Fredrik Winquist

Electronic tongues for environmental monitoring based on sensor arrays and pattern recognition: a review

Abstract The use of sensor arrays and pattern recognition applied to the obtained signal patterns for environmental monitoring are discussed in some detail. Different types of electronic tongues are described and evaluated for monitoring purposes. More specifically the performance of multielectrode arrays used for voltammetric analysis of aqueous samples is described. It is, e.g. shown how such an ‘electronic tongue’ can be used to monitor the quality of water in a production plant for drinking water. It is pointed out that the concepts of ‘electronic noses’ and ‘electronic tongues’ often predict a quality of a sample rather than giving exact information about concentrations of individual species.

A hybrid electronic tongue.

Abstract A hybrid electronic tongue is described based on a combination of potentiometry, voltammetry and conductivity. It was used for classification of six different types of fermented milk. Using ion-selective electrodes, pH, carbon dioxide and chloride ion concentrations were measured. The voltammetric electronic tongue consisted of six working electrodes of different metals (gold, iridium, palladium, platinum, rhenium and rhodium) and an Ag/AgCl reference electrode. The measurement principle is based on pulse voltammetry in which current transients are measured due to the onset of voltage pulses at decreasing potentials. The data obtained from the measurements were treated by multivariate data processing based on principal components analysis and an artificial neural net. The hybrid tongue could separate all six samples. Also, the nature of the micro-organisms in the different fermentations was reflected in the principal component analysis.

Performance of an electronic nose for quality estimation of ground meat

An electronic nose is described, which consists of a gas sensor array combined with a pattern recognition routine. The sensor array used consists of ten metal-oxide-semiconductor field effect transistors with gates of catalytically active metals. It also contains four commercially available chemical sensors based on tin dioxide, so-called Taguchi sensors. In some studies, a carbon dioxide monitor based on infrared absorption is also used. Samples of ground beef and pork, stored in a refrigerator, have been studied. Gas samples from the meat were then led to the sensor array, and the resulting patterns of sensor signals were treated with pattern recognition software based on an artificial neural network as well as with an algorithm based on an abductory induction mechanism. When using all sensors for learning, the two nets could predict both type of meat and storage time quite well. Omitting the carbon dioxide monitor, both nets could predict type of meat, but storage time not so well. Finally, it is also shown how a net based on unsupervised training could be used to predict storage time for ground beef.

Some studies of molecularly-imprinted polymer membranes in combination with field-effect devices

Investigations of the usefulness of molecularly-imprinted polymers in sensor applications were undertaken. Thin polymer membranes containing molecular imprints against l-phenylalanine anilide were prepared and applied as sensing layer in field-effect capacitors. In this report some experimental results obtained for C-V measurements when the polymer membranes were exposed to l-phenylalanine anilide (FAA), tyrosinanilide (TA), and phenylalaninol (FA) in ethanol are presented. Despite some difficulties, it was shown that in ethanol solution the compounds FAA and TA can be measured and distinguished from FA using a imprinted polymer membrane.

Modified palladium metal‐oxide‐semiconductor structures with increased ammonia gas sensitivity

It is known that palladium metal‐oxide‐semiconductor (Pd‐MOS) structures are sensitive detectors for hydrogen gas. We show that the evaporation of a thin film of catalytically active metals on top of the structure can increase the sensitivity towards ammonia considerably. It was found that the thin metal must be in contact with the oxide to cause the increased sensitivity. The largest increase was observed with the transition metals Ir and Pt. The ammonia sensitivity could be enhanced about 60 times compared to that of an unmodified structure

Monitoring of freshness of milk by an electronic tongue on the basis of voltammetry

We describe an electronic tongue which consists of a reference electrode, an auxiliary electrode and five wires of different metals (gold, iridium, palladium, platinum and rhodium) as working electrodes. The measurement principle is based on pulsed voltammetry, in which successive voltage pulses of gradually changing amplitudes are applied to the working electrodes connected in a standard three-electrode configuration. The five working electrodes were successively connected and corresponding current-response transients are recorded. The electronic tongue was used to follow the deterioration of the quality of milk due to microbial growth when milk is stored at room temperature. The data obtained were treated with principal component analysis and the deterioration process could clearly be followed in the diagrams. To make models for predictions, projections to latent structure and artificial neural networks were used. When they had been trained, both models could satisfactorily predict the course of bacterial growth in the milk samples.

Artificial neural networks and gas sensor arrays: quantification of individual components in a gas mixture

A very promising way of increasing the selectivity and sensitivity of gas sensors is to treat the signals from a number of different gas sensors with pattern recognition (PARC) methods. A gas sensor array with six metal-oxide-semiconductor field-effect-transistors (MOSFETs) operating at elevated temperatures was exposed to two types of multiple-component gas mixture, one containing 5-65 ppm of hydrogen, ammonia, ethanol and ethylene in air and the other containing hydrogen and acetone in air. The signals from the sensors were analysed with both conventional multivariate analysis, partial least-squares (PLS), and artificial neural network (ANN) models. The results show that both hydrogen and ammonia concentrations can be predicted with PLS models; the predictions were even better with ANN models. The predictions for ethanol and ethylene concentrations were, however, poor for both types of model. Hydrogen and acetone, from the two-component mixture, were best predicted from an ANN model.

Electronic nose for microbial quality classification of grains

The odour of grains is in many countries the primary criterion of fitness for consumption. However, smelling of grain for quality grading should be avoided since inhalation of mould spores or toxins may be hazardous to the health and determinations of the off-odours are subjective. An electronic nose, i.e. a gas sensor array combined with a pattern recognition routine might serve as an alternative. We have used an electronic nose consisting of a sensor array with different types of sensors. The signal pattern from the sensors is collected by a computer and further processed by an artificial neural network (ANN) providing the pattern recognition system. Samples of oats, rye and barley with different odours and wheat with different levels of ergosterol, fungal and bacterial colony forming units (cfu) were heated in a chamber and the gas in the chamber was led over the sensory array. The ANN could predict the odour classes of good, mouldy, weakly and strongly musty oats with a high degree of accuracy. The ANN also indicated the percentage of mouldy barley or rye grains in mixtures with fresh grains. In wheat a high degree of correlation between ANN predictions and measured ergosterol as well as with fungal and bacterial cfu was observed. The electronic nose can be developed to provide a simple and fast method for quality classification of grain and is likely to find applications also in other areas of food mycology.

Gas sensors based on catalytic metal-gate field-effect devices

Abstract The properties of gas-sensitive semiconductor devices with catalytic metal gates are reviewed, with emphasis on field-effect structures sensitive to hydrogen-containing molecules like H 2 , NH 3 , H 2 S, alcohols, ethylene etc. A brief review of some of the developed device structures are given. The principles of hydrogen sensors with Pd gates are described in some detail. Ammonia-sensitive field-effect devices with thin catalytic metal gates are discussed. Applications of gas-sensitive field-effect devices for studies of catalytic reactions together with electron spectroscopy in UHV systems, for medical diagnosis, in leak detectors and as biosensors are reviewed.

Drift counteraction in odour recognition applications: lifelong calibration method

Sensor drift is addressed as one of the most serious impairments afflicting chemical and biochemical sensors. One possible solution to this problem is to view sensor arrays as time-varying dynamic systems, whose variations have to be tracked by adaptive estimation algorithms. A theory of hidden variable dynamics for the rejection of common mode drifting of sensors has previously been developed and is here coupled with a recursive least squares algorithm. In Section 7 a smart solution is provided for a difficult vapour recognition problem vexed by drift that have failed with traditional pattern recognition techniques. Among the many advantages we distinguish that model adaptation to changes in the sensor array makes lifelong calibration possible without interrupting the operation of the array.