Peaks libraries

Identification of an intoxicating agent and its metabolites in post-mortem plasma and urine was described by Verheij et al. [27] by combining accurate mass determination with high resolution mass spectrometry in on-line LC-MS, El, and Cl spectra of the various peaks, library searching of the El spectra, and information from the LC retention times. 

A mass spectrometer produces an enormous amount of data, especially in combination with chromatographic sample inlets [42]. Over the years, many approaches for analysis of GC-MS data have been proposed using various algorithms, many of which are quite sophisticated, in efforts to detect, identify, and quantify all of the chromatographic peaks. Library search algorithms are com monly provided with mass spectrometer data systems with the purpose to assist in the identi cation of unknown compounds [43]. 

Woodruff and co-workers introduced the expert system PAIRS [67], a program that is able to analyze IR spectra in the same manner as a spectroscopist would. Chalmers and co-workers [68] used an approach for automated interpretation of Fourier Transform Raman spectra of complex polymers. Andreev and Argirov developed the expert system EXPIRS [69] for the interpretation of IR spectra. EXPIRS provides a hierarchical organization of the characteristic groups that are recognized by peak detection in discrete ames. Penchev et al. [70] recently introduced a computer system that performs searches in spectral libraries and systematic analysis of mixture spectra. It is able to classify IR spectra with the aid of linear discriminant analysis, artificial neural networks, and the method of fe-nearest neighbors. 

Once the peaks have been collected and stored, the computer can be asked to work on the data to produce a mass spectrum and print it out, or it can be asked to carry out other operations such as library searching, producing a mass chromatogram, and making an accurate mass measurement on each peak. Many other examples of the use of computers to process mass data are presented in other chapters of this book. 

Most mass spectrometers for analytical work have access to a large library of mass spectra of known compounds. These libraries are in a form that can be read immediately by a computer viz., the data corresponding to each spectrum have been compressed into digital form and stored permanently in memory. Each spectrum is stored as a list of m/z values for all peaks that are at least 5% of the height of the largest peak. To speed the search process, a much shorter version of the spectrum is normally examined (e.g., only one peak in every fourteen mass units). 

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. 

In conclusion, SSIMS spectra provide not only evidence of all the elements present, but also detailed insight into molecular composition. Quasimolecular ions can be desorbed intact up to 15000 amu, depending on the particular molecule [3.17] and on whether an effective mechanism of ionization is present. Larger molecules can be identified from fragment peak patterns which are characteristic of the particular molecules. If the identity of the material being analyzed is completely unknown, spectral interpretation can be accomplished by comparing the major peaks in the spectrum with those in a library of standard spectra. 

Figure 10.11 Comparison of the mass spectra of a neroli oil peak (camphene) obtained by HPLC-HRGC-MS (a) and GC-MS (b) with a library specti um of the same compound (c). Reprinted from Perfumer and Flavorist, 21, L. Mondello et al., On-line HPLC- HRGC in the analytical chemistiy of citms essential oils , pp. 25-49, 1996, with permission from Allured Publishing Coip.

Solvents and their impurities represent a wide class of compound types therefore, a discussion of common mass spectral features is meaningless. However, most of the mass spectra are listed in computer library search programs and The Eight Peak Index. ... 

The extracted fractions were esterified with either BF3-MeOH reagent or diazomethane and analyzed by GLC. Gas liquid chromatography (GLC) was conducted with a Perkin-Elmer Sigma 3 equipped with flame ionization detector. Separations were obtained on a Hewlett Packard 12 m x 0.2 mm i.d. capillary column coated with methyl silicon fluid (OV-101). The temperature was maintained at 80°C for 2 min then programmed from 80 to 220°C at 8°C/min. The injector temperature was 250°C. Mass spectra were obtained on a Hewlett Packard model 5995 GC-MS mass spectrometer, equipped with a 15 m fused silica capillary column coated with 5% phenyl methyl silicone fluid. Spectra were obtained for major peaks in the sample and compared with a library of spectra of authentic compounds. 

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