Application of metalloporphyrins-based gas and liquid sensor arrays to the analysis of red wine [An article from: Analytica Chimica Acta]
Book Details
PublisherElsevier
ISBN / ASINB000RR02H2
ISBN-13978B000RR02H0
AvailabilityAvailable for download now
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Analytica Chimica Acta, published by Elsevier in 2004. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
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.
Description:
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.
