Artículo
Data mining approach based on chemical composition of grape skin for quality evaluation and traceability prediction of grapes
Canizo, Brenda Vanina
; Escudero, Leticia Belén
; Pellerano, Roberto Gerardo
; Wuilloud, Rodolfo German
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Fecha de publicación:
07/2019
Editorial:
Elsevier
Revista:
Computers and Eletronics in Agriculture
ISSN:
0168-1699
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
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.
Palabras clave:
GRAPE-SKINS
,
MACHINE LEARNING
,
MINERAL CONTENT
,
PROVENANCE
Archivos asociados
Licencia
Identificadores
Colecciones
Articulos(CCT - MENDOZA)
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - MENDOZA
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - MENDOZA
Articulos(IQUIBA-NEA)
Articulos de INSTITUTO DE QUIMICA BASICA Y APLICADA DEL NORDESTE ARGENTINO
Articulos de INSTITUTO DE QUIMICA BASICA Y APLICADA DEL NORDESTE ARGENTINO
Citación
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
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