The use of MS classifiers and structure generation to assist in the identification of unknowns in effect-directed analysis

Abstract

Structure generation and mass spectral classifiers have been incorporated into a new method to gain further information from low-resolution GC-MS spectra and subsequently assist in the identification of toxic compounds isolated using effect-directed fractionation. The method has been developed for the case where little analytical information other than the mass spectrum is available, common, for example, in effect-directed analysis (EDA), where further interpretation of the mass spectra is necessary to gain additional information about unknown peaks in the chromatogram. Structure generation from a molecular formula alone rapidly leads to enormous numbers of structures; hence reduction of these numbers is necessary to focus identification or confirmation efforts. The mass spectral classifiers and structure generation procedure in the program MOLGEN-MS was enhanced by including additional classifier information available from the NIST05 database and incorporation of post-generation 'filtering criteria'. The presented method can reduce the number of possible structures matching a spectrum by several orders of magnitude, creating much more manageable data sets and increasing the chance of identification. Examples are presented to show how the method can be used to provide 'lines of evidence' for the identity of an unknown compound. This method is an alternative to library search of mass spectra and is especially valuable for unknowns where no clear library match is available.