Bacteria classification using Cyranose 320 electronic nose

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 × 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

Novel intelligent data processing techniques for electronic noses : feature selection and neuro-fuzzy knowledge base

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Electronic noses inter-comparison, data fusion and sensor selection in discrimination of standard fruit solutions.

The paper presents a research that supported real applicability of (4 different, as shown in section EN Systems) Electronic Nose systems. The groups had been researching in the theory of artificial olfaction and with the work reported in this paper we reach a critical waypoint in making all this work useful to the real world using a set of commercial sensors and artificial intelligence data processing techniques. The impact factor of the journal is 2.331

Non-destructive egg freshness determination : an electronic nose based approach

An electronic nose (EN) based system, which employs an array of four inexpensive commercial tin-oxide odour sensors, has been used to analyse the state of freshness of eggs. Measurements were taken from the headspace of four sets of eggs over a period of 20-40 days, two 'types of egg data' being gathered using our EN; one type of 'data' related to eggs without a hole in the shells and the other type of 'data' related to eggs wherein we made tiny holes in the shells. Principal component analysis, fuzzy C means, self-organizing maps and 3D scatter plots were used to define regions of clustering in multisensor space according to the state of freshness of the eggs. These were correlated with the 'use by date' of the eggs. Then four supervised classifiers, namely multilayer perceptron, learning vector quantization, probabilistic neural network and radial basis function network, were used to classify the samples into the three observed states of freshness. A comparative evaluation of the classifiers was conducted for this application. The best results suggest that we are able to predict egg freshness into one of three states with up to 95% accuracy. This shows good potential for commercial exploitation

Apertureless near-field optical microscopy: A study of the local tip field enhancement using photosensitive azobenzene-containing films

International audienceThe local optical field enhancement which can occur at the end of a nanometer-size metallic tip has given rise to both increasing interest and numerous theoretical works on near-field optical microscopy. In this article we report direct experimental observation of this effect and present an extensive study of the parameters involved. Our approach consists in making a “snapshot” of the spatial distribution of the optical intensity in the vicinity of the probe end using photosensitive azobenzene-containing films. This distribution is coded by optically induced surface topography which is characterized in situ by atomic force microscopy using the same probe. We perform an extensive analysis of the influence of several experimental parameters. The results are analyzed as a function of the illumination parameters (features of the incident laser beam, exposure time, illumination geometry) as well as the average tip-to-sample distance and tip geometry. The results obtained provide substantial information about the tip’s field. In particular, they unambiguously demonstrate both the nanometric spatial confinement of the tip field and the evanescent nature of the nanosource excited at the tip’s end. Most of the experimental results are illustrated by numerical calculations based on the finite element method and commented using the literature on the subject. Additionally, we discuss the origin of the optically induced topography on a nanometer scale and present some preliminary results of the apertureless near-field optical lithography based on local field enhancement. Our approach constitutes a useful tool to investigate the near-field of apertureless probes and should enable the optimization of the nanosource for any experiment requiring local optical excitation of the matter