Centro de Ciências Naturais e Humanas, Universidade Federal do ABC, Rua Santa Adélia 166, Vila São Pedro, Santo Andre, SP, Brazil.
Centro de Quimica e Meio Ambiente, Ipen/CNEN-SP - Instituto de Pesquisas Energeticas e Nucleares/Comissao Nacional de Energia Nuclear, Av. Lineu Prestes, 2242 Sao Paulo, SP, Brazil.
Food Chem. 2019 Dec 1;300:125145. doi: 10.1016/j.foodchem.2019.125145. Epub 2019 Jul 8.
Rice geographical traceability requires analytical procedures and data evaluation capable of linking its composition to the producing area. In this work, major and trace elements in soil and rice grains and husk from 9 cities and 17 producers were evaluated. Arsenic species were measured solely in rice grains. The rice mineral profile evaluated by principal component analysis allowed the identification of controlling variables and origin fingerprints. Vectors controlling data variability were linked to the geographical area, to crop management, producers and in a lower extent to soil composition. Elemental discrimination through 3D models was proposed. Arsenic species in the grains and elemental husk composition were decisive to achieve the required discrimination. Rice discrimination was obtained by cities, producers and varieties. The present work model was compared with others from similar studies.
大米的地理溯源需要能够将其成分与其产地联系起来的分析程序和数据评估。在这项工作中，评估了来自 9 个城市和 17 个生产者的土壤和稻谷及稻壳中的常量和微量元素。仅在稻谷中测量了砷的形态。通过主成分分析评估的大米矿物质图谱允许识别控制变量和起源特征。控制数据变化的向量与地理区域、作物管理、生产者以及在较小程度上与土壤成分有关。通过三维模型提出了元素鉴别。稻谷和元素稻壳成分中的砷形态对实现所需的鉴别至关重要。通过城市、生产者和品种对大米进行了鉴别。本工作模型与其他类似研究的模型进行了比较。
