Julian W. Gardner

Handbook of Machine Olfaction

Odors are sensations that occur when compounds (called odorants) stimulate receptors located in the olfactory epithelium at the roof of the nasal cavity. Odorants are hydrophobic, volatile compounds with a molecular weight of less than 300 daltons. Humans can recognize and distinguish up to 10 000 different substances on the basis of their odor quality. Odorant receptors (ORs) in the nasal cavity detect and discriminate among these thousands of diverse chemical ligands. An individual odorant can bind to multiple receptor types, and structurally different odorants can bind to a single receptor. Specific patterns of activation generate signals that allow us to discriminate between the vast number of distinct smells. The physicochemical attributes of odorants that induce specific odor sensations are not well understood. The genes that code for ORs have been cloned, and results from cloning studies indicate that ORs are members of a superfamily of hundreds of different G-protein-coupled receptors that possess seven transmembrane domains. A complete knowledge of structureodor relationships in olfaction awaits the three-dimensional analysis of this large family of ORs. Ultimately, simultaneous knowledge of the three-dimensional structure of ORs as well as odorants will allow us to develop a pattern recognition paradigm that can predict odor quality.

Gas identification by modulating temperatures of SnO2-based thick film sensors

Abstract A new method is presented to identify the presence of two gases in the ambient atmosphere. The method employs only one SnO2-based gas sensor in a sinusoidal temperature mode to perform the quantitative analysis of a binary gas mixture (CO/NO2) in air.

Detection of Vapours and Odours from a Multisensor Array Using Pattern Recognition Part 1. Principal Component and Cluster Analysis

Abstract Mathematical expressions describing the response of individual sensors and arrays of tin oxide gas sensors are derived from a barrier-limited electron mobility model. From these expressions, the fractional change in conductance is identified as the optimal response parameter with which to characterize sensor array performance instead of the more usual relative conductance. In an experimental study, twelve tin oxide gas sensors are exposed to five alcohols and six beverages, and the responses are studied using pattern-recognition methods. Results of regression and supervised learning analysis show a high degree of colinearity in the data with a subset of only five sensors needed for classification. Principal component analysis and clustering methods are applied to the response of the tin oxide sensors to all the vapours. The results show that the theoretically derived normalization of the data set substantially improves the classification of vapours and beverages. The individual alcohols are separated out into five distinct clusters, whereas the beverages cluster into only three distinct classes, namely, beers, lagers and spirits. It is suggested that the separation may be improved further by employing other sensor types or processing techniques.

Sensors and sensory systems for an electronic nose

1. Odours -- The Stimulus for an Electronic Nose G.H. Dodd, P.N. Bartlett, J.W. Gardner. 2. Biophysical Properties of Olfactory Receptor Neurones H.A. Schultens, D. Schild. 3. Molecular Modeling and the Selective Sensor Response M. Thompson, D.C. Strone. 4. Odour Sensors for an Electronic Nose P.N. Bartlett, J.W. Gardner. 5. Fundamentals and Recent Developments of Homogeneous Semiconducting Sensors D. Kohl. 6. Fine-Tuning of Electron- and Ion-Conducting Materials for Sensor Arrays W. Gopel, K-D. Schierbaum, S. Vaihinger, U. Weimar. 7. Microsensors Based on Modulation of Work Function J. Janata. 8. Studies of Interactions Between Organic Vapours and Organic Semiconductors Aplications to Chemical Sensing M. Josowicz, P. Topart. 9. Silicon Based Surface Acoustic Wave Gas Sensors M.S. Nieuwenhuizen, A.J. Nederlof. 10. Miniaturisation of Gas Sensor Substrates. Problems and Benefits of Microelectronic Technology U. Dibbern. 11. Pattern Recognition in Odour Sensing J.W. Gardner, P.N. Bartlett. 12. Desired and Achieved Characteristics of Sensor Arrays G. Horner, R. Muller. 13. Use of Pattern Recognition Techniques Applied to Signals Generated by a Multielement Gas Sensor Array as a Means of Compensating for Poor Individual Element Response A.W.J. Cranny, J.K. Atkinson. 14. Pattern Recognition in Electronic Noses by Artificial Neural Network Models T. Moruzumi, T. Nakamoto, Y. Sakuraba. 15. Sensor Arrays Using Conducting Polymers for an Artificial Nose K.C. Persaud, P. Pelosi. 16. Monitoring ofFish Freshness Using Tin Oxide Sensors R. Olafsson, E. Martinsdottir, G. Olafsdottir, P.I. Sigfusson, J.W. Gardner. 17. Chemical Sensor Arrays: Practical Insights and Examples J.R. Stetter. 18. Electronic Noses Based on Field Effect Structures I. Lundstrom, E. Hedborg, A. Spetz, H. Sundgren, F. Winquist. Index.

Application of an electronic nose to the discrimination of coffees

Abstract An investigation has been carried out into the response of an array of twelve tin oxide sensors to the headspace of coffee packs. Discriminant and classification function analyses are performed on the array response to each of three commercial coffees (covering two different blends and two roasts) as well as one coffee which has been subjected to a range of six roasting times. Multivariate functions are calculated from the entire data set (90 samples) or alternatively using half of it, to permit cross-validation. A success rate of 89.9% is achieved with the former procedure in classifying the three commercial coffee odours directly from the response (change in sensor conductances) of the array. This value falls to 81.1% when half of the data set is used for cross-validation. Preprocessing the array data, by normalizing the response of each sensor over the array, is found to increase the success rate (to 95.5%) on the entire data set only. The effect on coffee odour of a set of six roasting times (zero to 11.5 min) is also investigated and found to be considerable, some sensors registering an increase in conductance by a factor of three. A 100% group classification is achieved with zero and long roasting times, the overall success rate being 88.1%. The main conclusion is that tin oxide gas sensors can be used to discriminate between both the blend and roasting level of coffee, confirming their potential application in an electronic instrument for on-line quantitative process control in the food industry.

Electronic nose for monitoring the flavour of beers

The flavour of a beer is determined mainly by its taste and smell, which is generated by about 700 key volatile and non-volatile compounds. Beer flavour is traditionally measured through the use of a combination of conventional analytical tools (e.g., gas chromatography) and organoleptic profiling panels. These methods are not only expensive and time-consuming but also inexact due to a lack of either sensitivity or quantitative information. In this paper an electronic instrument is described that has been designed to measure the odour of beers and supplement or even replace existing analytical methods. The instrument consists of an array of up to 12 conducting polymers, each of which has an electrical resistance that has partial sensitivity to the headspace of beer. The signals from the sensor array are then conditioned by suitable interface circuitry and processed using a chemometric or neural classifier. The results of the application of multivariate statistical techniques are given. The instrument, or electronic nose, is capable of discriminating between various commercial beers and, more significantly, between standard and artificially-tainted beers. An industrial version of this instrument is now undergoing trials in a brewery.

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.