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中文摘要：
【目的】探讨矿物元素指纹分析技术对小麦产地溯源的可行性,筛选出判别小麦产地溯源的有效指标。【方法】利用电感耦合等离子体质谱（ICP-MS）测定来自河北省、河南省、山东省和陕西省4个地域120份小麦样品中24种矿物元素（Be、Na、Mg、Al、K、Ca、V、Cr、Mn、Fe、Co、Ni、Cu、Zn、Se、Mo、Ag、Cd、Sb、Ba、Tl、Pb、Th和U）的含量,对数据进行方差分析、主成分分析和判别分析。【结果】不同地域小麦样品的元素含量有其各自的特征。河北样品的V含量最高,Ca含量最低;河南样品的Cr含量最高;山东样品的Ba和Ni含量最高;陕西样品的Na、Al、Mn、Fe、Co、Cu、Ag、Sb和Pb含量都显著高于其它地区,V含量最低。主成分分析结果表明,不同地域来源的大多数样品能被正确区分。通过逐步判别分析筛选出11项可用于小麦产地判别的矿物元素指标,依次为Ba、Mn、Sb、Ca、Mo、U、Ni、V、Cr、Pb和Mg,所建立的判别模型对样品整体检验判别率为90.8%,交叉检验判别率为89.2%。【结论】矿物元素指纹分析技术结合多元统计学方法是用于小麦产地判别的一种有效方法。
英文摘要：
Objective The preliminary investigation was carried out to examine the feasibility of multi-element analysis in identifying the geographical origin of wheat,and to screen out the effective indicators in wheat origin assessment.Method The concentrations of 24 chemical elements（Be,Na,Mg,Al,K,Ca,V,Cr,Mn,Fe,Co,Ni,Cu,Zn,Se,Mo,Ag,Cd,Sb,Ba,Tl,Pb,Th and U） have been determined using inductively coupled plasma mass spectrometry（ICP-MS） in 120 wheat samples from Hebei,Henan,Shandong and Shaanxi provinces of China,and analysis of variance（ANOVA）,principal component analysis（PCA） and discriminant analysis（DA） were applied in data analysis.Result The element contents of wheat from different regions were different.The content of V was the highest and Ca was the lowest in the samples from Hebei,and Cr was the highest in those from Henan.However,the contents of Ba and Ni were the highest in those from Shandong,and Na,Al,Mn,Fe,Co,Cu,Ag,Sb and Pb were the highest and V was the lowest in those from Shaanxi,respectively.Most of the samples were classified correctly into different categories according to region by PCA.Furthermore,eleven key variables（Ba,Mn,Sb,Ca,Mo,U,Ni,V,Cr,Pb and Mg） were identified by stepwise discriminant analysis to develop the discriminant models by which 90.8% correct classification and 89.2% cross validation were achieved.Conclusion It is a promising approach to classify wheat geographical origin based on multi-element analysis combined with multivariate statistical analysis.
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