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- Pavlou, AK, et al.
(author)
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An intelligent rapid odour recognition model in discrimination of Helicobacter pylori and other gastroesophageal isolates in vitro
- 2000
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In: Biosensors & bioelectronics. - : Elsevier Science B.V., Amsterdam.. - 0956-5663 .- 1873-4235. ; 15:08-jul, s. 333-342
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Journal article (peer-reviewed)abstract
- 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.
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| 3. |
- Pavlou, AK, et al.
(author)
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Sniffing out the truth: Clinical diagnosis using the electronic nose
- 2000
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In: Clinical Chemistry and Laboratory Medicine. - : Walter de Gruyter. - 1434-6621 .- 1437-4331. ; 38:2, s. 99-112
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Journal article (peer-reviewed)abstract
- Recently the use of smell in clinical diagnosis has been rediscovered due to major advances in odour sensing technology and artificial intelligence (AI). It was well known in the past that a number of infectious or metabolic diseases could liberate specific odours characteristic of the disease stage. Later chromatographic techniques identified an enormous number of volatiles in human clinical specimens that might serve as potential disease markers. "Artificial nose" technology has been employed in several areas of medical diagnosis, including rapid detection of tuberculosis (TB), Helicobacter pylori (HP) and urinary tract infections (UTI). Preliminary results have demonstrated the possibility of identifying and characterising microbial pathogens in clinical specimens. A hybrid intelligent model of four interdependent "tools", odour generation "kits", rapid volatile delivery and recovery systems, consistent low drift sensor performance and a hybrid intelligent system of parallel neural networks (NN) and expert systems, have been applied in gastric, pulmonary and urine diagnosis. Initial clinical tests have shown that it may be possible in the near future to use electronic nose technology not only for the rapid detection of diseases such as peptic ulceration, UTI, and TB but also for the continuous dynamic monitoring of disease stages. Major advances in information and gas sensor technology could enhance the diagnostic power of future bio-electronic noses and facilitate global surveillance models of disease control and management.
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| 4. |
- Pavlou, AK, et al.
(author)
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Use of an electronic nose system for diagnoses of urinary tract infections
- 2002
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In: Biosensors & bioelectronics. - : Elsevier Science B.V., Amsterdam.. - 0956-5663 .- 1873-4235. ; 17:10, s. 893-899
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Journal article (peer-reviewed)abstract
- The use of volatile production patterns produced by bacterial contaminants in urine samples were examined using electronic nose technology. In two experiments 25 and 45 samples from patients were analysed for specific bacterial contaminants using agar culture techniques and the major UTI bacterial species identified. These samples were also analysed by incubation in a volatile generation test tube system for 4-5 h. The volatile production patterns were then analysed using an electronic nose system with 14 conducting polymer sensors. In the first experiment analysis of the data using a neural network (NN) enabled identification of all but one of the samples correctly when compared to the culture information. Four groups could be distinguished, i.e. normal urine, Escherichia coli infected, Proteus spp. and Staphylococcus spp. In the second experiment it was again possible to use NN systems to examine the volatile production patterns and identify 18 of 19 unknown UTI cases. Only one normal patient sample was mis-identified as an E coli infected sample. Discriminant function analysis also differentiated between normal urine samples, that infected with E coli and with Staphylococcus spp. This study has shown the potential for early detection of microbial contaminants in urine samples using electronic nose technology for the first time. These findings will have implications for the development of rapid systems for use in clinical practice. (C) 2002 Elsevier Science B.V. All rights reserved.
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