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Determination of the geographic origin of rice by element fingerprints and correlation analyses with the soil of origin

机译:用元素指纹法确定稻的地理起源及与起源土壤的相关性分析

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The objective of this study was to select the elements associated with the geographical origin of rice and evaluate the traceability efficiency of these elements by a chemometrics method. The contents of 15 elements (Mg, K, Ca, Na, Be, Mn, Ni, Cu, Cd, Fe, Al, Cr, Zn, Sb, and Pb) in rice samples and the contents of their exchangeable, available form, and "total" form in soil samples from four provinces of China were analyzed by inductively coupled plasma mass spectrometry and flame atomic absorption spectrometry. The results of variance analysis and correlation analysis demonstrated that there is a significant difference in the contents for 9 of 15 elements (Mg, K, Ca, Na, Be, Mn, Ni, Cu, and Cd) in the rice samples from 4 provinces, and the 9 elements in rice samples are closely connected with the mineral elements in soil. R-type hierarchical cluster analysis of 15 elements in rice indicated that Mg, Cu, K, Ni, Be, Mn, and Ca clustered together with similar behavior in rice and soil. The predictions of the geographic origin made by linear discriminant analysis (LDA) based on 9 elements gave an overall correct classification rate of 100.0% and a cross-validation rate of 93.8%, which was superior to the result for the total 15 elements. The correct rate of Q-type hierarchical cluster analysis (Q-type CA) based on 9 elements was higher than that based on 15 elements. The result of Q-type CA and LDA demonstrated that the 9 elements are good indicators to discriminate the geographical origin of rice.
机译:这项研究的目的是选择与大米地理起源相关的元素,并通过化学计量学方法评估这些元素的可追溯性效率。大米样品中15种元素(Mg,K,Ca,Na,Be,Mn,Ni,Cu,Cd,Fe,Al,Cr,Zn,Sb和Pb)的含量及其可交换和可利用的形式的含量,用电感耦合等离子体质谱法和火焰原子吸收光谱法分析了中国四个省份土壤样品中的“总”形态。方差分析和相关性分析的结果表明,四个省的水稻样品中15种元素(镁,钾,钙,钠,铍,锰,镍,铜和镉)中的9种含量存在显着差异。 ,大米样品中的9种元素与土壤中的矿物质元素紧密相关。水稻中15种元素的R型分层聚类分析表明,Mg,Cu,K,Ni,Be,Mn和Ca在水稻和土壤中具有相似的聚集性。通过线性判别分析(LDA)对9个元素进行的地理起源预测给出了100.0%的总体正确分类率和93.8%的交叉验证率,这优于15个元素的结果。基于9个元素的Q型层次聚类分析(Q型CA)的正确率高于基于15个元素的Q型聚类分析的正确率。 Q型CA和LDA的结果表明,这9个元素是区分水稻地理起源的良好指标。

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