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We report herein the development of a simple electronic system that uses simple potentiometric electrodes applied to the analysis fish freshness. The array of sensors is built by using metallic electrodes as plain sharp wires or in a thick-film configuration. Experiments on real fish led to the conclusion that gold and silver electrodes are suitable for the development of qualitative systems able to classify fish in relation to its freshness. Equipment based on a microprocessors programmed with fuzzy logic can be developed for in situ monitorization of fish quality.

The gas sensing properties of chemical sensors based on nanoparticle WO/sub 3/ thick films, doped with indium and bismuth, are studied. Commercial WO/sub 3/ nanopowder with spherical diameter up to 33.1 nm was mixed with either InCl/sub 3/ or BiCl/sub 3/. The sensing films of the sensors were prepared using three different concentrations of each doping metal (1.5 wt.%, 3.0 wt.% and 5.0 wt.%, respectively). The performance of the sensors was tested for different concentrations of ammonia, nitrogen dioxide and carbon monoxide diluted in synthetic dry air. The sensors were operated at 250/spl deg/C, 300/spl deg/C and 350/spl deg/C in order to analyze the effect of the temperature on their responses. The score plot of a principal component analysis (PCA) showed a linear separation between the three investigated gases. The response characteristics of the produced sensors are discussed on the basis of the structural properties of screen-printed nanoparticle WO/sub 3/ thick films. The results obtained show the possibility to classify and quantify between different gases with nanoparticle WO/sub 3/-doped gas sensors.

In this paper, a new method to enhance sensor selectivity is described. A flow modulation system driven by a PC-controlled peristaltic pump has been designed to feed a sensor chamber with different vapors. 45 measurements where performed comprising five different species (benzene, toluene, o-xylene, methanol and para-xylene) in three different concentrations (20, 200, 2000 ppm). Using frequency domain techniques and neural networks, the system was able to reach a 92% classification success rate when identifying all five vapors despite concentration was not constant and a single sensor was used. Moreover, when amplitude and variance information were removed from sensor transient signals, a 62% success rate was achieved, proving that the transient waveform has additional information that helps to enhance selectivity

A new procedure for variable selection, which runs in two steps, is introduced. First, an unsupervised and very fast variable selection procedure is applied: a parameter that accounts for the correlation between the features available is computed and, only near 20% of initial variables (those that are less collinear) are retained for further selection. Then, a fine-tuning selection based on a deterministic method (stepwise) coupled to a simple probabilistic neural network is conducted on the variable subset that resulted from the first selection step. The method is demonstrated using a database consisting of vapors of ethanol, acetone and toluene and their binary mixtures (120 variables). Vapors can be simultaneously identified and quantified with a 95.83% success rate and the time needed for the whole process is about 5 minutes in a Pentium 4 PC platform. Being unsupervised, the fast variable selection method applies generally, even in aroma analysis problems where category discovery is an issue. This is illustrated by applying the method to mixture analysis using direct mass spectrometry

In this work, we report for the first time that a system based on metal oxide resistive gas sensors can successfully determine the presence of trace-level pollutants in a carbon dioxide stream. The successful use of metal oxide sensors in the absence of oxygen (oxygen concentration below 15 ppm) has enabled a new type of analyzer to assess the quality of carbon dioxide, offering continuous monitoring capabilities, ruggedness, simplicity, wireless communications and low cost.