bacteria classification using cyranose 320 electronic nose
cyranose 320电子鼻技术眼部细菌感染分类鉴定研究
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
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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：电子鼻即cyrano sciences公司的cyranose 320，由32个聚合物-碳黑复合传感器阵列组成，用于识别6种在盐水溶液中浓度范围内导致眼睛感染的细菌。通过手动将便携式电子鼻系统放入含有固定量悬浮细菌的无菌玻璃中，从样品的顶部空间读取读数。收集到的数据是不同化合物的非常复杂的混合物。
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：采用线性主成分分析法(pca)对六类细菌中的四类进行分类，但实际中，pca分析的其他两类细菌分类效果并不明显，pca的分类准确率为74%。将三维散点图、模糊c均值(fcm)和自组织图(som)网络相结合，研究了一种新的细菌数据聚类方法。同时使用这三种数据聚类算法，可以更好地对六种眼睛细菌进行分类。然后是三个监督分类器，即多层感知器(mlp)、概率神经网络。采用pnn(pnn)和径向基函数网络(rbf)对六种细菌进行分类。结果：采用[6 1]som网络对细菌分类的准确率为96%，是的分类准确率。在此应用中对分类器进行了比较评估。结果表明，应用rbf网络可以预测六类细菌，准确率高达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.
结论：这类细菌的数据分析和特征提取非常困难。但是，我们可以得出结论，将三种非线性方法结合使用，可以解决数据非常复杂的特征提取问题，并提高cyranose 320的性能。