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A composite sensor array impedentiometric electronic tongue. Part II. Discrimination of basic tastes (Articolo in rivista)

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A composite sensor array impedentiometric electronic tongue. Part II. Discrimination of basic tastes (Articolo in rivista) (literal)

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Pioggia G.; Di Francesco F.; Marchetti A.; Ferro M.; Leardi R.; Ahluwalia, A. (2007)
A composite sensor array impedentiometric electronic tongue. Part II. Discrimination of basic tastes
in Biosensors & bioelectronics; Elsevier Science Ltd., Oxford (Regno Unito)
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Pioggia G.; Di Francesco F.; Marchetti A.; Ferro M.; Leardi R.; Ahluwalia, A. (literal)

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Interdepartmental Research Center \"E. Piaggio\", Faculty of Engineering, University of Pisa, Italy - Institute of Clinical Physiology CNR; Department of Chemistry and Industrial Chemistry, University of Pisa, Italy; DICTFA, University of Genoa, Italy (literal)

Titolo

A composite sensor array impedentiometric electronic tongue. Part II. Discrimination of basic tastes (literal)

Abstract

An impedentiometric electronic tongue based on the combination of a composite sensor array and chemometric techniques aimed at the discrimination of soluble compounds able to elicit different gustative perceptions is presented. A composite array consisting of chemo-sensitive layers based on carbon nanotubes or carbon black dispersed in polymeric matrices and doped polythiophenes was used. The electrical impedance of the sensor array was measured at a frequency of 150 Hz by means of an impedance meter. The experimental set-up was designed in order to allow the automatic selection of a test solution and dipping of the sensor array following a dedicated measurement protocol. Measurements were carried out on 15 different solutions eliciting 5 different tastes (sodium chloride, citric acid, glucose, glutamic acid and sodium dehydrocholate for salty, sour, sweet, umami and bitter, respectively) at 3 concentration levels comprising the human perceptive range. In order to avoid over-fitting, more than 100 repetitions for each sample were carried in a 4-month period. Principal component analysis (PCA) was used to detect and remove outliers. Classification was performed by linear discriminant analysis (LDA). A fairly good degree of discrimination was obtained. (literal)

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