Bacteria classification using Cyranose 320 electronic nose

Cyranose 320電子鼻技術眼部細菌感染分類鑒定研究

Ritaban Dutta*, Evor L Hines, Julian W Gardner and Pascal Boilot
Address: Division of Electrical and Electronic Engineering, School of Engineering, University of Warwick, Coventry, CV4 7AL, United Kingdom
: Ritaban Dutta* - r.dutta@warwick.ac.uk; Evor L Hines - e.l.hines@warwick.ac.uk; Julian W Gardner - j.w.gardner@warwick.ac.uk;
Pascal Boilot - P.Boilot@warwick.ac.uk
*Corresponding author

Published: 16 October 2002
BioMedical Engineering OnLine 2002, 1:4
Received: 1 September 2002
Accepted: 16 October 2002
This article is available from: /content/1/1/4
© 2002 Dutta et al; licensee BioMed Central Ltd. This article is published in Open Access: verbatim copying and redistribution of this article are permitted
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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.

背景：電子鼻即Cyrano Sciences公司的Cyranose 320，由32個聚合物-碳黑復合傳感器陣列組成，用于識別6種在鹽水溶液中濃度范圍內導致眼睛感染的細菌。通過手動將便攜式電子鼻系統放入含有固定量懸浮細菌的無菌玻璃中，從樣品的頂部空間讀取讀數。收集到的數據是不同化合物的非常復雜的混合物。

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 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.

方法：采用線性主成分分析法(PCA)對六類細菌中的四類進行分類，但實際中，PCA分析的其他兩類細菌分類效果并不明顯，PCA的分類準確率為74%。將三維散點圖、模糊C均值(FCM)和自組織圖(SOM)網絡相結合，研究了一種新的細菌數據聚類方法。同時使用這三種數據聚類算法，可以更好地對六種眼睛細菌進行分類。然后是三個監督分類器，即多層感知器(MLP)、概率神經網絡。采用PNN(PNN)和徑向基函數網絡(RBF)對六種細菌進行分類。結果：采用[6 1]SOM網絡對細菌分類的準確率為96%，是的分類準確率。在此應用中對分類器進行了比較評估。結果表明，應用RBF網絡可以預測六類細菌，準確率高達98%。

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.

結論：這類細菌的數據分析和特征提取非常困難。但是，我們可以得出結論，將三種非線性方法結合使用，可以解決數據非常復雜的特征提取問題，并提高Cyranose 320的性能。