Electronic noses and electronic tongues have been introduced in the past decade as technological attempts to mimic the functions of the human chemical senses. Beside this scientifically challenging objective, they have also shown to be practical instruments to analyse samples characterised by a complex composition. In this paper, two arrays of metalloporphyrins-based gas and liquid sensors are applied to the analysis of a red wine. The scope of the experiment here illustrated was to reproduce the classification properties of sensorial analysis and to quantify, from the sensor arrays data, both sensorial descriptors and chemical parameters. Results demonstrate the capability of such systems to be trained according to the behaviour of a practical panel of tasters. The analysis of data also revealed that the combination of the two arrays enhances the prediction properties both for qualitative and quantitative analysis. © 2003 Published by Elsevier B.V.
DI NATALE, C., Paolesse, R., Burgio, M., Martinelli, E., Pennazza, G., D'Amico, A. (2004). Application of metalloporphyrins-based gas and liquid sensor arrays to the analysis of red wine. In Analytica Chimica Acta (pp.49-56). ELSEVIER SCIENCE SA [10.1016/j.aca.2003.11.017].
Application of metalloporphyrins-based gas and liquid sensor arrays to the analysis of red wine
DI NATALE, CORRADO;PAOLESSE, ROBERTO;MARTINELLI, EUGENIO;D'AMICO, ARNALDO
2004-01-01
Abstract
Electronic noses and electronic tongues have been introduced in the past decade as technological attempts to mimic the functions of the human chemical senses. Beside this scientifically challenging objective, they have also shown to be practical instruments to analyse samples characterised by a complex composition. In this paper, two arrays of metalloporphyrins-based gas and liquid sensors are applied to the analysis of a red wine. The scope of the experiment here illustrated was to reproduce the classification properties of sensorial analysis and to quantify, from the sensor arrays data, both sensorial descriptors and chemical parameters. Results demonstrate the capability of such systems to be trained according to the behaviour of a practical panel of tasters. The analysis of data also revealed that the combination of the two arrays enhances the prediction properties both for qualitative and quantitative analysis. © 2003 Published by Elsevier B.V.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
