Towards an expert system for enantioseparations: induction of rules using machine learning

Bryant, CH ORCID: https://orcid.org/0000-0002-9002-8343, Adam, AE, Taylor, DR and Rowe, RC 1996, 'Towards an expert system for enantioseparations: induction of rules using machine learning' , Chemometrics and Intelligent Laboratory Systems, 34 (1) , pp. 21-40.

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Abstract

A commercially available machine induction tool was used in an attempt to automate the acquisition of the knowledge needed for an expert system for enantioseparations by High Performance Liquid Chromatography using Pirkle-type chiral stationary phases (CSPs). Various rule-sets were induced that recommended particular CSP chiral selectors based on the structural features of an enantiomer pair. The results suggest that the accuracy of the optimal rule-set is 63% + or - 3% which is more than ten times greater than the accuracy that would have resulted from a random choice.

Item Type:	Article
Themes:	Subjects / Themes > Q Science > QA Mathematics > QA075 Electronic computers. Computer science Subjects / Themes > Q Science > QD Chemistry Subjects outside of the University Themes
Schools:	Schools > School of Computing, Science and Engineering Schools > School of Computing, Science and Engineering > Salford Innovation Research Centre
Journal or Publication Title:	Chemometrics and Intelligent Laboratory Systems
Publisher:	Elsevier
Refereed:	Yes
ISSN:	01697439
Related URLs:	Author
Depositing User:	Dr Chris H. Bryant
Date Deposited:	17 Feb 2009 15:25
Last Modified:	16 Feb 2022 08:17
URI:	https://usir.salford.ac.uk/id/eprint/1772

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