Premium
Computer‐aided interpretation of mass spectra. Information on substructural probabilities form stirs
Author(s) -
Dayringer Henry E.,
Pesyna Gail M.,
Venkataraghavan Rengachari,
McLafferty F. W.
Publication year - 1976
Publication title -
organic mass spectrometry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.475
H-Index - 121
eISSN - 1096-9888
pISSN - 0030-493X
DOI - 10.1002/oms.1210110512
Subject(s) - substructure , identification (biology) , class (philosophy) , interpretation (philosophy) , computer science , reliability (semiconductor) , artificial intelligence , pattern recognition (psychology) , mass spectrum , spectral line , computer program , natural language processing , data mining , engineering , chemistry , programming language , physics , structural engineering , mass spectrometry , power (physics) , botany , quantum mechanics , biology , chromatography , astronomy
The capability of the ‘Self‐Training Interpretive and Retrieval System’ has been extended so that the compounds selected as providing the best match in each data class to the unknown mass spectrum are examined by the computer for the presence of each of 179 common substructural fragments. Stastical methods were used to evaluate the selectivity for identification of each substructure by each data class using a reference file of 18 806 spectra of different compounds. In tests using at least 373 unknown spectra for each of the substructures, with criteria that gave a mean reliability of 98.1%, the method averaged 49% recall, which corresponds to the identification of 2.55 substructures per unknown spectrum, as well as the normal “Self‐Training Interpretative and Retrieval System” match‐factor results, requires 68 s on a laboratory computer. The method is also available to outside users on an international computer network.