Survey of Computer Aided Methods for Mass Spectral Interpretation

Abstract

Recent studies in the application of computerized techniques to mass spectral interpretation have been reviewed. The techniques have been divided into library search, pattern recognition, and interpretive methods. The more common methods in use in each category, as well as the more recent methods, are discussed. Earlier review articles are referenced for a more comprehensive treatment of earlier methods. The status of the large libraries of electron impact mass spectra is discussed and a number of attempts at improving the quality of those libraries are detailed.

Analysis of hazardous waste and environmental extracts by capillary gas chromatography/Fourier transform infrared spectrometry and capillary gas chromatography/mass spectrometry

“…It is seen that this difference is generally greatest for those cases where the two spectral techniques agree. It may also be noted from Table III that the GIFTS hit index differences ( ) are largest for sediment number 1 which contains many analytes whose spectra are contained in the FT-IR search library. Sediment 2 and the oil spill sample apparently contain many analytes whose spectra are not in this library, which is small (2300 spectra), and gives credence to the suggestion of Isenhour et al ( 18) that the FT-IR search library be used as a functional group "prefilter" for the much larger mass spectral library.…”

“…The large number of potentially hazardous analytes in environmental samples (1,2) coupled with deficiencies in 1 Present address: U.S. Environmental Protection Agency, Region 9, Las Vegas, NV. currently available organic analytical techniques (3,4) make routine environmental analysis of gas chromatographicable (GC) volatiles extremely difficult.…”

Determination of chemical classes from mass spectra of toxic organic compounds by SIMCA pattern recognition and information theory

“…Mathematically, binary encoding produces two important results. It reduces the variation of the variable values by assigning one value (1) to all above the threshold level. It also The masses with an asterisk were used in the principal component models for the particular classes.…”

Preprocessing, variable selection, and classification rules in the application of SIMCA pattern recognition to mass-spectral data

“…The standard deviation of the t's along axis a is given by The remaining root-mean-square variance, or OMS distance, is calculated by eq 4. Here A is the number of A))(l/(m -A -1)) ß*/]1/2 (4) principal-component axes in the class model, p is the number of variable axes in feature space so that ip -A) is the number of OMS axes, and tka is the principal-component score for compound k projected onto axis A. The summation is taken over both compounds and variables.…”

“…Therefore, it would be an advantage to be able to perform the data interpretation on a computer other that being used for instrument control and data acquisition. Recently, mass spectral data bases with software for identification of unknown mass spectra have become available for use on microcomputers (3,4), including the NBS/EPA/NIH data base and the Wiley registry of mass spectral data. These identification methods use library searching techniques that require large libraries of mass spectra and are rela- lively slow in actual practice.…”