Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/22944
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DC Field | Value | Language |
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dc.contributor.author | Liu, L. | - |
dc.contributor.author | Cozzolino, D. | - |
dc.contributor.author | Cynkar, W. | - |
dc.contributor.author | Gishen, M. | - |
dc.contributor.author | Colby, C. | - |
dc.date.issued | 2006 | - |
dc.identifier.citation | Journal of Agricultural and Food Chemistry, 2006; 54(18):6754-6759 | - |
dc.identifier.issn | 0021-8561 | - |
dc.identifier.issn | 1520-5118 | - |
dc.identifier.uri | http://hdl.handle.net/2440/22944 | - |
dc.description | Copyright © 2006 American Chemical Society | - |
dc.description.abstract | 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. | - |
dc.description.statementofresponsibility | L. Liu, D. Cozzolino, W. U. Cynkar, M. Gishen, and C. B. Colby | - |
dc.language.iso | en | - |
dc.publisher | Amer Chemical Soc | - |
dc.source.uri | http://pubs.acs.org/cgi-bin/abstract.cgi/jafcau/2006/54/i18/abs/jf061528b.html | - |
dc.subject | Near-infrared | - |
dc.subject | principal component analysis | - |
dc.subject | discriminant partial least-squares | - |
dc.subject | linear discriminant analysis | - |
dc.subject | Tempranillo | - |
dc.subject | wine | - |
dc.subject | geographical origin | - |
dc.title | Geographic classification of Spanish and Australian tempranillo red wines by visible and near-infrared spectroscopy combined with multivariate analysis | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1021/jf061528b | - |
pubs.publication-status | Published | - |
Appears in Collections: | Aurora harvest 2 Chemical Engineering publications |
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