Bacteria classification using Cyranose 320 electronic nose

Despite the robustness of the eye, there is no doubt that it is exposed to a harsh environment where it is continually in contact with infectious airborne organisms. The function of the eyelids and production of tears help to protect the eye. However the warm, moist, enclosed environment, which exists between the surface of the eye (conjunctiva) and the eyelids, also provides an environment in which contaminating bacteria can establish an infection. The most common bacterial eye infection is conjunctivitis and organisms such as Staphylococcus aureus, Haemophilus influenzae, Streptococcus pneumoniae, Escherichia coli have been associated with this condition [1]. The number of organisms responsible for infection of the eye is relatively small; nevertheless the consequences are always potentially serious as the eye may become irreversibly damaged. Rapid diagnosis is therefore essential but currently relies on time-consuming isolation and culture of the infectious agent, and use of precise analytical instruments (e.g. liquid chromatography or optical microscopy). Since it is very important that the nature of the infection is diagnosed as quickly as possible, it is clear that techniques such as a neural network based e-nose, which can almost instantly detect and classify odorous volatile components, could make a major contribution [2]. The term electronic nose (e-nose) describes an electronic system that is able to mimic the human sense of smell. These systems have been the subject of much research at the University of Warwick over the past 20 years or so.

E-nose systems use a number of different gas sensors depending on the application, e.g. metal oxide chemoresistors, conducting polymer chemoresistors, etc. Other aroma-based techniques exist, however while gas chromatograph or mass spectrometry techniques can be used to separate, quantify and identify individual volatile chemicals, they do not indicate whether the compounds contain an odour or not. Therefore e-noses have been developed to improve on and to complement these techniques, and thus provide a better emulation of the human system for sensory analysis. Researchers are currently developing a new generation of artificial e-nose in order to build smaller and cheaper systems that thus will find application in the consumer marketplace. Research also focuses on the data processing aspects, exploring possibilities to integrate new techniques such as neural networks, fuzzy logic and genetic algorithms in order to develop the intelligent e-nose. Nearly twenty years of development, e-nose technology has been applied in various fields such as the food, drinks and cosmetic industries. More recently research has been directed towards health and safety issues [3], for example in the medical arena and medical diagnosis, food quality and control, environmental monitoring. E-nose systems have already been used with success in the medical domain [4], for microbial detection [2], and bioprocess monitoring [5]. In this paper we describe the use of Cyrano Sciences' Cyranose 320 to identify six species of bacteria, which are believed to be responsible for eye infections, when present at a range of concentrations in saline solutions.

This type of bacteria data analysis and feature extraction is very difficult. We can conclude that this combined use of three nonlinear methods (3D-Scatter plot, SOM, FCM) can solve the feature extraction problem with very complex data and enhance the performance of Cyranose 320. Later on two supervised ANN classifiers, PNN and RBF, were able to predict the six different bacteria classes with 94% and 98% accuracy respectively; where the training of the supervised ANN classifiers were performed using 40% of the whole data set for the six bacteria. Linear 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, FCM and SOM network. Using these three data clustering algorithms simultaneously better 'classification' of six eye bacteria classes were represented. A [6 × 1] SOM network gave 96% accuracy for bacteria classification which was best accuracy. Then three supervised classifiers, namely Multi Layer Perce ptron (MLP), Probabilistic Neural network (PNN) and Radial basis function network (RBF), were used to classify the six bacteria classes. 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. So from these results we can conclude that in future we can create a 'knowledge base of extracted features' by applying three nonlinear methods like 3D-Scatter plot, SOM and FCM for each bacteria class. So in future if we have an input dataset from unknown bacteria, by applying these three methods in a combined manner we can extract some feature for that unknown class of bacteria; later on we can match with the existing knowledge base of classes of bacteria features to predict the bacteria class. For this matching purpose, supervised ANN classifiers like PNN or RBF can be used with very high accuracy. This type of bacteria data analysis and feature extraction is very difficult. But we can conclude that this combined use of three nonlinear methods along with RBF neural network can solve the feature extraction problem with very complex data and enhance the performance of Cyranose 320.