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Artificial neural networks for the classification of Meliaceae extractives.

dc.contributor.advisorMulholland, Dulcie Aca.
dc.contributor.authorFraser, Leigh-Anne.
dc.descriptionThesis (Ph.D.)-University of Natal, Durban, 1998.en
dc.description.abstractThe goal of this project was the development of a computer-based system using artificial intelligence to classify the limonoids, protolimonoids and triterpenoids isolated from the family Meliaceae by the Natural Products Research Group of the University of Natal, Durban. A database of samples was obtained between 1991 and 1996, part of which time the author was a member of the group and isolated compounds from Turraea obtusifolia and Turraea floribunda. Over and above the problem of complexity and similarity in structures of the above mentioned natural products, are other difficulties. These include very small amounts of sample being isolated producing very weak peak signals in the C-13 NMR spectra, extraneous peaks in the NMR spectra due to different impurities and instrument noise, non-reproducible spectra due to the pulsed Fourier transform intervals and the nuclear Overhauser effect, impure samples often isolated as stereoisomeric mixtures or as mixed esters and superposition of peak signals in the NMR spectra due to carbons in the same environment within the same compound. These factors make identification by traditional computational and expert systems impossible. As a result of these shortcomings, the author has developed a novel approach using artificial neural network techniques. The artificial neural network system developed used real data from the 300 MHz NMR spectrometer in the Department of Chemistry, Durban. The system was trained to discriminate between limonoids, triterpenoids and flavonoids/coumarins from the C-13 NMR spectra of pure, impure and unseen compounds with an accuracy of better than 90%. Further differentiation of the glabretals from the rest of the protolimonoids as well as from the rest of the triterpenoids showed similarly significant results. Finally, individual limonoid discrimination within the limonoid dataset was extremely successful. Apart from its application to the extractives from Meliaceae, the methodology and techniques developed by the author can be applied to other sets of extractives to provide a robust method for the spectral classification of pre-identified natural products.en
dc.subjectNatural products--Data processing.en
dc.subjectArtificial intelligence.en
dc.subjectNeural networks (Computer science)en
dc.subjectNuclear magnetic resonance spectroscopy--Data processing.en
dc.subjectNatural products--Spectra.en
dc.titleArtificial neural networks for the classification of Meliaceae extractives.en


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