Laboratorio de Química Analítica para Investigación y Desarrollo (QUIANID), Facultad de Ciencias Exactas y Naturales, Universidad Nacional de Cuyo, Instituto Interdisciplinario de Ciencias Básicas (ICB), UNCUYO-CONICET, Padre J. Contreras 1300, 5500 Mendoza, Argentina.
Instituto de Química Básica y Aplicada del Nordeste Argentino (IQUIBA-NEA), CONICET, Facultad de Ciencias Exactas y Naturales y Agrimensura, Universidad Nacional del Nordeste (UNNE), Av. Libertad 5470, 3400 Corrientes, Argentina.
Food Chem. 2018 Mar 1;242:272-278. doi: 10.1016/j.foodchem.2017.09.062. Epub 2017 Sep 14.
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.
研究了化学计量学技术与多元素分析相结合，根据产地葡萄园土壤对葡萄种子进行分类的应用可行性。评估了来自阿根廷门多萨省不同地区的葡萄种子样本。电感耦合等离子体质谱法（ICP-MS）用于测定 29 种元素（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 和 Zr）。收集分析数据后，应用了监督模式识别技术，如线性判别分析（LDA）、偏最小二乘判别分析（PLS-DA）、k-最近邻（k-NN）、支持向量机（SVM）和随机森林（RF），以构建分类/判别规则。结果表明，非线性方法 RF 和 SVM 的表现最佳，准确率分别高达 98%和 93%，因此是葡萄分类的优秀工具。
