E. Llobet

Qualitative and quantitative analysis of volatile organic compounds using transient and steady-state responses of a thick-film tin oxide gas sensor array

Quantitative analysis of gases, by means of semiconductor sensor arrays and pattern-recognition techniques such as artificial neural networks, has been the goal of a great deal of work over the last few years. However, the lack of selectivity, repeatability and drifts of the sensors, have limited the applications of these systems to qualitative or semi-quantitative gas analysis. While the steady-state response of the sensors is usually the signal to be processed in such analysis systems, our method consists of processing both, transient and steady-state information. The sensor transient behaviour is characterised through the measure of its conductance rise time (Tr), when there is a step change in the gas concentration. Tr is characteristic of each gas/sensor pair, concentration-independent and shows higher repeatability than the steady state measurements. An array of four thick-film tin oxide gas sensors and pattern-recognition techniques are used to discriminate and quantify among ethanol, toluene and o-xylene [concentration range: 25, 50 and 100 ppm]. A principal component analysis is carried out to show qualitatively that selectivity improves when the sensor behaviour is dynamically characterised. The steady-state and transient conductance of the array components are processed with artificial neural networks. In a first stage, a feed-forward back-propagation-trained ANN discriminates among the studied compounds. Afterwards, three separate ANN (one for each vapour) are used to quantify the previously identified compound. Processing data from the dynamic characterisation of the sensor array, considerably improves its identification performance, rising the discrimination success rate from a 66% when only steady-state signals are used up to 100%.

Gas Sensing with Au-Decorated Carbon Nanotubes

The sensing properties of carbon nanotubes (CNTs) decorated with gold nanoparticles have been investigated by means of combined theoretical and experimental approaches. On one hand, first-principles and nonequilibrium Greens functions techniques give access to the microscopic features of the sensing mechanisms in individual nanotubes, such as electronic charge transfers and quantum conductances. On the other hand, drop coating deposition of carbon nanotubes decorated with gold nanoparticles onto sensor substrates and their characterization in the detection of pollutants such as NO(2), CO, and C(6)H(6) provide insight into the sensing ability of nanotube mats. Using the present combined approaches, the improvement in the detection of some specific gases (NO(2) and CO) using Au-functionalized nanotubes is explained. However, for other gases such as C(6)H(6), the Au nanoparticles do not seem to play a crucial role in the sensing process when compared with pristine CNTs functionalized with oxygen plasma. Indeed, these different situations can be explained by identifying the relationship between the change of resistance (macroscopic feature) and the shift of the Fermi level (microscopic feature) after gas adsorption. The understanding of the sensing ability at the atomic level opens the way to design new gas sensors and to tune their selectivity by predicting the nature of the metal that is the most appropriate to detect specific molecular species.

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.

Fruit ripeness monitoring using an Electronic Nose

Abstract In this work, the use of an Electronic Nose for non-destructively monitoring the fruit ripening process is presented. Based on a tin oxide chemical sensor array and neural network-based pattern recognition techniques, the olfactory system designed is able to classify fruit samples into three different states of ripeness (green, ripe and overripe) with very good accuracy. Measures done with peaches and pears show a success rate above 92%, while a slightly worse accuracy is reached with apples. An additional feature of the system is its ability to accurately predict the number of days the fruit has been in storage since harvest. Measures done with peaches show a maximum error of 1 day.

Fabrication of Highly Selective Tungsten Oxide Ammonia Sensors

Most of the chemical sensors described in the literature are based on inorganic semiconducting oxides. In some cases, the main oxide is modified by doping with small amounts of additives such as other oxides and/or metals. The sensing principle is based on the change in the conductance undergone by the oxide film when gases are adsorbed and react on its surface. A survey of typical sensor materials for detecting different gases in characteristic temperature ranges can be found elsewhere. 1,2 In recent years, some gas-sensitivity studies with tungsten trioxide (WO 3 ) based semiconductors have been reported. Pure or doped tungsten oxide is a promising material for the detection of nitrogen oxides (NO and NO 2 ) 3-6 and sulfur dioxide, 7 two substances which are considered to be responsible for ambient degradation together with carbon oxides and hydrocarbons. The gas-sensing properties of WO 3 are highly dependent on the deposition method. Tungsten oxide is generally deposited by reactive rf sputtering. 8-10 However, other techniques such as thermal evaporation, 11,12 sol-gel methods, 13 screen printing, 14 or photochemical production 6 have been reported to grow thin or thick active

Correlation between electronic nose signals and fruit quality indicators on shelf-life measurements with pinklady apples

Abstract In this work, an electronic nose is used to assess the ripeness state of pinklady apples through their shelf-life. In order to evaluate the electronic nose performance, fruit quality indicators are also obtained to compare results from both techniques. Pinklady apples were harvested at their optimal date so that electronic nose measurements and fruit quality measurements could be performed on the fruit samples during their ripening process. A PCA analysis, a well-known linear classification technique, does not show any clustering behaviour that might be attributed to ripening. On the other hand, Fuzzy art, an unsupervised neural network classification algorithm, shows a tendency to classify measurements regarding to their shelf-life period. Finally, electronic nose signals are correlated with classical fruit quality parameters such as firmness, starch index and acidity. Good correlation coefficients are obtained, a clear indication that electronic nose signals are related to the ripening process of apples.

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.).

Multicomponent gas mixture analysis using a single tin oxide sensor and dynamic pattern recognition

A new method, which is based on the discrete wavelet transform, is presented for extracting important features from the response transients of a micromachined, tin oxide-based gas sensor. It is shown that two components in a mixture can be simultaneously and accurately quantified by processing the response dynamics of a single sensor operated in a temperature-modulated mode. The discrete wavelet transform outperforms the fast Fourier transform (classical approach) because it is more appropriate for the non- linear frequency-time problem encountered here.

Wavelet transform and fuzzy ARTMAP-based pattern recognition for fast gas identification using a micro-hotplate gas sensor

Abstract It is shown that a single thermally-modulated tin oxide-based resistive microsensor can discriminate between two different pollutant gases (CO and NO2) and their mixtures. The method employs a novel feature-extraction and pattern classification method, which is based on a 1-D discrete wavelet transform and a Fuzzy adaptive resonant theory map (ARTMAP) neural network. The wavelet technique is more effective than FFT in terms of data compression and is highly tolerant to the presence of additive noise and drift in the sensor responses. Furthermore, Fuzzy ARTMAP networks lead to a 100% success rate in gas recognition in just two training epochs, which is significantly lower than the number of epochs required to train the back-propagation network.

Evaluation of an electronic nose to assess fruit ripeness

The main goal of our study was to see whether an artificial olfactory system can be used as a nondestructive instrument to measure fruit maturity. In order to make an objective comparison, samples measured with our electronic nose prototype were later characterized using fruit quality techniques. The cultivars chosen for the study were peaches, nectarines, apples, and pears. With peaches and nectarines, a PCA analysis on the electronic nose measurements helped to guess optimal harvest dates that were in good agreement with the ones obtained with fruit quality techniques. A good correlation between sensor signals and some fruit quality indicators was also found. With pears, the study addressed the possibility of classifying samples regarding their ripeness state after different cold storage and shelf-life periods. A PCA analysis showed good separation between samples measured after a shelf-life period of seven days and samples with four or less days. Finally, the electronic nose monitored the shelf-life ripening of apples. A good correlation between electronic nose signals and firmness, starch index, and acidity parameters was found. These results prove that electronic noses have the potential of becoming a reliable instrument to assess fruit ripeness.