Interpreting IR Spectra

Because IR spectra of even relatively simple compounds contain so many peaks, the possibility that two different compounds will have the same IR spectrum is exceedingly small. It is because of this that an IR spectrum has been called the fingerprint of a molecule. Thus, with organic compounds, if two pure samples give different IR spectra, one can be certain that they are different compounds. If they give the same IR spectrum, then they are very likely to be the same compound. 

IR spectra contain a wealth of information about the structures of compounds. We show some of the information that can be gathered from the spectra of octane and methylbenzene (commonly called toluene) in Figs. 2.11 and 2.12. In this section we shall learn how to recognize the presence of characteristic IR absorption peaks that result from vibrations of alkyl and functional groups. The data given in Table 2.7 will provide us with key information to use when correlating actual spectra with IR absorption frequencies that are typical for various groups. 

We can use these peaks in interpreting IR spectra because the exact location of the peak depends on the strength (and stiffness) of the C—H bond, which in turn depends on 

The carbon-hydrogen bond of a terminal alkyne (=C—H) gives an absorption in the 3300-cm region. We can see the absorption of the acetylenic (alkynyl) C—H bond of 1-heptyne at 3320 cm in Fig. 2.13. 

alkenyl C—H bonds and the C—H groups of aromatic rings give absorption peaks in this region. We can see the alkenyl C—H absorption peak at 3080 cm in the spectrum of 1-octene (Fig. 2.14), and we can see the C—H absorption of the aromatic hydrogen atoms at 3090 cm in the spectrum of methylbenzene (Fig. 2.12). 

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Neural networks have been applied to IR spectrum interpreting systems in many variations and applications. Anand [108] introduced a neural network approach to analyze the presence of amino acids in protein molecules with a reliability of nearly 90%. Robb and Munk [109] used a linear neural network model for interpreting IR spectra for routine analysis purposes, with a similar performance. Ehrentreich et al. [110] used a counterpropagation network based on a strategy of Novic and Zupan [111] to model the correlation of structures and IR spectra. Penchev and co-workers [112] compared three types of spectral features derived from IR peak tables for their ability to be used in automatic classification of IR spectra. 

For a spectroscopic observation to be understood, a theoretical model must exist on which the interpretation of a spectrum is based. Ideally one would like to be able to record a spectrum and then to compare it with a spectrum computed theoretically. As is shown in the next section, the model based on the harmonic oscillator approximation was developed for interpreting IR spectra. However, in order to use this model, a complete force-constant matrix is needed, involving the calculation of numerous second derivatives of the electronic energy which is a function of nuclear coordinates. This model was used extensively by spectroscopists in interpreting vibrational spectra. However, because of the inability (lack of a viable computational method) to obtain the force constants in an accurate way, the model was not initially used to directly compute IR spectra. This situation was to change because of significant advances in computational chemistry. 

The Infrared Region 515 12-4 Molecular Vibrations 516 12-5 IR-Active and IR-lnactive Vibrations 518 12-6 Measurement of the IR Spectrum 519 12-7 Infrared Spectroscopy of Hydrocarbons 522 12-8 Characteristic Absorptions of Alcohols and Amines 527 12-9 Characteristic Absorptions of Carbonyl Compounds 528 12-10 Characteristic Absorptions of C—N Bonds 533 12-11 Simplified Summary of IR Stretching Frequencies 535 12-12 Reading and Interpreting IR Spectra (Solved Problems) 537 12-13 Introduction to Mass Spectrometry 541 12-14 Determination of the Molecular Formula by Mass Spectrometry 545... 

IR spectroscopy is used for the qualitative identihcation of surfactants and for differentiating between them and nonsurfactant compounds. Prior to IR spectroscopy, however, separation of the organic compound complex into different fractions, performed by, e.g., the use of thin-layer chromatography, is required to obtain meaningful spectra. °" ° By comparing the IR spectra of the isolated fractions with IR spectra of standard compounds with regard to characteristic bands, the qualitative determination of surfactants in environmental samples is possible. The method is equally applicable to anionic, ° nonionic, °" and cationic surfactants.The prerequisite for a clear identification of surfactants, however, is the availability of suitable standards. Moreover, considerable experience and knowledge are needed to interpret IR spectra of environmental samples. 

The weak absorption at 304 0 cm in the infrared spectrum would be indicative of the N-H stretch of secondary amines. The absorption at 1380 would be indicative of an ethyl, methyl, isopropyl or t-butyl group. The broad absorption at 1120 would be indicative of a C-N stretch, while the broad absorptions at 750 cm and 700 cm would be indicative of a monosubstituted benzene ring. Unfortunately one can only interpret infrared spectra if one is familiar with the various group characteristic peaks. There are no "easy" ways of interpreting ir spectra. 

We can use these peaks in interpreting IR spectra because the exact location of the peak depends on the strength (and stiffness) of the C — H bond, which in turn depends on the hybridization state of the carbon that bears the hydrogen. The C — H bonds involving /-hybridized carbon are strongest and those involving j/ -hybridized carbon are weakest. The order of bond strength is... 

Be amenable to practice by QC laboratory technicians not highly skilled at interpreting IR spectra... 

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