Scientific Paper describing research work done using our designs of VOC analysers and electronic noses
An intelligent rapid odour recognition model in discrimination of Helicobacter pylori and other gastroesophageal isolates in vitro
Biosensors & Bioelectronics 15 (2000) 333-342
A.K. Pavlou a, N. Magan a, D. Sharp b, J. Brown b, H. Barr b, A.P.F. Turner a,* a Cranfield Biotechnology Centre, Cranfield Uni6ersity, Cranfield, Beds, MK430AL, UK b PHLS and Gastroenterology Unit, Gloucestershire Royal Hospital, Gloucester, UK
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°C), 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 (107 ml_1), Mixed infection (Proteus mirabilis, Escherichia coli, and E. faecalis 3_106 ml 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°C, 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 of' '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.