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

Particle Sensor Using Solidly Mounted Resonators

This paper describes the development of a novel particle sensing system employing zinc oxide based solidly mounted resonator (SMR) devices for the detection of airborne fine particles (i.e., PM2.5 and PM10). The system operates in a dual configuration in which two SMR devices are driven by Colpitts-type oscillators in a differential mode. Particles are detected by the frequency shift caused by the mass of particles present on one resonator with while the other acts as a reference channel. Experimental validation of the system was performed inside an environmental chamber using a dust generator with the particles of known size and concentration. A sensor sensitivity of 4.6 Hz per μg/m3 was demonstrated for the SMRs resonating at a frequency of 970 MHz. Our results demonstrate that the SMR-based system has the potential to be implemented in CMOS technology as a low-cost, miniature smart particle detector for the real-time monitoring of airborne particles

Graphene-coated Rayleigh SAW resonators for NO2detection

This paper describes the development of a novel low-cost Rayleigh Surface Acoustic Wave Resonator (SAWR) device coated with a graphene layer that is capable of detecting PPM levels of NO2 in air. The sensor comprises two 262 MHz ST-cut quartz based Rayleigh SAWRs arranged in a dual oscillator configuration; where one resonator is coated with gas-sensitive graphene, and the other left uncoated to act as a reference. An array of NMP-dispersed exfoliated reduced graphene oxide dots was deposited in the active area inside the SAWR IDTs by a non-contacting, micro ink-jet printing system. An automated Mass Flow Controller system has been developed that delivers gases to the SAWR sensors with circuitry for excitation, amplification, buffering and signal read-out. This SAW-based graphene sensor has sensitivity to NO2 of ca. 25 Hz/ppm and could be implemented in a low-power low-cost gas sensor

Estimation of missing air pollutant data using a spatiotemporal convolutional autoencoder

A key challenge in building machine learning models for time series prediction is the incompleteness of the datasets. Missing data can arise for a variety of reasons, including sensor failure and network outages, resulting in datasets that can be missing significant periods of measurements. Models built using these datasets can therefore be biased. Although various methods have been proposed to handle missing data in many application areas, more air quality missing data prediction requires additional investigation. This study proposes an autoencoder model with spatiotemporal considerations to estimate missing values in air quality data. The model consists of one-dimensional convolution layers, making it flexible to cover spatial and temporal behaviours of air contaminants. This model exploits data from nearby stations to enhance predictions at the target station with missing data. This method does not require additional external features, such as weather and climate data. The results show that the proposed method effectively imputes missing data for discontinuous and long-interval interrupted datasets. Compared to univariate imputation techniques (most frequent, median and mean imputations), our model achieves up to 65% RMSE improvement and 20–40% against multivariate imputation techniques (decision tree, extra-trees, k-nearest neighbours and Bayesian ridge regressors). Imputation performance degrades when neighbouring stations are negatively correlated or weakly correlated

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Deploying chemosensor arrays in close proximity to stationary phases imposes stimulusdependent spatio-temporal dynamics on their response and leads to improvements in complex odour discrimination. These spatio-temporal dynamics need to be taken into account explicitly when considering the detection performance of this new odour sensing technology, termed an artificial olfactory mucosa. For this purpose, we develop here a new measure of spatio-temporal information that combined with an analytical model of the artificial mucosa, chemosensor and noise dynamics completely characterizes the discrimination capability of the system. This spatio-temporal information measure allows us to quantify the contribution of both space and time to discrimination performance and may be used as part of optimization studies or calculated directly from an artificial mucosa output. Our formal analysis shows that exploiting both space and time in the mucosa response always outperforms the use of space alone and is further demonstrated by comparing the spatial versus spatio-temporal information content of mucosa experimental data. Together, the combination of the spatio-temporal information measure and the analytical model can be applied to extract the general principles of the artificial mucosa design as well as to optimize the physical and operating parameters that determine discrimination performance

Mobile robot multi-sensor unit for unsupervised gas discrimination in uncontrolled environments

In this work we present a novel multi-sensor unit to detect and discriminate unknown gases in uncontrolled environments. The unit includes three metal oxide (MOX) sensors with CMOS micro heaters, a plasmonic enhanced non-dispersive infra-red (NDIR) sensor, a commercial temperature humidity sensor, and a flow sensor. The proposed sensing unit was evaluated with plumes of gases (propanol, ethanol and acetone) in both, a laboratory setup on a gas testing bench and on-board a mobile robot operating in an indoor workshop. It offers significantly improved performance compared to commercial systems, in terms of power consumption, response time and physical size. We verified the ability to discriminate gases in an unsupervised manner, with data collected on the robot and high accuracy was obtained in the classification of propanol versus acetone (96%), and ethanol versus acetone (90%).SmokeBo