Mostrar el registro sencillo del ítem

dc.contributor.author
Canizo, Brenda Vanina  
dc.contributor.author
Escudero, Leticia Belén  
dc.contributor.author
Pellerano, Roberto Gerardo  
dc.contributor.author
Wuilloud, Rodolfo German  
dc.date.available
2020-05-12T19:40:52Z  
dc.date.issued
2019-07  
dc.identifier.citation
Canizo, Brenda Vanina; Escudero, Leticia Belén; Pellerano, Roberto Gerardo; Wuilloud, Rodolfo German; Data mining approach based on chemical composition of grape skin for quality evaluation and traceability prediction of grapes; Elsevier; Computers and Eletronics in Agriculture; 162; 7-2019; 514-522  
dc.identifier.issn
0168-1699  
dc.identifier.uri
http://hdl.handle.net/11336/104947  
dc.description.abstract
The knowledge of wine origin is an important aspect in winemaking industries due to the Denomination of Controlled Origin. In this work, a data mining algorithms comparison study of grape-skin samples from five regions of Mendoza, Argentina, and builds classification models capable of predicting provenance based on multi-elemental composition, were developed. Inductively coupled plasma mass spectrometry (ICP-MS) was used to determine 29 elements (Ag, As, Ce, Co, Cs, Cu, Eu, Fe, Ga, Gd, La, Lu, Mn, Mo, Nb, Nd, Ni, Pr, Rb, Sm, Te, Ti, Tl, Tm, U, V, Y, Zn and Zr). Four classification techniques, including multinomial logistic regression (MLR), k-nearest neighbors (k-NN), support vector machines (SVM), and random forests (RF) were assessed. The best results were achieved for SVM and RF models, with 84% and 88.9% prediction accuracy, respectively, on the 10-fold cross validation. The RF variable importance showed that Rb (rubidium) was the most relevant components for prediction.  
dc.format
application/pdf  
dc.language.iso
eng  
dc.publisher
Elsevier  
dc.rights
info:eu-repo/semantics/restrictedAccess  
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/  
dc.subject
GRAPE-SKINS  
dc.subject
MACHINE LEARNING  
dc.subject
MINERAL CONTENT  
dc.subject
PROVENANCE  
dc.subject.classification
Química Analítica  
dc.subject.classification
Ciencias Químicas  
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS  
dc.title
Data mining approach based on chemical composition of grape skin for quality evaluation and traceability prediction of grapes  
dc.type
info:eu-repo/semantics/article  
dc.type
info:ar-repo/semantics/artículo  
dc.type
info:eu-repo/semantics/publishedVersion  
dc.date.updated
2020-04-24T17:58:14Z  
dc.journal.volume
162  
dc.journal.pagination
514-522  
dc.journal.pais
Países Bajos  
dc.journal.ciudad
Amsterdam  
dc.description.fil
Fil: Canizo, Brenda Vanina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales. Laboratorio de Química Analítica para Investigación y Desarrollo; Argentina  
dc.description.fil
Fil: Escudero, Leticia Belén. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales. Laboratorio de Química Analítica para Investigación y Desarrollo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina  
dc.description.fil
Fil: Pellerano, Roberto Gerardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Química Básica y Aplicada del Nordeste Argentino. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Química Básica y Aplicada del Nordeste Argentino; Argentina  
dc.description.fil
Fil: Wuilloud, Rodolfo German. Universidad Nacional de Cuyo. Facultad de Ciencias Exactas y Naturales. Laboratorio de Química Analítica para Investigación y Desarrollo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina  
dc.journal.title
Computers and Eletronics in Agriculture  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0168169918314248  
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1016/j.compag.2019.04.043