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Bacteria classification using Cyranose 320 electronic nose.

Dutta R, Hines EL, Gardner JW, Boilot P - Biomed Eng Online (2002)

Bottom Line: Gathered data were a very complex mixture of different chemical compounds.A comparative evaluation of the classifiers was conducted for this application.But we can conclude that this combined use of three nonlinear methods can solve the feature extraction problem with very complex data and enhance the performance of Cyranose 320.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Electrical and Electronic Engineering, School of Engineering, University of Warwick, Coventry, CV4 7AL, United Kingdom. r.dutta@warwick.ac.uk

ABSTRACT

Background: An electronic nose (e-nose), the Cyrano Sciences' Cyranose 320, comprising an array of thirty-two polymer carbon black composite sensors has been used to identify six species of bacteria responsible for eye infections when present at a range of concentrations in saline solutions. Readings were taken from the headspace of the samples by manually introducing the portable e-nose system into a sterile glass containing a fixed volume of bacteria in suspension. Gathered data were a very complex mixture of different chemical compounds.

Method: Linear Principal Component Analysis (PCA) method was able to classify four classes of bacteria out of six classes though in reality other two classes were not better evident from PCA analysis and we got 74% classification accuracy from PCA. An innovative data clustering approach was investigated for these bacteria data by combining the 3-dimensional scatter plot, Fuzzy C Means (FCM) and Self Organizing Map (SOM) network. Using these three data clustering algorithms simultaneously better 'classification' of six eye bacteria classes were represented. Then three supervised classifiers, namely Multi Layer Perceptron (MLP), Probabilistic Neural network (PNN) and Radial basis function network (RBF), were used to classify the six bacteria classes.

Results: A [6 x 1] SOM network gave 96% accuracy for bacteria classification which was best accuracy. A comparative evaluation of the classifiers was conducted for this application. The best results suggest that we are able to predict six classes of bacteria with up to 98% accuracy with the application of the RBF network.

Conclusion: This type of bacteria data analysis and feature extraction is very difficult. But we can conclude that this combined use of three nonlinear methods can solve the feature extraction problem with very complex data and enhance the performance of Cyranose 320.

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Related in: MedlinePlus

Plots of the results from PCA for six classes of bacteria.
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Figure 3: Plots of the results from PCA for six classes of bacteria.

Mentions: PCA is an effective linear method for discriminating between the e-nose responses to simple and complex odours [8]. The method consists of expressing the response vectors in terms of a linear combination of orthogonal vectors that account for a certain amount of variance in the data. The results of the PCA are shown in Figure 3. Three principal components were kept, which accounted for 99.87% of the variance (PC 1, PC 2 and PC 3 representing respectively 98.82%, 0.94% and 0.11%). 6 categories or clusters appear to be evident. The six clusters formed match the six types of bacteria so that the bacteria were completely separated in the principal component space but classification accuracy was upto 74%. From PCA analysis four classes of bacteria namely Staphylococcus aureus (sar), Haemophilus influenzae (hai), Streptococcus pneumoniae (stp) and Moraxella catarrhalis (moc) were properly classified though other two most common classes of eye bacteria like Escherichia coli (eco), Pseudomonas aeruginosa (psa) were not properly classified (see Figure 3). Most of the variance in the data is explained by considering only the first principal component (PC1), which implies that the sensor responses are highly correlated. As PC1 accounts for most of the information in the data, this suggests that the clusters were not made any more evident using PCA. That is linear PCA analysis is not informative for this type of data. The objective of this analysis was to establish simple classes for the different bacteria species in order to examine whether or not the data clusters could be separated, prior to the conventional pattern recognition stage.


Bacteria classification using Cyranose 320 electronic nose.

Dutta R, Hines EL, Gardner JW, Boilot P - Biomed Eng Online (2002)

Plots of the results from PCA for six classes of bacteria.
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC149373&req=5

Figure 3: Plots of the results from PCA for six classes of bacteria.
Mentions: PCA is an effective linear method for discriminating between the e-nose responses to simple and complex odours [8]. The method consists of expressing the response vectors in terms of a linear combination of orthogonal vectors that account for a certain amount of variance in the data. The results of the PCA are shown in Figure 3. Three principal components were kept, which accounted for 99.87% of the variance (PC 1, PC 2 and PC 3 representing respectively 98.82%, 0.94% and 0.11%). 6 categories or clusters appear to be evident. The six clusters formed match the six types of bacteria so that the bacteria were completely separated in the principal component space but classification accuracy was upto 74%. From PCA analysis four classes of bacteria namely Staphylococcus aureus (sar), Haemophilus influenzae (hai), Streptococcus pneumoniae (stp) and Moraxella catarrhalis (moc) were properly classified though other two most common classes of eye bacteria like Escherichia coli (eco), Pseudomonas aeruginosa (psa) were not properly classified (see Figure 3). Most of the variance in the data is explained by considering only the first principal component (PC1), which implies that the sensor responses are highly correlated. As PC1 accounts for most of the information in the data, this suggests that the clusters were not made any more evident using PCA. That is linear PCA analysis is not informative for this type of data. The objective of this analysis was to establish simple classes for the different bacteria species in order to examine whether or not the data clusters could be separated, prior to the conventional pattern recognition stage.

Bottom Line: Gathered data were a very complex mixture of different chemical compounds.A comparative evaluation of the classifiers was conducted for this application.But we can conclude that this combined use of three nonlinear methods can solve the feature extraction problem with very complex data and enhance the performance of Cyranose 320.

View Article: PubMed Central - HTML - PubMed

Affiliation: Division of Electrical and Electronic Engineering, School of Engineering, University of Warwick, Coventry, CV4 7AL, United Kingdom. r.dutta@warwick.ac.uk

ABSTRACT

Background: An electronic nose (e-nose), the Cyrano Sciences' Cyranose 320, comprising an array of thirty-two polymer carbon black composite sensors has been used to identify six species of bacteria responsible for eye infections when present at a range of concentrations in saline solutions. Readings were taken from the headspace of the samples by manually introducing the portable e-nose system into a sterile glass containing a fixed volume of bacteria in suspension. Gathered data were a very complex mixture of different chemical compounds.

Method: Linear Principal Component Analysis (PCA) method was able to classify four classes of bacteria out of six classes though in reality other two classes were not better evident from PCA analysis and we got 74% classification accuracy from PCA. An innovative data clustering approach was investigated for these bacteria data by combining the 3-dimensional scatter plot, Fuzzy C Means (FCM) and Self Organizing Map (SOM) network. Using these three data clustering algorithms simultaneously better 'classification' of six eye bacteria classes were represented. Then three supervised classifiers, namely Multi Layer Perceptron (MLP), Probabilistic Neural network (PNN) and Radial basis function network (RBF), were used to classify the six bacteria classes.

Results: A [6 x 1] SOM network gave 96% accuracy for bacteria classification which was best accuracy. A comparative evaluation of the classifiers was conducted for this application. The best results suggest that we are able to predict six classes of bacteria with up to 98% accuracy with the application of the RBF network.

Conclusion: This type of bacteria data analysis and feature extraction is very difficult. But we can conclude that this combined use of three nonlinear methods can solve the feature extraction problem with very complex data and enhance the performance of Cyranose 320.

Show MeSH
Related in: MedlinePlus