have special uses in areas such as multivariate calibration where we often expect an independent linear response from each component in a mixture. It is often important, though, to provide a more detailed model of a system. There are two prime reasons. The first is for optimisation - to find the conditions that result in a maximum or minimum as appropriate. An example is when improving die yield of synthetic reaction, or a chromatographic resolution. The second is to produce a detailed quantitative model to predict mathematically how a response relates to die values of various factors. An example may be how the near-infrared spectrum of a manufactured product relates to the nature of the material and processing employed in manufacturing. 

Rigid Molecule Group theory will be given in the main part of this paper. For example, synunetry adapted potential energy function for internal molecular large amplitude motions will be deduced. Symmetry eigenvectors which factorize the Hamiltonian matrix in boxes will be derived. In the last section, applications to problems of physical interest will be forwarded. For example, conformational dependencies of molecular parameters as a function of temperature will be determined. Selection rules, as wdl as, torsional far infrared spectrum band structure calculations will be predicted. Finally, the torsional band structures of electronic spectra of flexible molecules will be presented. 

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