dimerization reaction (eq 6) is uncertain, but bridged structures containing one OHbridge and another containing one OHand one deprotonated amide nitrogen bridge have been proposed1 23456 for the analogous glycylglycine complex, Cu2(H-iGlygly)2(OH)-. Since KO for the formation of these dimers is the same (Table I) for all isomers of Leu-Leu, it appears that there is relatively little peptide rearrangement during dimer formation and also there is probably little contact between the two peptide ligands in the dimer. Thus the structure with one OHgroup bridging the two Cu(H-íL) residues via the Cu(II) atoms appears to be the most probable.
In the present work, the simplex optlmiratlon procedure is examined as a means of producing high quality linear discriminant functions for recognitlon of eleven functional group categories from low resolution mass spectra. Classifier performance Is evaluated with test sets totaling over 1900 compounds; and a new performance measure-the figure of merlt-Is employed In the evaluation. For linearly separable data, slmpiex-derived weight vectors are not superior to those obtained by the simpler error-correction-feedback methods. For inseparable data, however, a simplex with lnltial vertices at welght vectors derived by error-correction-feedback does indeed generally converge on a set of weight vectors with superior performance. It is found that those weight vectors showing poorest performance correspond to the more difficult chemical discriminations.Studies o f chemical pattern recognition have shown the promise of the method for eventual routine application to spectral analysis (1-4). Previous work has been open to the criticism that it was based on relatively small data sets unrepresentative of practical analytical problems. Therefore, a primary objective of the work presented here was to investigate the applicability of linear discriminant analysis to a larger data set. Accordingly, 2140 mass spectra drawn from a larger collection of 18 806 spectra ( 5 ) , were used in this study.
Carbon-13 nuclear magnetic resonance spectra represented either in binary coded (presence or absence of peaks) or relative intensity form have been subjected to Hadamard Transform processing. Results of linear discriminant function analysis of both Hadamard sequency ordered carbon-13 spectra and Hadamard energy spectra are described. These results are compared to those obtained in a previous study in which Hadamard preprocessing was not used. It is shown that learning machine analysis of the Hadamard domain data is, in general, less efficacious.Recently, we reported (1) the first application of the heuristic pattern recognition method called the "learning machine" (2) approach to the analysis of experimental proton noise-decoupled 13C high resolution nuclear magnetic resonance spectra. We found that the general applicability of 1 Author to whom requests for reprints should be sent.
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