Wine aroma volatiles of two different typical Apulian wines made by autochthonous grape varieties (i.e. Negroamaro and Primitivo) were extracted by solid phase extraction (SPE) and analyzed using gas chromatography-mass spectrometry (GC-MS) in conjugation with an electronic nose (E-nose). Eighteen compounds were found over their own odour threshold and they were taken into account for further data analysis. Sensor data were analyzed by principal component analysis (PCA) to investigate the discrimination capability of the sensor array. The concentrations of volatile chemical compounds in wines determined by GC-MS have been correlated with electronic nose (E-nose) responses using partial least squares (PLSs) and quadratic response surface regression (RSR) analysis. By means of these regression models, relationships between E-nose responses and wine aroma compounds were established. Quite all of the 18 wine odorant concentration were predicted at a satisfactory extent; RSR technique gave better prediction results compared to PLS. © 2012 Elsevier B.V. All rights reserved.
Aroma analysis by GC/MS and electronic nose dedicated to Negroamaro and Primitivo typical Italian Apulian wines
Capone Simonetta;Tufariello Maria;Francioso Luca;Montagna Giovanni;Casino Flavio;Leone Alessandro;Siciliano Pietro
2013
Abstract
Wine aroma volatiles of two different typical Apulian wines made by autochthonous grape varieties (i.e. Negroamaro and Primitivo) were extracted by solid phase extraction (SPE) and analyzed using gas chromatography-mass spectrometry (GC-MS) in conjugation with an electronic nose (E-nose). Eighteen compounds were found over their own odour threshold and they were taken into account for further data analysis. Sensor data were analyzed by principal component analysis (PCA) to investigate the discrimination capability of the sensor array. The concentrations of volatile chemical compounds in wines determined by GC-MS have been correlated with electronic nose (E-nose) responses using partial least squares (PLSs) and quadratic response surface regression (RSR) analysis. By means of these regression models, relationships between E-nose responses and wine aroma compounds were established. Quite all of the 18 wine odorant concentration were predicted at a satisfactory extent; RSR technique gave better prediction results compared to PLS. © 2012 Elsevier B.V. All rights reserved.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.