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dc.contributor.author
Camiña, José Manuel  
dc.contributor.author
Savio, Marianela  
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Azcarate, Silvana Mariela  
dc.contributor.author
Furlong, Octavio Javier  
dc.contributor.author
Marchevsky, Eduardo Jorge  
dc.contributor.other
El Rayess, Youssef  
dc.date.available
2022-03-30T16:53:51Z  
dc.date.issued
2014  
dc.identifier.citation
Camiña, José Manuel; Savio, Marianela; Azcarate, Silvana Mariela; Furlong, Octavio Javier; Marchevsky, Eduardo Jorge; Chemometric Methods for the Classification of White Wines; Nova Science Publishers; 10; 2014; 245-279  
dc.identifier.isbn
978-1-63321-048-6  
dc.identifier.uri
http://hdl.handle.net/11336/154062  
dc.description.abstract
Wines are products whose cost depends on several quality factors, required by the customer: geographical origin, variety of grape, oak aging, etc. The Controlled Denomination of Origin (CDO) of wines is frequently desired due to several properties, which depend on characteristics of the different places of origin around the world, including weather, grape variety, crop, temperature variation, winery practices, etc. The control of the properties mentioned above, is usually difficult by traditional methods, since it is necessary to determine several specific variables such as trace elements, organic acids, phenolic compounds, etc., which require expensive equipment, expert operators, long-time analysis and pretreatment of samples, among other undesired aspects. However, the introduction of chemometric tools has simplified the interpretation and analysis of data, allowing the use of a great number of variables (data matrix), filtering only the most important information and leaving out the noisy data. These tools represent a fundamental advantage since they allow the implementation of several spectroscopic methods, which were not useful for such complex analysis: UV-Vis, infrared spectroscopy, nuclear magnetic resonance, and fluorescence methods, which could be used thanks to their multivariable advantage. Based on the benefits of chemometric methods in comparison to traditional methods, this chapter will involve the implementation of the most recent tools used for wine analysis around the world. The determinations include Controlled Denomination of Origin (CDO), geographical and/or botanical origin, and other important quality properties of wines. Chemometric tools include artificial neural network analysis (ANN), Principal Component Analysis (PCA), Cluster Analysis (CA), Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Partial Least Square Discriminant Analysis (PLS-DA), Soft Independent Modeling of Class Analogy (SIMCA), among others. This chapter has been organized in topics based on the application of methods for quality analysis in white wines, according to botanical and geographical classification, as well as other quality analysis, describing for each one, all the chemometric and analytical methods available for the determination of quality properties, for an easy reading and understanding.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Nova Science Publishers  
dc.rights
info:eu-repo/semantics/restrictedAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
CHEMOMETRIC  
dc.subject
MULTIVARIATE  
dc.subject
CLASSIFICATION  
dc.subject
WHITE  
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WINES  
dc.subject.classification
Química Analítica  
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Ciencias Químicas  
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CIENCIAS NATURALES Y EXACTAS  
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Horticultura, Viticultura  
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Agricultura, Silvicultura y Pesca  
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CIENCIAS AGRÍCOLAS  
dc.title
Chemometric Methods for the Classification of White Wines  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.type
info:eu-repo/semantics/bookPart  
dc.type
info:ar-repo/semantics/parte de libro  
dc.date.updated
2022-01-27T18:12:24Z  
dc.journal.number
10  
dc.journal.pagination
245-279  
dc.journal.pais
Estados Unidos  
dc.journal.ciudad
Nueva York  
dc.description.fil
Fil: Camiña, José Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias de la Tierra y Ambientales de La Pampa. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; Argentina  
dc.description.fil
Fil: Savio, Marianela. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias de la Tierra y Ambientales de La Pampa. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; Argentina  
dc.description.fil
Fil: Azcarate, Silvana Mariela. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Ciencias de la Tierra y Ambientales de La Pampa. Universidad Nacional de La Pampa. Facultad de Ciencias Exactas y Naturales. Instituto de Ciencias de la Tierra y Ambientales de La Pampa; Argentina  
dc.description.fil
Fil: Furlong, Octavio Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Física Aplicada "Dr. Jorge Andrés Zgrablich". Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Instituto de Física Aplicada "Dr. Jorge Andrés Zgrablich"; Argentina  
dc.description.fil
Fil: Marchevsky, Eduardo Jorge. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis. Instituto de Química de San Luis. Universidad Nacional de San Luis. Facultad de Química, Bioquímica y Farmacia. Instituto de Química de San Luis; Argentina  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://novapublishers.com/shop/wine-phenolic-composition-classification-and-health-benefits/  
dc.conicet.paginas
348  
dc.source.titulo
Wine: Phenolic Composition, Classification and Health Benefits