Component identification

Fuzzy logic and fuzzy set theory are applied to various problems in chemistry. The applications range from component identification and spectral Hbrary search to fuzzy pattern recognition or calibrations of analytical methods. 

In chromatography-FTIR applications, in most instances, IR spectroscopy alone cannot provide unequivocal mixture-component identification. For this reason, chromatography-FTIR results are often combined with retention indices or mass-spectral analysis to improve structure assignments. In GC-FTIR instrumentation the capillary column terminates directly at the light-pipe entrance, and the flow is returned to the GC oven to allow in-line detection by FID or MS. Recently, a multihyphenated system consisting of a GC, combined with a cryostatic interfaced FT1R spectrometer and FID detector, and a mass spectrometer, has been described [197]. Obviously, GC-FTIR-MS is a versatile complex mixture analysis technique that can provide unequivocal and unambiguous compound identification [198,199]. Actually, on-line GC-IR, with... 

Most of the detectors permit peak recognition but provide no structural information, which can be particularly important for identification of unknown compounds. From this point of view, the spectro-metric detectors, specifically mass spectrometer and photodiode array detectors, add a third dimension to the multidimensional system and give additional information useful in components identification. 

Quantification of faecal BAs is carried out in SIM mode by using the internal standard method, and peak areas are obtained from the chromatograms generated by data handling. Component identification is based on fragmentation and comparison of the retention times with those of standards. 

All GC-MS analyses were performed on a Hewlett-Packard 5985 quad-rupole mass spectrometer. Pertinent chromatographic and mass spectral operating parameters are described elsewhere (2). Spectra were collected and recorded in the total ion mode. Individual component spectra were manually compared with U.S. Environmental Protection Agency-National Institutes of Health (USEPA-NIH) library spectra to provide component identifications. All quantitative data were provided as referenced to the dio-anthracene internal standard. 

PUF. Each of the three 2.0-mL PUF extracts was subjected to GC-MS analyses. All analyses were conducted with a Finnigan OWA 1020 GC-MS system fitted with an SE-54 fused-silica capillary column. All spectra were collected in the total ion mode. Component spectra, both raw and background subtracted, were manually compared against USEPA-NIH library spectra to permit component identifications. Quantitative data were again provided by using dio-anthracene as the internal standard. 

Figure 4, Extractable organic profile (ethyl ether/hexane, 5/95) of a random lot of flexible PUF reconstructed ion chromatograms (GC-MS). A, solvent extract B, Soxhlet blank. Component identification (scan number, component) 232, phenol 391, hexanoic acid, 2-ethyl 490, 2,4- or 2,6-toluene diisocyanate (TDI) 507,2-propanamine, 2-methyl 592, phenol, 2,6-bis-(l,l-dimethylethyl)-4-methyl 696, chloroctane (isomer) 737, anthracene-dw (internal standard) 1047, isooctane, ethenyloxy. Continued on next page.

HPLC units have been interfaced with a wide range of detection techniques (e.g. spectrophotometry, fluorimetry, refractive index measurement, voltammetry and conductance) but most of them only provide elution rate information. As with other forms of chromatography, for component identification, the retention parameters have to be compared with the behaviour of known chemical species. For organo-metallic species element-specific detectors (such as spectrometers which measure atomic absorption, atomic emission and atomic fluorescence) have proved quite useful. The state-of-the-art HPLC detection system is an inductively coupled plasma/MS unit. HPLC applications (in speciation studies) include determination of metal alkyls and aryls in oils, separation of soluble species of higher molecular weight, and separation of As111, Asv, mono-, di- and trimethyl arsonic acids. There are also procedures for separating mixtures of oxyanions of N, S or P. 

Instrument automation may be required to provide us with more powerful techniques of data analysis and data handling using statistical techniques that would be otherwise too time consuming to be practical or computer graphics to gain greater flexibility in data analysis. Small data base systems of spectral libraries can help address a problem of faster component identification. 

FIGURE 43 The Optimized Monier-Williams Apparatus Component Identification Is Given in Text (component F is depicted in FIGURE 44). 

Bandemer considered the role of fuzzy set theory in analytical chemistry. The applications they described focused on pattern recognition problems, the calibration of analytical methods,quality control, and component identification and mixture evaluation. Gordon and Somorjai applied a fuzzy clustering technique to the detection of similarities among protein substructures. A molecular dynamics trajectory of a protein fragment was analyzed. In the following subsections, some applications based on the hierarchical fuzzy clustering techniques presented in this chapter are reviewed. 

Demirgian, J.C. Gas chromatography fourier transform infrared spectroscopy mass spectrometry a powerful tool for component identification in complex organic mixtures. Trends Anal. Chem. 1987, 6, 58. 

A possible solution to the above problems would be the triple-dimensional analysis by using GC x GC coupled to TOFMS. Mass spectrometric techniques improve component identification and sensitivity, especially for the limited spectral fragmentation produced by soft ionization methods, such as chemical ionization (Cl) and field ionization (FI). The use of MS to provide a unique identity for overlapping components in the chromatogram makes identification much easier. Thus MS is the most recognized spectroscopic tool for identification of GC X GC-separated components. However, quadru-pole conventional mass spectrometers are unable to reach the resolution levels required for such separations. Only TOFMS possess the necessary speed of spectral acquisition to give more than 50 spectra/sec. This area of recent development is one of the most important and promising methods to improve the analysis of essential oil components. 

Zink, D. Dufresne, C. Liesch, J. Martin, J. Automated LC-MS Analysis of Natural Products Extraction of UV, MS, and Retention Time Data for Component Identification and Characterization, in Proceedings ofthe 50th ASMS Conference on Mass Spectrometry and Allied Topics, Orlando, Florida, June 2-6, 2002. 

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