DSpace About DSpace Software

DSpace Biblioteca Universidad de Talca (v1.5.2) >
Dirección de Investigación >
Artículos en publicaciones no ISI - Universidad de Talca >

Please use this identifier to cite or link to this item: http://dspace.utalca.cl/handle/1950/9081

Title: Varietal discrimination of Chilean wines by direct injection mass spectrometry analysis combined with multivariate statistics
Authors: Villagra, E.
Santos, L.S.
Vaz, B.G.
Eberlin, M.N.
Laurie, V.F.
Keywords: Wine
Mass spectrometry
Multivariate statistics
Issue Date: 15-Mar-2012
Citation: FOOD CHEMISTRY Volume: 131 Issue: 2 Pages: 692-697 DOI: 10.1016/j.foodchem.2011.08.078
Abstract: A simple, direct injection, electrospray ionization Fourier transform mass spectrometry (ESI FT-MS) method, in combination with multivariate statistics, was used for the characterization and sorting of Chilean wines. 47 commercial red wines labelled as Cabernet Sauvignon, Carmenere, Syrah, and Pinot noir, and 25 white wines of the varieties Chardonnay and Sauvignon blanc were diluted, directly infused into the mass spectrometer, and analyzed in negative ion mode. The signature ions used for statistical analyses were manually filtered out from the signals with m/z ratios over 10%. The results of principal component analysis allowed a good sorting of white wines, but not so in the case of reds. The main three principal components explained 96.82% and 85.65% of the variance for white and red wines, respectively. Instead, linear discriminant analysis, allowed the correct discrimination of 100.00% of white and 95.74% of red samples. The validation of these results using the leave-one-out cross-validation method gave lower percentages of correct classification (76.00% and 61.70% of white and red samples respectively), suggesting that some of the wine samples analyzed might have been blends of more than one variety. Consequently, ESI FT-MS direct injection analysis of wines can be used for sample discrimination, but requires a stronger mathematical model built with spectral information of pure and blended samples before improving the percentages of classification. (C) 2011 Elsevier Ltd. All rights reserved.
Description: Reprint Address: Laurie, VF (reprint author), Univ Talca, Sch Agr Sci, 2 Norte 685, Talca, Chile.
URI: http://dspace.utalca.cl/handle/1950/9081
ISSN: 0308-8146
Appears in Collections:Artículos en publicaciones no ISI - Universidad de Talca

Files in This Item:

File Description SizeFormat

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.


Valid XHTML 1.0! DSpace Software Copyright © 2002-2009  The DSpace Foundation - Feedback