Quantification of Wine Mixtures with an Electronic Nose and a Human Panel.

In this work, an electronic nose and a human panel were used for the quantification of wines formed by binary mixtures of four white grape varieties and two varieties of red wines at different percentages (from 0 to 100% in 10% steps for the electronic nose and from 0 to 100% in 25% steps for the human panel). The wines were prepared using the traditional method with commercial yeasts. Both techniques were able to quantify the mixtures tested, but it is important to note that the technology of the electronic nose is faster, simpler, and more objective than the human panel. In addition, better results of quantification were also obtained using the electronic nose.

Advances in artificial olfaction: sensors and applications.

The artificial olfaction, based on electronic systems (electronic noses), includes three basic functions that operate on an odorant: a sample handler, an array of gas sensors, and a signal-processing method. The response of these artificial systems can be the identity of the odorant, an estimate concentration of the odorant, or characteristic properties of the odour as might be perceived by a human. These electronic noses are bio inspired instruments that mimic the sense of smell. The complexity of most odorants makes characterisation difficult with conventional analysis techniques, such as gas chromatography. Sensory analysis by a panel of experts is a costly process since it requires trained people who can work for only relatively short periods of time. The electronic noses are easy to build, provide short analysis times, in real time and on-line, and show high sensitivity and selectivity to the tested odorants. These systems are non-destructive techniques used to characterise odorants in diverse applications linked with the quality of life such as: control of foods, environmental quality, citizen security or clinical diagnostics. However, there is much research still to be done especially with regard to new materials and sensors technology, data processing, interpretation and validation of results. This work examines the main features of modern electronic noses and their most important applications in the environmental, and security fields. The above mentioned main components of an electronic nose (sample handling system, more advanced materials and methods for sensing, and data processing system) are described. Finally, some interesting remarks concerning the strengths and weaknesses of electronic noses in the different applications are also mentioned.

Characterization of an array of Love-wave gas sensors developed using electrospinning technique to deposit nanofibers as sensitive layers.

The electrospinning technique has allowed that very different materials are deposited as sensitive layers on Love-wave devices forming a low cost and successful sensor array. Their excellent sensitivity, good linearity and short response time are reported in this paper. Several materials have been used to produce the nanofibers: polymers as Polyvinyl alcohol (PVA), Polyvinylpyrrolidone (PVP) and Polystirene (PS); composites with polymers as PVA+SnCl4; combined polymers as PS+Poly(styrene-alt-maleic anhydride) (PS+PSMA) and metal oxides (SnO2). In order to test the array, well-known chemical warfare agent simulants (CWAs) have been chosen among the volatile organic compounds due to their importance in the security field. Very low concentrations of these compounds have been detected by the array, such as 0.2 ppm of DMMP, a simulant of sarin nerve gas, and 1 ppm of DPGME, a simulant of nitrogen mustard. Additionally, the CWA simulants used in the experiment have been discriminated and classified using pattern recognition techniques, such as principal component analysis and artificial neural networks.

Array of Love-wave sensors based on quartz/Novolac to detect CWA simulants.

An array of Love-wave sensors based on quartz and Novolac has been developed to detect chemical warfare agents (CWAs). These weapons are a risk for human health due to their efficiency and high lethality; therefore an early and clear detection is of enormous importance for the people safety. Love-wave devices realized on quartz as piezoelectric substrate and Novolac as guiding layer have been used to make up an array of six sensors, which have been coated with specific polymers by spin coating. The CWAs are very dangerous and for safety reasons their well known simulants have been used: dimethylmethyl phosphonate (DMMP), dipropyleneglycol methyl ether (DPGME), dimethylmethyl acetamide (DMA), dichloroethane (DCE), dichloromethane (DCM) and dichloropentane (DCP). The array has been exposed to these CWA simulants detecting very low concentrations, such as 25 ppb of DMMP, a simulant of nerve agent sarin. Finally, principal component analysis (PCA) as data pre-processing and discrimination technique, and probabilistic neural networks (PNN) as patterns classification technique have been applied. The performance of the sensor array has shown stability, accuracy, high sensitivity and good selectivity to these simulants.

Differentiation of red wines using an electronic nose based on surface acoustic wave devices.

An electronic nose, utilizing the principle of surface acoustic waves (SAW), was used to differentiate among different wines of the same variety of grapes which come from the same cellar. The electronic nose is based on eight surface acoustic wave sensors, one is a reference sensor and the others are coated by different polymers by spray coating technique. Data analysis was performed by two pattern recognition methods; principal component analysis (PCA) and probabilistic neuronal network (PNN). The results showed that electronic nose was able to identify the tested wines.

Classification of white wine aromas with an electronic nose.

This paper reports the use of a tin dioxide multisensor array based electronic nose for recognition of 29 typical aromas in white wine. Headspace technique has been used to extract aroma of the wine. Multivariate analysis, including principal component analysis (PCA) as well as probabilistic neural networks (PNNs), has been used to identify the main aroma added to the wine. The results showed that in spite of the strong influence of ethanol and other majority compounds of wine, the system could discriminate correctly the aromatic compounds added to the wine with a minimum accuracy of 97.2%.

Electronic nose for the identification of pig feeding and ripening time in Iberian hams.

An electronic nose system to control the processing of dry-cured Iberian ham is presented. The sensors involved are tin oxide semiconductors thin films. They were prepared by RF sputtering. Some of the sensors were doped with metal catalysts as Pt and Pd, in order to improve the selectivity of the sensors. The multisensor with 16 semiconductor sensors, gave different responses from two types of dry-cured Iberian hams which differ in the feeding and curing time. The data has been analysed using the PCA (principal component analysis) and backpropagation and probabilistic neural networks. The analysis shows that different types of Iberian ham can be discriminated and identified successfully.