Electronic nose based bacteria species detection in diabetic foot infection
This thesis presents a fundamental study of early bacteria detection using electronic nose. There is a need for early detection of bacterial infection in order to give effective treatment for diabetic foot infection. To date, the clinical method based on sample culture is a standard practise used b...
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| Format: | Thesis |
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| Language: | English |
| Subjects: | |
| Online Access: | http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77967/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77967/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/77967/4/Nurlisa%20Yusof.pdf |
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| Summary: | This thesis presents a fundamental study of early bacteria detection using electronic nose. There is a need for early detection of bacterial infection in order to give effective treatment for diabetic foot infection. To date, the clinical method based on sample
culture is a standard practise used by microbiologist to detect and classify bacteria species. The cultured samples were taken from debridement of diabetic foot wound can take up to two to three days. Alternatively, identification of causative bacteria from their
odours could provide an early and rapid diagnosis and therefore allow initiating
appropriate treatment. This research project used an existing sensor technology in the
form of an e-nose in conjunction with data processing and classification methods to
classify six types of bacteria, common causal organism of diabetic foot infection from
their odours. There were three main aims in this research study namely, to identify
different bacteria species in different culture media using e-nose, investigate the ability
of the e-nose to detect cultured bacteria species in blood agar medium in less than 24
hours and study in-vitro diagnosis of single and poly microbial species targeted for
diabetic foot infection using e-nose. Cyranose 320 e-nose device which consist of 32
gas sensor array, measures the changes in resistance of each chemical sensor which can
detect and classify bacteria according to their volatile organic compound (VOC). The
sniffing process or e-nose measurements were performed immediately after placing the
petri dish of bacteria suspension in a special stainless steel container. The odour data
were collected and stored as numerical values within data files in the computer system.
Once the dataset extracted, various classification experiments were performed.
Comparisons were made and conclusions were drawn from the performance of various
data analysis and classification methods. The classification methods used in this work
include Support Vector Machine (SVM), K Nearest Neighbor (KNN), Linear
Discriminant Analysis (LDA) and Probability Neural Network (PNN). 100% accuracy
was achieved using all classifiers for identification of bacteria species in three different
culture media. The results confirmed that possible to discriminate different bacterial
groups on diabetic foot infection regardless of different culture media used for bacteria
growth. For early detection of six bacterial species, the best accuracy was 96 %. This
was achieved using KNN with k value of 2 and 6 using Euclidean and City block
distance. For study in-vitro diagnosis of single and poly microbial species, the best
accuracy was up to 90 % for all classifiers. Thus, this fundamental work on the
classification of bacteria odours using e-nose can be a „real world‟ application if this
technology is successfully developed. The methods and techniques discussed here are
one step towards the goal of introducing multi class sensor systems into everyday use.
The conclusion of this thesis is that an e-nose can detect and classify different types of
bacteria on diabetic foot infection with convincing results which are comparable to the
existing standard procedure. |
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