info:eu-repo/semantics/article
Data mining approach based on chemical composition of grape skin for quality evaluation and traceability prediction of grapes
Fecha
2019-07Registro en:
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
0168-1699
CONICET Digital
CONICET
Autor
Canizo, Brenda Vanina
Escudero, Leticia Belén
Pellerano, Roberto Gerardo
Wuilloud, Rodolfo German
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.