Step by step approach to multi-element data analysis in testing the provenance of wines

Serapinas, P.; Venskutonis, P. R.; Aninkevičius, V.; Ežerinskis, Žilvinas; Galdikas, Arvaidas; Juzikienė, V.

doi:10.1016/j.foodchem.2007.09.003

Cited by 41 publications

(20 citation statements)

References 18 publications

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“…These techniques have been applied to characterise wines from different regions of a country such as France (Medina & Van Zeller, 1984), Italy (Moret, Scarponi, & Cescon, 1994), Spain (Gonzálvez, Llorens, Cervera, Armenta, & de la Guardia, 2009;Paneque, Alvarez, & Gómez, 2009), Germany (Danzer, Calle, Thiel, & Reichenbächer, 1999), the Czech Republic (Kment et al, 2005), Argentina (Fabani et al, 2010), or among different countries (Serapinas et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Content in metallic ions of wines from the Madeira and Azores archipelagos

et al. 2011

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a b s t r a c tThis study determines for the first time Na, K, Ca, Mg, Fe, Cu, Zn, Mn, Sr, Li and Rb contents in wines from the archipelagos of Madeira and Azores (Portugal). The greater part of the mean content for the different parameters fell within the ranges described in the literature, except for sodium whose higher content may be due to the effect of marine spray. ANOVA was used to establish the metals with significant differences in mean content between the wines from both archipelagos, between table and liquor wines of Madeira, and between wines of Pico and Terceira Islands from the Azores archipelago. Principal component analysis shows differences in the wines according to the wine-making process and/or the equipment employed. Stepwise linear discriminant analysis achieves a good classification and validation of wines according to the archipelago of origin, and the island in the case of Azores wines.

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Section: Introductionmentioning

confidence: 99%

Content in metallic ions of wines from the Madeira and Azores archipelagos

et al. 2011

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“…1 and 2 below. The data represent the problem of classification of wine samples measured in [8]. The absolute concentrations of 19 elements, namely Li, B, Na, Mg, Al, K, Ca, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Rb, Sr, Ba in 102 wines from Bulgaria (5), Chile (25), France (26), Hungary (7), Italy (6), Spain (28), California (5), measured by double focusing sector field mass spectrometer Element2 were taken for the analysis below.…”

Section: Analysis Of the Real Distributions Discussionmentioning

confidence: 99%

“…The Anova F-test was used to select the most informative elements at each classification step and enabled reduction of noise. More details on the measurement procedure can be found in [8]. In particular, elements Rb, Sr, Li, and Zn had large F-ratio values and were most useful for the current classification.…”

Section: Analysis Of the Real Distributions Discussionmentioning

confidence: 99%

“…The main tendencies of data variation highlighted by the principal components cannot be necessarily due to the regional effects. Studies in [8], where comparison of the data classification capabilities of the raw element concentration data and the principal components was undertaken, revealed that application of the PC increased classification capabilities in comparison to the raw data in the case under study. In addition, the results of the PC distribution tests were in correspondence Fig.…”

Section: Analysis Of the Real Distributions Discussionmentioning

confidence: 99%

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Quantification of pattern recognition quality by multivariate normal distribution functions

Serapinas¹

2008

Lithuanian J. Phys.

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Analysis of the multivariate data distributions can be helpful or directly applicable in pattern recognition tests. Estimate of the volume of the critical region of overlapping distributions is essential in determination of the confidence level of classification. Mathematical tools for analysis of the multivariate distributions (included probability, false positives and false negatives, means for calculation of the critical region) are developed. Sum of the false negative and the false positive is found as a very approximate characteristic of the total uncertainty of classification. The false negative probability is extremely distribution coordinate dependent and analysis of the details of the overlapping distributions is needed to evaluate the real risk of misclassification of samples. Application of the multivariate distributions to the regional classification of wine samples according to the data of multielement analysis is presented as an example.

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“…The main variables that determine the composition and concentration of minerals in wines are: (a) the mineral content of the soil and the plant's capacity to uptake and accumulate these elements in the grape berry, (b) viticulture and winemaking treatments with metal-containing products (e.g. copper based fungicides and bentonite respectively), and (c) contamination with external sources such as non-stainless steel winery equipment, irrigation water, and air pollution (Ough and Amerine, 1988;Angelova et al, 1999;Jakubowski et al, 1999;Orescanin et al, 2003;Pyrzynska, 2004;Catarino et al, 2006;Mihucz et al, 2006;La Pera et al, 2008;Serapinas et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Analysis of major metallic elements in Chilean wines by atomic absorption spectroscopy

et al. 2010

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V. F. Laurie, E. Villagra, J. Tapia, J. E. S. Sarkis, and Marcos A. Hortellani. 2010. Analysis of major metallic elements in Chilean wines by atomic absorption spectroscopy. Cien. Inv. Agr. 37(2): 77-85. A set of 75 wine samples from seven of the major wine producing regions in Chile were analyzed with flame atomic absorption spectroscopy (AAS) to determine their content of major metallic elements (K, Mg, Ca, Na, Fe and Zn). The results obtained were further analyzed with principal component analysis (PCA) in an attempt to loosely discriminate these wines according to grape type and geographical origin. The metal concentrations measured were within normal ranges, according to previously published data from other wine producing countries. Multivariate statistics allowed the possibility to reasonably discriminate between red and white wines, and to show a distinctive pattern of Na concentration, with higher total contents for the wines produced in the northern parts of the country.

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Step by step approach to multi-element data analysis in testing the provenance of wines

Cited by 41 publications

References 18 publications

Content in metallic ions of wines from the Madeira and Azores archipelagos

Content in metallic ions of wines from the Madeira and Azores archipelagos

Quantification of pattern recognition quality by multivariate normal distribution functions

Analysis of major metallic elements in Chilean wines by atomic absorption spectroscopy

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