Spectrum computer search system

Virtually all IR instrument manufacturers now offer computer search. systems to aid in identifying compounds from stored IR spectral data. The position and relative magnitudes of peaks in the spectrum of the analyte are determined and stored in memory to give a peak profile, which can be compared with profiles of pure compounds stored on disk or CD-ROM. The computer then matches profiles and prints a list of compounds having spectra similar to that of the analyte. Usually, the spectrum of the analyte and that of each potential match can be shown simultaneously on the computer display for comparison, as shown in Figure 17-7. 

They generally lack sensitivity and a direct relation to molecular structure. GC-MS is fast, direct, and very sensitive, and the spectrum provides a result which puts identification beyond dispute. Computer-assisted systems are now available which embody extensive drug reference libraries and can be automatically searched to identify unknown spectra. The further development of chemical ionization and mass fragmentography methods using stable isotopes now permits very accurate quantitative work. 

As just noted, correlation charts seldom suffice for the positive identification of an organic compound from its IR spectrum. However, several catalogs of IR spectra are available that assist in qualitative identification by providing comparison spectra for a large number of pure compounds. Manually searching large catalogs of spectra is slow and tedious. For this reason, computer-based search systems are widely used. 

Perhaps the most common application of VS in the determination of chemical makeup in polymeric systems is the identification of components in complex polymer mixtures. Polymeric products are rarely composed of a single component. There are always additives present that aid in processing, appearance, adhesion, chemical stability or other properties important to the function of the final product. In an industrial setting, it is important to be able to determine both the identity and quantity of polymers and additives in a specific formulation for quality control purposes. This can be a fairly routine operation if tools such as spectral libraries are utilized. In this method, a computer search algorithm compares a spectrum with a catalogue of standard spectra to determine the identity of the compovmd or compounds present. Advanced statistical techniques, such as partial least squares (PLS) and principal-component analysis (PCA), are also often used to identify known and unknown components in polymeric systems. The details of these methods are described elsewhere in the Encyclopedia. 

Once a mass spectrum from an eluting component has been acquired, the next step is to try to identify the component either through the skill of the mass spectroscopist or by resorting to a library search. Most modem GC/MS systems with an attached data station include a large library of spectra from known compounds (e.g., the NIST library). There may be as many as 50,000 to 60,000 stored spectra covering most of the known simple volatile compounds likely to be met in analytical work. Using special search routines under the control of the computer, one can examine... 

When a mass spectrum has been acquired by the spectrometer/computer system, it is already in digital form as m/z values versus peak heights (ion abundances), and it is a simple matter for the computer to compare each spectrum in the library with that of the unknown until it finds a match. The shortened search is carried out first, and the computer reports the best fits or matches between the unknown and spectra in the library. A search of even 60,000 to 70,000 spectra takes only a few seconds, particularly if transputers are used, thus saving the operator a great deal of time. Even a partial match can be valuable because, although the required structure may not have been found in the library, it is more than likely that some of the library compounds will have stractural pieces that can be recognized from a partial fit and so provide information on at least part of the structure of the unknown. 

Modem pulse height analysers essentially contain dedicated digital computers which store and process data, as well as control the display and operation of the instrument. The computer will usually provide spectrum smoothing, peak search, peak identification, and peak integration routines. Peak identification may be made by reference to a spectrum library and radionuclide listing. Figure 10.15 summarizes such a pulse height analysis system. 

Fourier transform spectroscopy technology is widely used in infrared spectroscopy. A spectrum that formerly required 15 min to obtain on a continuous wave instrument can be obtained in a few seconds on an FT-IR. This greatly increases research and analytical productivity. In addition to increased productivity, the FT-IR instrument can use a concept called Fleggetts Advantage where the entire spectrum is determined in the same time it takes a continuous wave (CW) device to measure a small fraction of the spectrum. Therefore many spectra can be obtained in the same time as one CW spectrum. If these spectra are summed, the signal-to-noise ratio, S/N can be greatly increased. Finally, because of the inherent computer-based nature of the FT-IR system, databases of infrared spectra are easily searched for matching or similar compounds. 

In contrast to these interactive searches, which are of little appeal to those with large numbers of searches to carry out, there are available two batch-type searches which accept the complete spectrum of the unknown and examine all spectra in the file sequentially to find the best fits. These are the Biemann and PBM search algorithms. Spectra can be entered from a teletype, but in a more common arrangement, a user s data system can be connected to the network for this purpose and the unknown spectra can be down-loaded into the network computer for use in the search, which can be carried out at once, or, if preferred, overnight at 30% of the cost. 

A PC-based 1H-nmr database, which includes full spectrum search capability, is being constructed by the Toyohashi University of Technology (67). Speclnfo, owned by Chemical Concepts, offers a 150,000 spectra library and database system for mainframe computers, which includes H, 15n, 19f, 17o, 31P-nmr, and a large collection of 13C-nmr spectra compiled by Bremser at BASF (68,69). It also offers nB-nmr spectra compiled by Nu th at the University of Munich. 

From the Dewar — Longuet-Higgins formula, Eqs. (1) and (4), it is immediately seen that the above problem is equivalent to the question whether there exist zero eigenvalues in the spectrum of a benzenoid graph. Indeed, in computer-aided searches, constructions and classifications of benzenoid systems, the easiest and most efficient way to recognize non-Kekulean species is just to compute det A. At this point it should be mentioned that Hall [66] recently proposed a new easy method for rapid calculation of det A of a benzenoid system. 

As is true with infrared and nuclear magnetic resonance spectroscopy, large libraries of mass spectra (>150,000 entries) are available in computer-compatible formats,- Most commercial mass spectrometer computer systems have the ability to rapidly search all or pari of such files for spectra that match or closely match the spectrum of an analyte. 

In the case of a spectral search by comparison of peak tables, the system first computes the necessary peak table from the experimental spectmm of the analyte. For the subsequent search both the position and the intensity of the peaks in the spectrum of the unknown and in the spectra of the compounds contained in the data bank are compared. After the search has been completed, the top position in the hit list is assigned to the substance with least differences in peak positions and peak intensities. 

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