Chemometric classification of some european wines using pyrolysis mass spectrometry (original) (raw)
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Journal of Agricultural and Food Chemistry, 2007
elements from wine samples from the Denomination of Origin (DO) Empordà -Costa Brava (Catalonia, Spain) were analyzed by inductively coupled plasma atomic emission spectrometry (ICP-AES) and mass spectrometry (ICP-MS) respectively. Previously, a comparison of different calibration methodologies and sample digestion treatments had been carried out using ANOVA statistical tool. The obtained results demonstrated that internal standardization provides reliable results with the advantage that no further manipulation of the sample is needed. A principal component analysis of the concentration data was performed to differentiate the samples of DO Empordà -Costa Brava from wine samples from other wine-producing regions in Spain (i.e., Penedè s, Somontano, and Rioja). It was found that Sr and Ba contents discriminate the two DO groups. Moreover, a discriminant analysis function involving both variables distinguishes the two groups with a 100% classification rate. At the level of the leave-one-out cross-validation, all of the Empordà -Costa Brava samples were well classified, whereas the other DOs presented two borderline misclassifications.
Journal of Food Science and Technology, 2019
A highly informative chemometric approach using elemental data to distinguish and classify wine samples according to different criteria was successfully developed. The robust chemometric methods, such fuzzy principal component analysis (FPCA), FPCA combined with linear discriminant analysis (LDA), namely FPCA-LDA and mainly fuzzy divisive hierarchical associativeclustering (FDHAC), including also classical methods (HCA, PCA and PCA-LDA) were efficaciously applied for characterization and classification of white wines according to the geographical origin, vintage or specific variety. The correct rate of classification applying LDA was 100% in all cases, but more compact groups have been obtained for FPCA scores. A similar separation of samples resulted also when the FDHAC was employed. In addition, FDHAC offers an excellent possibility to associate each fuzzy partition of wine samples to a fuzzy set of specific characteristics, finding in this way very specific elemental contents and fuzzy markers according to the degrees of membership (DOMs).
Classification of wines according to several factors by ICP-MS multi-element analysis
Food chemistry, 2019
Wines from different grape varieties, geographical zones, soil types, foliar N application, SO addition and oak ageing were analyzed by inductively coupled plasma mass spectrometry (ICP-MS). For this purpose, ICP-MS methodology was optimized. The elements which allowed differentiate wines from studied grape varieties were Sr, Ca, Mg and Mn. Geographical zones were classified according to Sr, Ba, Ni, and Cu. Cs and Pb were the main elements to discriminate the wines from the 3 soil types. Wines from several N foliar doses application were classified by Pb, Ni, Mn and Zn. The content of Cs, Mg, Cu and Pb in wines characterized the SO addition. Finally, wines storage in barrels were differentiate by Na and Cs concentration. The discriminant functions classify 100% of the wines, with the exception of grape variety (97.0%) and oak ageing (95.8%). Consequently, ICP-MS can be applied to classified wines according to viticultural and oenological factors.
American Journal of Enology and Viticulture, 2010
Chemical analysis in conjunction with multivariate data evaluation methods was used to study elemental profiles and geographical origin of wines from central Balkan countries (Serbia, Montenegro, and Macedonia). Nine elements (Na, K, Mg, Ca, Fe, Mn, Zn, Cu, and Pb) chosen as chemical descriptors were analyzed in 41 commercial wine samples. Unsupervised pattern recognition methods-principal component analysis (PCA) and factor analysis-identified the main factors controlling the data variability, while the application of hierarchical cluster analysis (HCA) highlighted a differentiation between sample groups belonging to different variable inputs. Three PCs were shown to be the most significant, together accounting for 70.8% of the total variance. Supervised pattern recognition methods-linear discriminant analysis (LDA), k-nearest neighbor (kNN), soft independent modeling of class analogy (SIMCA), and artificial neural network (ANN)-applied to the classification of wine samples demonstrated different recognition and prediction abilities. The recognition rate for LDA was 97.6%, and the percentage of classification obtained by kNN, SIMCA, and ANN was 100%. However, the LDA method produced the best prediction rate of 83.3%, whereas kNN, SIMCA, and ANN gave much lower percentages of correctly classified samples, at 72.2, 61.1, and 55.6 %, respectively. Trace elements seem to be suitable descriptors for classification studied wine samples, since their concentrations comprising both natural and other sources of inf luence are attributed to grapegrowing and winemaking sites. Comparison of pattern recognition methods reveals the difference in their classification power.
2011
In this study, the potential of high performance liquid chromatography coupled to quadrupole time-offlight mass spectrometry (HPLC-QTOFMS) for metabolomic profiling of red wine samples was examined. Fifty one wines representing three varieties (Cabernet Sauvignon, Merlot, and Pinot Noir) of various geographical origins were sourced from the European and US retail market. To find compounds detected in analyzed samples, an automated compound (feature) extraction algorithm was employed for processing background subtracted single MS data. Stepwise reduction of the data dimensionality was followed by principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA) which were employed to explore the structure of the data and construct classification models. The validated PLS-DA model based on data recorded in positive ionization mode enabled correct classification of 96% of samples. Determination of molecular formula and tentative identification of marker compound was carried out using accurate mass measurement of full single MS spectra. Additional information was obtained by correlating the fragments obtained by MS/MS accurate mass spectra using the QTOF with collision induced dissociation (CID) of precursor ions.
Journal of Agricultural and Food Chemistry, 2003
High-performance ion chromatography exclusion, inductively coupled plasma emission spectroscopy, and nuclear magnetic resonance (NMR) measurements were carried out in combination with chemometrics on 33 wine samples coming from three Slovenian wine-growing regions and from Apulia (southern Italy). The chemometric classification of wines according to their geographical origin was obtained with a nearly 100% degree of achievement. The discriminating potential of the 1 H NMR and of the other analytical determinations has been estimated separately. The best prediction of wines has been obtained with NMR data.
Multivariate discrimination of wines with respect to their grape varieties and vintages
European Food Research and Technology, 2010
The primary focus of the European Union funded project entitled ''Establishing a WINE Data Bank for analytical parameters for wines from Third Countries'' (WINE-DB project, G6RD-CT-2001-00646-WINE-DB) was the discrimination of wine samples with respect to their geographical origin using only a few chemical parameters. Taking a step further, we have investigated the possibility of discriminating the wines in the data bank according to their harvesting seasons and grape varieties. Several chemometric methods were carefully selected and evaluated for this purpose. These were discriminant partial least squares, classification and regression trees, uninformative variable elimination discriminant partial least squares and neuro-fuzzy systems. With classification and regression trees, it was possible to identify a few chemical parameters including isotopic ratios (e.g. d 18 O), biogenic amines and rare earth elements that discriminate between vintages and some grape varieties for wines produced in a particular country such as Czech Republic, Hungary, Romania or South Africa. These parameters can be used in evaluating the authenticity of wines.
Multivariate data analysis in classification of must and wine from chemical measurements
European Food Research and Technology, 2000
A chemometric study was carried out; based on measurements of chemicals present in musts and wines from the Tacoronte-Acentejo Designation of Origin area (Canary Islands, Spain) obtained in an artisanal winemaking procedure. Univariate and multivariate data analysis was used to distinguish between two different harvest years (1987 and 1988) in both musts and wines. The data was examined using the statistical techniques of discriminant analysis, principal component analysis, biplot analysis and factor analysis, which allowed the study of the underlying structures of the data and the characterization of the wines. This methodology was able to differentiate between musts and wines according to traditional variables used in wine analysis. However, no clear differentiation was found with respect to the year of vintage.
Journal of the Science of Food and Agriculture, 1999
In 42 white wines from Galicia (northwestern Spain) some trace elements were determined by atomic spectroscopy. Data were processed using multivariate chemometric techniques involving cluster analysis, principal component analysis, discriminant analysis, Knearest neighbors, and soft independent modeling of class analogy to develop a typification for wine samples of Rlas Baixas origin. The wines with Certified Brand of Origin Rias Baixas can be differentiated from wines of the other two Certified Brands of Origin from Galicia, Ribeiro and Valdeorras, which are possible substrates for falsification. Using lithium and rubidium as key features, a nearly correct classification was achieved. The probability of a non-Rlas Baixas wine being accepted as genuine is practically nil.