Artificial tongue applications

In this review, a critical overview of artificial tongue applications over the last decade is outlined. In particular, the focus is centered on the chemometric techniques, which allow the extraction of valuable information from nonspecific data. The basic steps of signal processing and pattern recognition are discussed and the principal chemometric techniques are described in detail, highlighting benefits and drawbacks of each one. Furthermore, some novel methods recently introduced and particularly suitable for artificial tongue data are presented. 

The term artificial tongue is used in two main branches of science. The first one concerns the neurophysiological studies aimed at developing perceptual supplementation devices, with biomedical engineering applications to human disabilities. The second utilization of the term artificial tongue concerns, instead, the laboratory analytical instruments used in combination with chemometric techniques to obtain complex information (often sensory-like, but not only) on samples. As for this latter meaning, also the synonymous electronic tongue is frequently used, particularly for electroanalytical devices. 

From examination of almost 230 papers published in ISI journals over the period 1996-2009, it emerges that the many sectors in which artificial tongues have found applications go from the industrial plant processmonitoring to biomedical and clinical studies (see Fig. 2.4). As for this latter field, a number of sensors for the determination of several clinical... 

An)nvay, the principal use of artificial tongues is within the food sciences. The applications concern almost exclusively liquid food mainly wine (about 18% of the studies examined), fruit juices (almost 15%), mineral water (about 13%), followed by infusions like tea and coffee, soft drinks, milk, beer, and other alcoholic beverages. All these liquid foods are characterized by both low-viscosity and high-polarity values. 

Regression techniques provide models for quantitative predictions. The ordinary least squares (OLS) method is probably the most used and studied historically. Nevertheless, it presents a number of restrictions which often limit its applicability in the case of artificial tongue data. 

Artificial tongues represent effective analytical tools able to characterize samples by means of a nonspecific approach. They may provide information useful for many purposes, allowing both qualitative and quantitative applications. 

The largest voliune of polsrmeric materials used in dentistry is in prosthetic applications. Polymeric materials are also important in operative dentistry, being used to produce composite resins, dental cements, adhesives, cavity liners, and as a protective sealant for pits and fissures. Elastomers are employed as impression materials. Resilient prosthetic devices are oft en fabricated to restore external soft-tissue defects. Mouth protectors are fabricated to prevent injury to teeth, as well as prevent head and neck injinaes. Other polymer applications include fabricating patterns for metal castings and partial denture frameworks, impression trays, orthodontic and periodontal devices, space maintainers, bite plates, cleft palate obdurators, and oral implants. Polymeric materials may also be used to fabricate an artificial tongue, when disease results in its loss. 

Application of chemosensors as components of an artificial tongue to the parallel detection of organic molecules in solution has also been proposed [10-12]. Analogs of natural sensory systems should have the ability to discriminate between enantiomers [13, 14], such as L-amino acids, which are mostly bitter, from o-amino acids which are generally sweet. 

The electronic nose and electronic tongue can be considered as a specific branch of the development of artificial intelligence and application of the electronic brain. 

In parallel, during recent years, several applications of artificial noses and tongues were demonstrated to be suitable not only for a sensory-like evaluation but for a wider-ranging characterization of the samples. Nonspecific analytical responses, in fact, may provide information about the... 

Artificial neural networks (ANNs) have been widely applied in the electronic tongue literature both for classification and multivariate regression problems almost one-third of the papers on electronic tongues examined for this review show ANN applications (see Fig. 2.10). 

The application of this concept to liquid samples is what we already refer to electronic tongue . It entails the use of multidimensional information coming from an array of chemical sensors, mimicking the animal sense of taste. As several possibilities exist on the side of which sensors form the array, the general response shown by the different sensors used is of paramount importance that is, cross-selectivity features are needed in order to profit from the multidimensional aspects of the information [7]. The performance of electronic tongues can be suited not only to qualitative purposes like identification of species and classification of sample varieties, but also to quantitative uses, normally the multidetermination of a set of chemical species, an interesting objective for process control. A more bioinspired trend is the artificial taste [8] in order to perform automated taste perception, especially in the industrial field. 

Moreno, L., Cartas, R., Merkogi, A., Alegret, S., Leija, L., Hernandez, P.R., Munoz, R. Application of the wavelet transform coupled with artificial neural networks for quantification purposes in a voltammetric electronic tongue. Sens. Actuators B 113, 487 99 (2006) Ogawa, H., Sato, M., Yamashita, S. Multiple sensitivity of chorda tympani fibres of the rat and hamster to gustatory and thermal stimuli. J. Physiol 199, 223-240 (1968)... 

Electronic tongue systems for remote environmental monitoring applications have been presented in several applications. A new approach in the chemical sensor field consists in the use of an array of nonspecific sensors coupled with a multivariate calibration tool which may form a node of a sensor network. The proposed arrays were made up of potentiometric sensors based on polymeric membranes, and the subsequent cross-response processing was based on a multilayer artificial neural network model as proposed by Mimendia et al. who described environmental monitoring of ammonium as a pollutant plus alkaline ions at different measuring sites in the states of Mexico and Hidalgo (Mexico), and monitoring of heavy metals (Cu ", Pb ", Zn ", and Cd " ) in open-air waste streams and rivers. 

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