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Classification of limonoids and protolimonoids using neural networks

✍ Scribed by Leigh-Anne Fraser; Dulcie A. Mulholland; David D. Fraser

Publisher: John Wiley and Sons
Year: 1997
Tongue: English
Weight: 197 KB
Volume: 8
Category: Article
ISSN: 0958-0344
DOI: 10.1002/(sici)1099-1565(199711/12)8:6<301::aid-pca373>3.0.co;2-2

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✦ Synopsis

A novel technique of classifying liminoids and protolimonoids using artificial neural networks is presented. The difficulties associated with natural product classification are discussed, as well as the relevance of artificial neural networks to the task of automated classification by computer. Data from the 13 C nuclear magnetic resonance spectra of the compounds is pre-processed using histogram binning. Neural networks are trained on this data correctly to classify the data as belonging to a "limonoid", "triterpenoid" or "other" category and to discriminate the protolimonoids from the rest of the triterpenoids in the "triterpenoid" data set. Finally, neural networks are trained to recognise each individual limonoid belonging to the "limonoid" data set. The accuracy of the neural networks is typically better than 90% on unseen or impure data.

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