Evor L. Hines

An electronic nose system to diagnose illness

Recently, medical diagnostics has emerged to be a promising application area for electronic noses (e-nose). In this paper, we review work carried out at Warwick University on the use of an e-nose to diagnose illness. Specifically, we have applied an e-nose to the identification of pathogens from cultures and diagnosing illness from breath samples. These initial results suggest that an e-nose will be able to assist in the diagnosis of diseases in the near future.

The prediction of bacteria type and culture growth phase by an electronic nose with a multi-layer perceptron network

An investigation into the use of an electronic nose to predict the class and growth phase of two potentially pathogenic micro-organisms, Eschericha coli ( E. coli) and Staphylococcus aureus ( S. aureus), has been performed. In order to do this we have developed an automated system to sample, with a high degree of reproducibility, the head space of bacterial cultures grown in a standard nutrient medium. Head spaces have been examined by using an array of six different metal oxide semiconducting gas sensors and classified by a multi-layer perceptron (MLP) with a back-propagation (BP) learning algorithm. The performance of 36 different pre-processing algorithms has been studied on the basis of nine different sensor parameters and four different normalization techniques. The best MLP was found to classify successfully 100% of the unknown S. aureus samples and 92% of the unknown E. coli samples, on the basis of a set of 360 training vectors and 360 test vectors taken from the lag, log and stationary growth phases. The real growth phase of the bacteria was determined from optical cell counts and was predicted from the head space samples with an accuracy of 81%. We conclude that these results show considerable promise in that the correct prediction of the type and growth phase of pathogenic bacteria may help both in the more rapid treatment of bacterial infections and in the more efficient testing of new anti-biotic drugs.

Application of artificial neural networks to an electronic olfactory system

The human sense of smell is the faculty upon which many industries rely to monitor items such as beverages, food and perfumes. Previous work has been carried out to construct an instrument that mimics the remarkable capabilities of the human olfactory system. The instrument or electronic nose consists of a computer-controlled multi-sensor array which exhibits a differential response to a range of vapours and odours. The authors report on a novel application of artificial neural networks (ANNS) to the processing of data gathered from the integrated sensor array or electronic nose. This technique offers several advantages, such as adaptability, fault tolerance, and potential for hardware implementation over conventional data processing techniques. Results of the classification of the signal spectra measured from several alcohols are reported and they show considerable promise for the future application of ANNS within the field of sensor array processing.

Tea quality prediction using a tin oxide-based electronic nose: an artificial intelligence approach

In this paper, we have (analyzed using a metal oxide sensor (MOS)-based electronic nose (EN)) five tea samples with different qualities, namely, drier month, drier month again over-fired, well-fermented normal fired in oven, well-fermented over-fired in oven, and under-fermented normal fired in oven. The flavour of tea is determined mainly by its taste and smell, which are determined by hundreds of volatile organic compounds (VOC) and non-volatile organic compounds present in tea. Tea flavour is traditionally measured through the use of a combination of conventional analytical instrumentation and human organoleptic profiling panels. These methods are expensive in terms of for example time and labour. The methods are also inaccurate because of a lack of either sensitivity or quantitative information. In this paper an investigation has been made to determine the flavours of different tea samples using an EN and thus to explore the possibility of replacing existing analytical and profiling panel methods. The technique uses an array of four MOSs, each of, which has an electrical resistance that has partial sensitivity to the headspace of tea. The signals from the sensor array are then conditioned by suitable interface circuitry resulting in our tea data-set. The data were processed using principal component analysis (PCA), fuzzy C means (FCM) algorithm. The data were then analyzed following the neural network paradigms, following the self-organizing map (SOM) method along with radial basis function (RBF) network and probabilistic neural network (PNN) classifier. Using FCM and SOM feature extraction techniques along with RBF neural network, we achieved 100% correct classification for the five different tea samples, each of which have different qualities. These results prove that our EN is capable of discriminating between the flavours of teas manufactured under different processing conditions, viz. over-fermented, over-fired, under-fermented, etc.

Non-destructive banana ripeness determination using a neural network-based electronic nose

An electronic nose based system, which employs an array of inexpensive commercial tin-oxide odour sensors, has been used to analyse the state of ripeness of bananas. Readings were taken from the headspace of three sets of bananas during ripening over a period of 8-14 days. A principal-components analysis and investigatory techniques were used to define seven distinct regions in multisensor space according to the state of ripeness of the bananas, predicted from a classification of banana-skin colours. Then three supervised classifiers, namely Fuzzy ARTMAP, LVQ and MLP, were used to classify the samples into the observed seven states of ripeness. It was found that the Fuzzy ARTMAP and LVQ classifiers outperformed the MLP classifier, with accuracies of 90.3% and 92%, respectively, compared with 83.4%. Furthermore, these methods were able to predict accurately the state of ripeness of unknown sets of bananas with almost the same accuracy, i.e. 90%. Finally, it is shown that the Fuzzy ARTMAP classifier, unlike LVQ and MLP, is able to perform efficient on-line learning in this application without forgetting previously learnt knowledge. All of these characteristics make the Fuzzy-ARTMAP-based electronic nose a very attractive instrument with which to determine non-destructively the state of ripeness of fruit.

Detection of vapours and odours from a multisensor array using pattern-recognition techniques Part 2. Artificial neural networks

Abstract Considerable interest has recently arisen in the use of arrays of gas sensors together with an associated pattern-recognition technique to identfy vapours and odours. The performance of the pattern-recognition technique depends upon the choice of parametric expression used to define the array output. At present, there is no generally agreed choice of this parameter for either individual sensors or arrays of sensors. In this paper, we have initially performed a parametric study on experimental data gathered from the response of an array of twelve tin oxide gas sensors to five alcohols and three beers. Five parametric expressions of sensor response are used to characterize the array output, namely, fractional conductance change, relative conductance, log of conductance change and normalized versions of the last two expressions. Secondly, we have applied the technique of artificial neural networks (ANNs) to our preprocessed data. The Rumelhart back-propagation technique is used to train all networks. We find that nearly all of our ANNs can correctly identify all the alcohols using our array of twelve tin oxide sensors and so we use the total sum of squared network errors to determine their relative performance. It is found that the lowest network error occurs for the response parameter defined as the fractional change in conductance, with a value of 1.3 × 10−4, which is almost half that for the relative conductance. The normalized procedure is also found to improve network performance and so is worthwhile. The optimal network for our data-set is found to contain a single hidden layer of seven elements with a learning rate of 1.0 and momentum term of 0.7, rather than the values of 0.9 and 0.6 recommended by Rumelhart and McClelland, respectively. For this network, the largest output error is less than 0.1. We find that this network outperforms principal-component and cluster analyses (discussed in Part 1) by identifying similar beer odours and offers considerable benefit in its ability to cope with non-linear and highly correlated data.

Fuzzy neural computing of coffee and tainted-water data from an electronic nose

Abstract In this paper we compare the ability of a fuzzy neural network and a common back-propagation network to classify odour samples that were obtained by an electronic nose employing semiconducting oxide conductometric gas sensors. Two different sample sets have been analysed: first, the aroma of three blends of commercial coffee, and secondly, the headspace of six different tainted-water samples. The two experimental data sets provide an excellent opportunity to test the ability of a fuzzy neural network due to the high level of sensor variability often experienced with this type of sensor. Results are presented on the application of three-layer fuzzy neural networks to electronic nose data. They demonstrate a considerable improvement in performance compared to a common back-propagation network.

Effect of Primary User Traffic on Sensing-Throughput Tradeoff for Cognitive Radios

The effect of the primary user traffic on the performance of the secondary network is investigated for the tradeoff between the sensing quality and the achievable throughput. Numerical results show that the actual secondary network performance when the random departure or arrival of the primary user is taken into account is worse than the predicted secondary network performance in the literature assuming constant occupancy state of the primary user. The degree of degradation depends on the traffic intensity as well as the received signal-to-noise ratio at the secondary user. Also, unlike the conventional model where the occupancy state of the primary user is assumed constant, the optimal sensing time in the new model varies for different primary channel conditions when the primary user traffic is considered.

Electronic nose based tea quality standardization

In this paper we have used a metal oxide sensor (MOS) based electronic nose (EN) to analyze five tea samples with different qualities, namely, drier month, drier month again over-fired, well fermented normal fired in oven, well fermented overfired in oven, and under fermented normal fired in oven. The flavour of tea is determined mainly by its taste and smell, which is generated by hundreds of Volatile Organic Compounds (VOCs) and Non-Volatile Organic Compounds present in tea. These VOCs are present in different ratios and determine the quality of the tea. For example Assamica (Sri Lanka and Assam Tea) and Assamica Sinesis (Darjeeling and Japanese Tea) are two different species of tea giving different flavour notes. Tea flavour is traditionally measured through the use of a combination of conventional analytical instrumentation and human or ganoleptic profiling panels. These methods are expensive in terms of time and labour and also inaccurate because of a lack of either sensitivity or quantitative information. In this paper an investigation has been made to determine the flavours of different tea samples using an EN and to explore the possibility of replacing existing analytical and profiling panel methods. The technique uses an array of 4 MOSs, each of, which has an electrical resistance that has partial sensitivity to the headspace of tea. The signals from the sensor array are then conditioned by suitable interface circuitry. The data were processed using Principal Components Analysis (PCA), Fuzzy C Means algorithm (FCM). We also explored the use of a Self-Organizing Map (SOM) method along with a Radial Basis Function network (RBF) and a Probabilistic Neural Network classifier. Using FCM and SOM feature extraction techniques along with RBF neural network we achieved 100% correct classification for the five different tea samples with different qualities. These results prove that our EN is capable of discriminating between the flavours of teas manufactured under different processing conditions, viz. over-fermented, over-fired, under fermented, etc.

Fuzzy ARTMAP based electronic nose data analysis

Abstract The Fuzzy ARTMAP neural network is a supervised pattern recognition method based on fuzzy adaptive resonance theory (ART). It is a promising method since Fuzzy ARTMAP is able to carry out on-line learning without forgetting previously learnt patterns (stable learning), it can recode previously learnt categories (adaptive to changes in the environment) and is self-organising. This paper presents the application of Fuzzy ARTMAP to odour discrimination with electronic nose (EN) instruments. EN data from three different datasets, alcohol, coffee and cows breath (in order of complexity) were classified using Fuzzy ARTMAP. The accuracy of the method was 100% with alcohol, 97% with coffee and 79%, respectively. Fuzzy ARTMAP outperforms the best accuracy so far obtained using the back-propagation trained multilayer perceptron (MLP) (100%, 81% and 68%, respectively). The MLP being by far the most popular neural network method in both the field of EN instruments and elsewhere. These results, in the case of alcohol and coffee, are better than those obtained using self-organising maps, constructive algorithms and other ART techniques. Furthermore, the time necessary to train Fuzzy ARTMAP was typically one order of magnitude faster than back-propagation. The results show that this technique is very promising for developing intelligent EN equipment, in terms of its possibility for on-line learning, generalisation ability and ability to deal with uncertainty (in terms of measurement accuracy, noise rejection, etc.).