Fabrizio Davide

Tasting of beverages using an electronic tongue

An electronic tongue based on the sensor array of non-specific solution sensors together with pattern recognition tools has been applied to qualitative analysis of different beverages. It has been found that it is capable both to discriminate reliably between various sorts of the same type of beverages (tea, coffee, beer, soft drinks, juice, etc.) and to monitor the process of aging of juice. Correlations have been found between integral parameters produced by electronic tongue and quality of juice. Some conceptions and backgrounds used for electronic tongue development have been put forward and discussed.

An electronic nose for food analysis

Since the first developments of electronic noses, food analysis has been considered as one of its most useful applications. In this paper an electronic nose based on quartz microbalances coated with metallo-porphyrins and related compounds is presented and illustrated. Extensive tests on various substances playing key roles in food analysis show that sensing properties of the sensing materials (in terms of sensitivity and selectivity) can be exploited for electronic nose applications devoted to the analysis of various kinds of foods. The versatility of this system has been successfully tested on different kinds of foods, such as fish, meat, vegetable and wine for which results are shown.

Multicomponent analysis on polluted waters by means of an electronic tongue

In this paper the simultaneous measurements of the concentrations of a number of chemical species in solutions performed by a sensor array of ion sensitive electrodes are presented and discussed. By analogy with the well known electronic nose this sensor array operating in solutions, will be here called electronic tongue. In order to extract optimized information from the electronic tongue output data, many different techniques have been applied; they were based on chemometrics, non-linear least squares and neural networks. The best results have been achieved by the introduction of modular models which make use, at the same time, of both qualitative and quantitative information.

The application of metalloporphyrins as coating material for quartz microbalance-based chemical sensors

Abstract The results of both optimization and tests to prove the suitability of an array of quartz microbalance sensors (QMBs) modified with various metalloporphyrins for the determination of food freshness are presented and discussed. As far as optimization is concerned, it was found that a minimum amount of 50 μg of metalloporphyrin must be used for the modification of the quartz microbalance sensors in order to obtain the maximum sensitivity. The sensory behavior of five different porphyrins was subsequently studied. QMBs were modified using four different meso -tetraphenylporphyrins: phenyl, p -nitrophenyl, p -bromophenyl, p -methoxyphenyl and an octa-alkylporphyrin ( etio -porphyrin I), all loaded with a Co 2+ metal ion. A clear decrease in the sensitivity for the etio -porphyrin I was observed whereas for the meso -tetraphenyl-porphyrins the best response was obtained for the p -nitrophenyl derivative. These results can be attributed to the different electron densities which are present at the metal center of the macrocycle. The determination of the response behavior with respect to methanol, thiophene, diethylamine and triethylamine of a sensor array consisting of rhodium, ruthenium, cobalt, and manganese meso -tetraphenylporphyrin revealed that there is a clear difference in terms of the sensitivity and hence, the selectivity for the various QMBs. The rhodium and the cobalt-based QMBs were alike and demonstrated a preference for the gases with soft donating sites, i.e. thiophene and the amines. The QMBs based on ruthenium and manganese demonstrated distinctly different behavior. The ruthenium-based QMB demonstrated no clear preference for gases with either hard or soft donating sites, whereas the manganese-based QMB preferred gases with hard donating sites, i.e. methanol. These results led to the overall conclusion that this sensor array could be used for the analysis of complex gas mixtures, where the most prevalent gases fall under the categories of the amines, the alcohols and the sulphides.

An electronic nose for the recognition of the vineyard of a red wine

Sensor arrays have been tentatively utilized in the past as tools for the odours characterisation of particular classes of foods and beverages. In this paper a technique aiming to discriminate among similar wines is described. A sensor array of metal-oxide based gas sensors was employed for the recognition of two wines, having the same denomination (Groppello red wine) but coming from different vineyards. The performance of the array was compared with that of the standard chemical analytical approach adopted by the wines authority.

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.

Drift counteraction for an electronic nose

Abstract Most gas sensors do not give stable responses over a long period of time. This drift causes electronic noses to classify gases wrongly after some period of time. In this paper this problem has been studied, and two methods for counteracting drift are proposed. One is based on reference patterns that are updated when new measurements are made, and the other uses models known from system identification theory. The two methods are suited for different experimental situations, and both give a better result than a back-propagation neutral network classifier when they are used under the right conditions.

Pattern recognition in gas sensing: well-stated techniques and advances

Abstract A conceptual systematization of pattern-recognition techniques that are or could be applied to the gas-sensing field is provided. In particular, the Bayesian method and artificial neural networks are considered. Inner similarities, i.e., extended use of discriminants, drawbacks and possible improvements are also reported. The context of artificial olfaction has also been taken into account, in particular the physiological aspects, as an important area where enhanced pattern-recognition techniques can be fruitfully applied. In this respect ‘fuzzy set theories’ and ‘self-organizing maps’ are also illustrated as promising new tools for increasing the overall performance of pattern recognition.

Complex chemical pattern recognition with sensor array: the discrimination of vintage years of wine

A sensor array formed by a number of metal-oxide semiconductor gas sensors has been utilized for the recognition of complex chemical patterns characterized by small differences from each other. The system has been applied to the recognition of different vintage years of the same kind of wine. The sensors are fabricated adopting different materials in order to endow the array with a broad sensitivity spectrum and to avoid the occurrence of duplication of single-sensor performance. The obtained array shows a satisfactory discrimination among different vintages. The principal-component-analysis technique has been proved to be suitable for the extraction of the pattern features from the arrays data set. An intriguing correlation between the recognition performance of the array and a particular technique adopted for wine fabrication has been found.

Multicomponent analysis of heavy metal cations and inorganic anions in liquids by a non-selective chalcogenide glass sensor array

Abstract A first step towards a multisensor system, based on ion-selective electrodes, for the analysis of complex solutions is presented. An array formed by chalcogenide glass electrodes has been utilized for the quantitative analysis of solutions containing seven ionic species (heavy metal cations and inorganic anions). An important step in working with a sensor array is the data analysis strategy. The array data have been processed by multiple linear regression, partial least squares and artificial neural network. By using neural networks the best performances were obtained. It is worth noting that zince and sulphate have been measured although no specific sensors for these ions were present in the array.