Intra-regional classification of grape seeds produced in Mendoza province (Argentina) by multi-elemental analysis and chemometrics tools

The feasibility of the application of chemometric techniques associated with multi-element analysis for the classification of grape seeds according to their provenance vineyard soil was investigated. Grape seed samples from different localities of Mendoza province (Argentina) were evaluated. Inducti...

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Detalhes bibliográficos
Autores: Canizo, Brenda Vanina, Escudero, Leticia Belén, Pérez, María Belén, Pellerano, Roberto Gerardo, Wuilloud, Rodolfo German
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2018
País:Argentina
Recursos:Consejo Nacional de Investigaciones Científicas y Técnicas
Repositorio:CONICET Digital (CONICET)
Idioma:inglés
OAI Identifier:oai:ri.conicet.gov.ar:11336/96186
Acesso em linha:http://hdl.handle.net/11336/96186
Access Level:acceso abierto
Palavra-chave:CHEMOMETRICS
GRAPE SEEDS
ICP-MS
MULTI-ELEMENTAL ANALYSIS
MULTIVARIATE CLASSIFICATION
https://purl.org/becyt/ford/1.4
https://purl.org/becyt/ford/1
Descrição
Resumo:The feasibility of the application of chemometric techniques associated with multi-element analysis for the classification of grape seeds according to their provenance vineyard soil was investigated. Grape seed samples from different localities of Mendoza province (Argentina) were evaluated. Inductively coupled plasma mass spectrometry (ICP-MS) was used for the determination of twenty-nine 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). Once the analytical data were collected, supervised pattern recognition techniques such as linear discriminant analysis (LDA), partial least square discriminant analysis (PLS-DA), k-nearest neighbors (k-NN), support vector machine (SVM) and Random Forest (RF) were applied to construct classification/discrimination rules. The results indicated that nonlinear methods, RF and SVM, perform best with up to 98% and 93% accuracy rate, respectively, and therefore are excellent tools for classification of grapes.