thesis
Bacteria classification with an electronic nose employing artificial neural networks
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Abstract
This PhD thesis describes research for a medical application of electronic nose technology.
There is a need at present for early detection of bacterial infection in order to
improve treatment. At present, the clinical methods used to detect and classify bacteria
types (usually using samples of infected matter taken from patients) can take up to
two or three days. Many experienced medical staff, who treat bacterial infections, are
able to recognise some types of bacteria from their odours. Identification of pathogens
(i.e. bacteria responsible for disease) from their odours using an electronic nose could
provide a rapid measurement and therefore early treatment. This research project used
existing sensor technology in the form of an electronic nose in conjunction with data
pre-processing and classification methods to classify up to four bacteria types from
their odours. Research was performed mostly in the area of signal conditioning, data
pre-processing and classification. A major area of interest was the use of artificial neural
networks classifiers. There were three main objectives. First, to classify successfully
a small range of bacteria types. Second, to identify issues relating to bacteria odour
that affect the ability of an artificially intelligent system to classify bacteria from odour
alone. And third, to establish optimal signal conditioning, data pre-processing and
classification methods.
The Electronic Nose consisted of a gas sensor array with temperature and humidity
sensors, signal conditioning circuits, and gas flow apparatus. The bacteria odour was
analysed using an automated sampling system, which used computer software to direct
gas flow through one of several vessels (which were used to contain the odour samples,
into the Electronic Nose. The electrical resistance of the odour sensors were monitored
and output as electronic signals to a computer. The purpose of the automated sampling system was to improve repeatability and reduce human error. Further improvement
of the Electronic Nose were implemented as a temperature control system which controlled
the ambient gas temperature, and a new gas sensor chamber which incorporated
improved gas flow.
The odour data were collected and stored as numerical values within data files in
the computer system. Once the data were stored in a non-volatile manner various classification
experiments were performed. Comparisons were made and conclusions were
drawn from the performance of various data pre-processing and classification methods.
Classification methods employed included artificial neural networks, discriminant
function analysis and multi-variate linear regression. For classifying one from four
types, the best accuracy achieved was 92.78%. This was achieved using a growth phase
compensated multiple layer perceptron. For identifying a single bacteria type from a
mixture of two different types, the best accuracy was 96.30%. This was achieved using
a standard multiple layer perceptron.
Classification of bacteria odours is a typical `real world' application of the kind that
electronic noses will have to be applied to if this technology is to be successful. The
methods and principles researched here are one step towards the goal of introducing
artificially intelligent sensor systems into everyday use. The results are promising and
showed that it is feasible to used Electronic Nose technology in this application and that
with further development useful products could be developed. The conclusion from this
thesis is that an electronic nose can detect and classify different types of bacteria