Geographical differentiation of white wines from three subzones of the designation of origin Valencia

Álvarez, Inmaculada; Aleixandre, José Luis; García, María J.; Casp, Ana; Zunica, L.

doi:10.1007/s00217-003-0715-2

Cited by 12 publications

(10 citation statements)

References 8 publications

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“…If the components have sufficient discriminatory power, the set of their concentrations will form a characteristic pattern or 'fingerprint' relating to the geographical origin of the sample. Therefore the use of MDA provides the ability to detect these patterns, and is essentially helpful when the number of components necessary to differentiate samples from different geographical origins increases (Alvarez, Aleixandre, Garcia, Casp, & Zenica, 2003;Castiñeira, Feldmann, Jakubowski, & Andersson, 2004;Reid, O'Donnell, & Downey, 2006). Sauvignon Blanc (Vitis vinifera L., var.…”

Section: Introductionmentioning

confidence: 99%

Can spectroscopy geographically classify Sauvignon Blanc wines from Australia and New Zealand?

et al. 2011

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Section: Introductionmentioning

confidence: 99%

Can spectroscopy geographically classify Sauvignon Blanc wines from Australia and New Zealand?

et al. 2011

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“…Additionally, the use of multivariate statistical techniques (chemometrics) on chemical and sensory data has gained increasing attention as a tool to classify wines from different geographical regions and to describe similar sensory and chemical characteristics. A diverse range of physicochemical parameters have been measured in wines to classify samples according to geographic origin, such as phenolic compounds (5,12), macro-and trace elements (6,7,10,11), physical and chemical characteristics (8)(9)(10), amino acids and biogenic amines (13), and volatile compounds (4,14). Although these methods provide valuable information, most of them involve time-consuming, laborious, and costly procedures.…”

Section: Introductionmentioning

confidence: 99%

Geographic Classification of Spanish and Australian Tempranillo Red Wines by Visible and Near-Infrared Spectroscopy Combined with Multivariate Analysis

Liu

Cozzolino

Cynkar

et al. 2006

J. Agric. Food Chem.

128

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Visible (vis) and near-infrared (NIR) spectroscopy combined with multivariate analysis was used to classify the geographical origin of commercial Tempranillo wines from Australia and Spain. Wines (n = 63) were scanned in the vis and NIR regions (400-2500 nm) in a monochromator instrument in transmission. Principal component analysis (PCA), discriminant partial least-squares discriminant analysis (PLS-DA) and linear discriminant analysis (LDA) based on PCA scores were used to classify Tempranillo wines according to their geographical origin. Full cross-validation (leave-one-out) was used as validation method when PCA and LDA classification models were developed. PLS-DA models correctly classified 100% and 84.7% of the Australian and Spanish Tempranillo wine samples, respectively. LDA calibration models correctly classified 72% of the Australian wines and 85% of the Spanish wines. These results demonstrate the potential use of vis and NIR spectroscopy, combined with chemometrics as a rapid method to classify Tempranillo wines accordingly to their geographical origin.

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“…[10][11][12][13][14]. Among all these techniques, Discriminant Analysis is frequently used to differentiate alcoholic beverages [15,16,17,18]. It is applied to populations or groups of samples previously identified as such, enabling the classification of each individual later on, so as to verify that it belongs to the established group.…”

Section: Introductionmentioning

confidence: 99%

Sensory Characterization of Commercial Lager Beers and Their Correlations with Iso-α-Acid Concentrations

Blanco¹,

Nimubona²,

Fernández‐Fernández³

et al. 2014

JFNR

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A total of 40 commercial lager beers (6 dark, 28 pale and 6 alcohol free) were analysed by HighPerformance Liquid Chromatography (HPLC) and evaluated by a descriptive sensorial panel. Discriminant Analysis was applied to 15 sensory descriptors to obtain a classification by types of beer. Cluster Analysis identified 4 clusters grouping the 15 sensory descriptors. The Correlation Matrix confirmed a correlation between them and the subsequent Principal Component Analysis. A stepwise discriminant analysis was used to eliminate the less significant descriptors, leading to 100% of the samples being correctly classified with a reduced number of variables (colour intensity, smell of caramel, smell of toasted, persistence and viscosity). The analysis of the correlations between iso-α-acid concentrations and sensory descriptors shows a good relationship with bitter taste. Thus, HPLC data can be used for predicting lager beer bitterness through the mathematical correlation developed in this paper.

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Geographical differentiation of white wines from three subzones of the designation of origin Valencia

Cited by 12 publications

References 8 publications

Can spectroscopy geographically classify Sauvignon Blanc wines from Australia and New Zealand?

Can spectroscopy geographically classify Sauvignon Blanc wines from Australia and New Zealand?

Geographic Classification of Spanish and Australian Tempranillo Red Wines by Visible and Near-Infrared Spectroscopy Combined with Multivariate Analysis

Sensory Characterization of Commercial Lager Beers and Their Correlations with Iso-α-Acid Concentrations

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Geographical differentiation of white wines from three subzones of the designation of origin Valencia

Cited by 12 publications

References 8 publications

Can spectroscopy geographically classify Sauvignon Blanc wines from Australia and New Zealand?

Can spectroscopy geographically classify Sauvignon Blanc wines from Australia and New Zealand?

Geographic Classification of Spanish and Australian Tempranillo Red Wines by Visible and Near-Infrared Spectroscopy Combined with Multivariate Analysis

Sensory Characterization of Commercial Lager Beers and Their Correlations with Iso-&#945;-Acid Concentrations

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Sensory Characterization of Commercial Lager Beers and Their Correlations with Iso-α-Acid Concentrations