Arnaldo D’Amico

Evaluation of Italian wine by the electronic tongue: recognition, quantitative analysis and correlation with human sensory perception

Abstract The electronic tongue based on a sensor array comprising 23 potentiometric cross-sensitive chemical sensors and pattern recognition and multivariate calibration data processing tools was applied to the analysis of Italian red wines. The measurements were made in 20 samples of Barbera d’Asti and in 36 samples of Gutturnio wine. The electronic tongue distinguished all wine samples of the same denomination and vintage, but from different vineyards. Simultaneously the following quantitative parameters of the wines were measured by the electronic tongue with precision within 12%: total and volatile acidity, pH, ethanol content, contents of tartaric acid, sulphur dioxide, total polyphenols, glycerol, etc. The electronic tongue is sensitive to multiple substances that determine taste and flavour of wine and, hence, the system was capable of predicting human sensory scores with average precision of 13% for Barbera d’Asti wines and 8% for Gutturnio wines.

An investigation on electronic nose diagnosis of lung cancer

The use of gas sensor arrays as medical diagnosis instruments has been proposed several years ago. Since then, the idea has been proven for a limited number of diseases. The case of lung cancer is particularly interesting because it is supported by studies that have shown the correlation between the composition of breath and the disease. However, it is known that many other diseases can alter the breath composition, so for lung cancer diagnosis it is necessary not only to detect generic alterations but those specifically consequent to cancer. In this paper an experiment, performed in the bronchoscopy unit of a large hospital, aimed at discriminating between lung cancer, diverse lung diseases and reference controls is illustrated. Results show not only a satisfactory identification rate of lung cancer subjects but also a non-negligible sensitivity to breath modification induced by other affections. Furthermore, the effects of some compounds frequently found in the breath of lung cancer subjects have also been studied. Results indicate that breath samples of control individuals drift towards the lung cancer group when added with either single or mixtures of these alleged cancer-related compounds.

The evaluation of quality of post-harvest oranges and apples by means of an electronic nose

Abstract The measure of quality of post-harvest fruits is considered a promising application field for electronic nose technology such as the detection of defects. Among the possible defects present in fruits those due to post-harvest treatment are particularly important. Among them defects like mealiness (due to post-harvest over-ripening), skin damage (due to mechanical or temperature stresses), and infections affect strongly the perception of consumers. They have to be avoided in order to achieve high quality products. In this paper, the study of the variations of aroma of oranges, during the storage, and apples, due to the presence of mealiness and skin damage by means of a thickness shear mode quartz resonators- (TSMR) based electronic nose, is illustrated and discussed. Results have evidenced that the electronic nose has enough sensitivity and resolution to distinguish among the various classes and to correctly predict the amount of defects (for apples) and storage days (for oranges).

Comparison and integration of different electronic noses for freshness evaluation of cod-fish fillets

Abstract For the instrumental analysis of fish freshness, several different methods have been proposed based on different principles (such as mechanical, electrical, and optical). Although, it is well-known that the headspace composition changes greatly during the spoilage process, the difficulties in sampling and measuring the headspaces with traditional analytical chemistry techniques makes the practical implementation of this principle not viable. Recently, the advent of sensor array technology has given the possibility to reconsider the headspace variation for instrumental applications. In this paper the use of measurements of two electronic noses, based on different sensor technologies and sampling methodologies, to detect freshness of cod-fish fillets is illustrated and discussed. Over a period of 17 storage days, the two sensor systems have shown different resolution, while the combination of them achieves the best performances allowing an almost complete evaluation of the freshness of samples. Trimethylamine (TMA) and total volatile basic nitrogen, measured with conventional techniques, show a non-monotonic behaviour, that induces the possibility of large errors in freshness estimation, as shown by the electronic noses.

Counteraction of environmental disturbances of electronic nose data by independent component analysis

Abstract Electronic nose data are usually affected by several disturbances in part due to the non-idealities of sensors and in part due to the effects of the environment as far as the composition of the gaseous sample is concerned. In some practical applications, the sample cannot be completely insulated from the surrounding environment and its drift (due to temperature and relative humidity changes) may be so large to completely dim the intrinsic resolution of the sensor array. The problem could be solved at data analysis level making some assumptions on the nature of the disturbances. In this paper, the independent component analysis (ICA) method will be illustrated and used to segregate the environmental disturbances from the meaningful part of the data. The method is tested on a practical and simple experiment aimed at distinguishing two classes of fruits measured for 1 week at different room temperatures and relative humidity. The application of ICA results in a meaningful improvement of the classification. The application of ICA gives new insights in the comprehension of the working mechanisms of electronic nose systems. Connections between ICA and electronic nose will also be discussed in the paper.

Direct and two-stage data analysis procedures based on PCA, PLS-DA and ANN for ISE-based electronic tongue—Effect of supervised feature extraction

A novel strategy of data analysis for artificial taste and odour systems is presented in this work. It is demonstrated that using a supervised method also in feature extraction phase enhances fruit juice classification capability of sensor array developed at Warsaw University of Technology. Comparison of direct processing (raw data processed by Artificial Neural Network (ANN), raw data processed by Partial Least Squares-Discriminant Analysis (PLS-DA)) and two-stage processing (Principal Components Analysis (PCA) outputs processed by ANN, PLS-DA outputs processed by ANN) is presented. It is shown that considerable increase of classification capability occurred in the case of the new method proposed by the authors.

The features of the electronic tongue in comparison with the characteristics of the discrete ion-selective sensors

Abstract A comparison of the features of the electronic tongue; comprising an array of non-specific chemical sensors and pattern recognition methods, with analytical characteristics of the discrete conventional ion-selective sensors (electrodes) has been performed. A method has been suggested that allows observing the detection limit and the selectivity of a sensor array at least three to five times better than that for a single sensor. The electronic tongue also exhibits a number of completely new features, such as the possibility to determine some unusual species in solutions, e.g. organic substances, and the capability to make measurements without the standard reference electrode.

Electronic nose based investigation of the sensorial properties of peaches and nectarines

Abstract Quality of fruit is traditionally associated with the physical characteristics, such as surface colour, shape, and firmness. Although, it is well known that the aroma is also associated with the ripening processes, this feature never resulted in any practical instrumentation due to the complexity of the aroma analysis. Over the past years, electronic nose technology opened the possibility to exploit information on aroma to assess fruit ripening stage, and more generally, fruit quality. In order to match the instrumental analysis with the consumer requirements, a sensorial profile of the fruit is necessary. From the point of view of sensory analysis, fruits have not been extensively studied and a complete and detailed description of the aroma components is not well established. In this paper, an electronic nose and a sensory panel are considered together in order to study the sensorial properties of a number of peaches and nectarines. Fruits were divided into four classes; each composed of several cultivars. The research is aimed at evaluating the sensorial features typical of each class. This work represents a first attempt toward the definition of a sensorial profile for these fruits, and it can be considered a further proof of the fruitful interactions between natural olfaction and electronic nose.

Outer product analysis of electronic nose and visible spectra: application to the measurement of peach fruit characteristics

Abstract Visual aspect and aroma are among the most important features of fruit that determine consumer preferences. Electronic nose and spectroscopic techniques have shown positive results in evaluating some basic analytical parameters of fruit and global features such as the cultivar. In this paper, we illustrate and discuss a study aimed at evaluating the improvement derived by the fusion of visible spectra and electronic nose data. These experiments were performed on a population of yellow peaches belonging to two cultivars. Each sample was measured by visible optical spectroscopy and by electronic nose. In addition, a number of reference parameters were also measured by conventional destructive methodologies. Collected data were analysed individually and then fused together in order to classify the two cultivars and to estimate the reference parameters. Data fusion was performed building the outer product matrix for each measurement. The set of matrices was then successively unfolded and analysed by conventional chemometrics tools. Results were improved using outer products, for instance in classification average percentage errors of 25, 10, and 7 for electronic nose, spectra, and outer product, respectively was achieved. Regression analysis provides the evidence of a substantial orthogonal appearance of the datasets, which offer former hidden information on fruit classification.

Characterization and design of porphyrins-based broad selectivity chemical sensors for electronic nose applications

Abstract The absolute selectivity of chemical sensors ceased in the last years to be a fundamental feature due to the uprising of many applications, such as electronic noses, which require the presence of broad selectivity sensors. On the other hand for these applications the attention is paid to the development of sensors whose broad selectivity can be oriented towards different classes of molecules representing a given environment in order to optimize the interface between the sensitive layers deposited on transducers and the chemical complexity of the environment itself. In this paper the main problems in the design and characterization of such sensors are presented and discussed in the case of a class of molecules, such as metallo-porphyrins, which have been recently exploited as sensitive layers in electronic nose applications.