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An intelligent rapid odour recognition model in discrimination of Helicobacter pylori and other gastroesophageal isolates in vitro
Cranfield University, Cranfield Biotechnol Centre, Cranfield MK43 0AL, Beds, England; Gloucestershire Royal Hospital, PHLS and Gastroenterol Unit, Gloucester GL1 3NN, England; .
Cranfield University, Cranfield Biotechnol Centre, Cranfield MK43 0AL, Beds, England; Gloucestershire Royal Hospital, PHLS and Gastroenterol Unit, Gloucester GL1 3NN, England; .
Cranfield University, Cranfield Biotechnol Centre, Cranfield MK43 0AL, Beds, England; Gloucestershire Royal Hospital, PHLS and Gastroenterol Unit, Gloucester GL1 3NN, England; .
Cranfield University, Cranfield Biotechnol Centre, Cranfield MK43 0AL, Beds, England; Gloucestershire Royal Hospital, PHLS and Gastroenterol Unit, Gloucester GL1 3NN, England; .
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2000 (English)In: Biosensors & bioelectronics, ISSN 0956-5663, E-ISSN 1873-4235, Vol. 15, no 08-jul, 333-342 p.Article in journal (Refereed) Published
Abstract [en]

Two series of experiments are reported which result in the discrimination between Helicobacter pylori and other bacterial gastroesophageal isolates using a newly developed odour generating system, an electronic nose and a hybrid intelligent odour recognition system. In the first series of experiments, after 5 h of growth (37 degreesC), 53 volatile sniffs were collected over the headspace of complex broth cultures of the following clinical isolates: Staphylococcus aureus, Klebsiella sp., H. pylori, Enterococcus faecalis (10(7) ml(-1)), Mixed infection (Proteus mirabilis, Escherichia coli, and E. faecalis 3 x 10(6) mi each) and sterile cultures. Fifty-six normalised variables were extracted from 14 conductive polymer sensor responses and analysed by a 3-layer back propagation neural network (NN). The NN prediction rate achieved was 98% and the test data (37.7% of all data) was recognised correctly. Successful clustering of bacterial classes was also achieved by discriminant analysis (DA) of a normalised subset of sensor data. Cross-validation identified correctly seven unknown samples. In the second series of experiments after 150 min of microaerobic growth at 37 degreesC, 24 volatile samples were collected over the headspace of H. pylori cultures in enriched (HPP) and normal (HP) media and 11 samples over sterile (N) cultures. Forty-eight sensor parameters were extracted from 12 sensor responses and analysed by a 3-layer NN previously optimised by a genetic algorithm (GA). GA-NN analysis achieved a 94% prediction rate or unknown data. Additionally the genetically selected 16 input neurones were used to perform DA-cross validation that showed a clear clustering of three groups and reclassified correctly nine sniffs. It is concluded that the most important factors that govern the performance of an intelligent bacterial odour detection system are: (a) an odour generation mechanism, (b) a rapid odour delivery system similar to the mammalian olfactory system, (c) a gas sensor array of high reproducibility and (d) a hybrid intelligent model (expert system) which will enable the parallel use of GA-NNs and multivariate techniques. (C) 1999 Elsevier Science S.A. All rights reserved.

Place, publisher, year, edition, pages
Elsevier Science B.V., Amsterdam. , 2000. Vol. 15, no 08-jul, 333-342 p.
Keyword [en]
microbial odour; electronic nose; conducting polymers; Helicobacter pylori; hybrid intelligent systems; neural networks; genetic algorithms; discriminant analysis-cross validation
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-65230ISI: 000165079200002OAI: oai:DiVA.org:liu-65230DiVA: diva2:395004
Available from: 2011-02-04 Created: 2011-02-04 Last updated: 2017-12-11

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CiteExportLink to record
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