کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
10412855 | 895266 | 2005 | 8 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Identification of Staphylococcus aureus infections in hospital environment: electronic nose based approach
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کلمات کلیدی
Electronic nose (e-nose)Methicillin-resistant S. aureus (MRSA)Principal component analysis (PCA) - آنالیز اجزا اصلیStaphylococcus aureus - استافیلوکوک اورئوسProbabilistic neural network (PNN) - شبکه عصبی احتمالی (PNN)Self-organizing map (SOM) - نقشه خودمراقبتی (SOM)Multi-layer perceptron (MLP) - چند لایه ی perceptron (MLP)
موضوعات مرتبط
مهندسی و علوم پایه
شیمی
شیمی آنالیزی یا شیمی تجزیه
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
An electronic nose (e-nose), the Cyrano Sciences' Cyranose 320 (C-320), comprising an array of 32 polymer carbon black composite sensors has been used to identify two species of Staphylococcus aureus bacteria, namely methicillin-resistant S. aureus (MRSA) and methicillin-susceptible S. aureus (MSSA) responsible for ear nose and throat (ENT) infections when present in standard agar solution in the hospital environment. C-320 e-nose has also been used to identify coagulase-negative staphylococci (C-NS) in the hospital environment. Swab samples were collected from the infected areas of the ENT patients' ear, nose and throat regions. Gathered data were a very complex mixture of different chemical compounds. An innovative object-oriented data clustering approach was investigated for these groups of S. aureus data by combining the principal component analysis (PCA) based three-dimensional scatter plot, Fuzzy C Means (FCM) and self-organizing map (SOM) network. Using these three data clustering algorithms simultaneously better 'classification' of three bacteria subclasses 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 three classes. A comparative evaluation of the classifiers was conducted for this application. The best results suggest that we are able to identify three bacteria subclasses with up to 99.69% accuracy with the application of the RBF network along with C-320. This type of bacteria data analysis and feature extraction is very difficult. But we can conclude that this preliminary study proves that e-nose based approach can provide very strong solution for identifying S. aureus infections in hospital environment and early detection.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Sensors and Actuators B: Chemical - Volume 109, Issue 2, 14 September 2005, Pages 355-362
Journal: Sensors and Actuators B: Chemical - Volume 109, Issue 2, 14 September 2005, Pages 355-362
نویسندگان
Ritaban Dutta, David Morgan, Nicky Baker, Julian W. Gardner, Evor L. Hines,