J.P. Santos

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%.

Identification of typical wine aromas by means of an electronic nose

In the field of electronic noses it is not very usual to find many applications in wine detection. Most of them are related to discrimination of wines in order to prevent their illegal adulteration and detection of off-odours but their objective is not the identification of wine aromas. In this paper, an e-nose using headspace as an extraction technique is used for the identification of typical aromatic compounds present in white and red wines. The descriptors of these compounds are: fruity, floral, herbaceous, vegetative, spicy, smoky, and microbiological and they are responsible for the usual aromas in wines; concentrations differ from 2 to 10 times the threshold concentration humans can smell. Some of the measured aromas are: pear, apple, peach, coconut, rose, geranium, cut green grass, mint, vanilla, clove, almond, toast, wood and butter. Principal component analysis (PCA) shows datasets of this group of compounds are clearly separated and radial basis neural networks (RB-NN) show a 98% rate of success in classification.

Threshold detection of aromatic compounds in wine with an electronic nose and a human sensory panel.

An electronic nose (e-nose) based on thin film semiconductor sensors has been developed in order to compare the performance in threshold detection and concentration quantification with a trained human sensory panel in order to demonstrate the use of an e-nose to assess the enologists in an early detection of some chemical compounds in order to prevent wine defects. The panel had 25 members and was trained to detect concentration thresholds of some compounds of interest present in wine. Typical red wine compounds such as whiskeylactone and white wine compounds such as 3-methyl butanol were measured at different concentrations starting from the detection threshold found in literature (in the nanograms to milligrams per liter range). Pattern recognition methods (principal component analysis (PCA) and neural networks) were used to process the data. The results showed that the performance of the e-nose for threshold detection was much better than the human panel. The compounds were detected by the e-nose at concentrations up to 10 times lower than the panel. Moreover the e-nose was able to identify correctly each concentration level therefore quantitative applications are devised for this system.

Existence results for partial neutral integro-differential equation with unbounded delay

In this article, we study the existence and regularity of mild solution for a class of partial neutral integro-differential equation with unbounded delay.In this article, we study the existence and regularity of mild solution for a class of partial neutral integro-differential equation with unbounded delay.

Evaluation of Wine Aromatic Compounds by a Sensory Human Panel and an Electronic Nose

A comparative study between the perception and recognition thresholds of volatile components calculated for an electronic nose and a human sensory panel is presented. The electronic nose is home-developed for wine purposes and is based on thin film semiconductor sensors. The human sensory panel is formed by 25 tasters with previous experience in wine tasting. Both systems were trained in parallel to detect 17 volatile compounds involved in aromatic and off-flavor notes (grouped under 9 aromatic descriptors) from the threshold concentrations found in the literature (T) to increasing concentrations (T, 2T, and 4T). The results showed that the perception level of the human nose is superior in relation to the electronic nose, but the electronic nose gave better results in the recognition threshold of the some aroma. According to these results, it can be concluded that the electronic nose could be a useful complementary tool to sensory human panels.

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.

Detection of toxic gases by a tin oxide multisensor

A tin oxide multisensor with eight sensor elements has been utilized to detect different gases that are emitted from the chemical industry. The different sensors have been prepared using the RF reactive sputtering technique. Some components of this multisensor were doped with Pt and Cr by sputtering. Each sensor has been characterized by electrical measurements for detection of atmospheric pollution (nitrogen dioxide, carbon monoxide, toluene, and propanal). The detected gases are toxic and their extreme concentrations recommended in air oscillate from 1 ppm to hundreds of ppm. Detections were carried out with a single gas or a gas mixture (two gases) in dry air at 250/spl deg/C. The sensitivity and selectivity to these gases were studied. Backpropagation neural networks and their classification results were discussed.

Real-Time Characterization of Electrospun PVP Nanofibers as Sensitive Layer of a Surface Acoustic Wave Device for Gas Detection

The goal of this work has been to study the polyvinylpyrrolidone (PVP) fibers deposited by means of the electrospinning technique for using as sensitive layer in surface acoustic wave (SAW) sensors to detect volatile organic compounds (VOCs). The electrospinning process of the fibers has been monitored and RF characterized in real time, and it has been shown that the diameters of the fibers depend mainly on two variables: the applied voltage and the distance between the needle and the collector, since all the electrospun fibers have been characterized by a scanning electron microscopy (SEM). Real-time measurement during the fiber coating process has shown that the depth of penetration of mechanical perturbation in the fiber layer has a limit. It has been demonstrated that once this saturation has been reached, the increase of the thickness of the fibers coating does not improve the sensitivity of the sensor. Finally, the parameters used to deposit the electrospun fibers of smaller diameters have been used to deposit fibers on a SAW device to obtain a sensor to measure different concentrations of toluene at room temperature. The present sensor exhibited excellent sensitivity, good linearity and repeatability, and high and fast response to toluene at room temperature.

The interaction of low NO2 concentrations in air with degenerate nanocrystalline tin dioxide thin films

Abstract A multispecies adsorption model has been developed based on a generalization of the Volkenstein isotherm. The study of the dependence of the resistivity on the NO2 concentration was carried out by numerically solving the Poisson equation with pressure dependent boundary conditions. The model predicts a power law behaviour of the electrical response in a wide range of oxygen partial pressure with the exponent varying with the grain size. For the NO2 response there is also a power law dependence but saturation effects arise at high levels. In order to confirm the theory predictions, the effects of NO2 concentrations ranging from 50 to 800 ppb on the electrical resistivity of the nanocrystalline films were studied. The experiments resulted in excellent agreement with the theory.

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.