基于多元素分析的冬枣产地鉴别方法

夏立娅; 高巍; 李亚平; 尹洁璇; 张晓瑜; 李晓杨

doi:10.13386/j.issn1002-0306.2016.24.001

基于多元素分析的冬枣产地鉴别方法

1. 河北大学质量技术监督学院
2. 中国农业大学工学院

基金项目:

国家自然科学基金资助项目(31501447)；河北省自然科学基金资助项目(B2013201235)；河北大学自然科学研究项目(2014-02)；大学生创新训练计划项目(201610075038)；

详细信息

作者简介:
夏立娅(1978-),女,博士研究生,副教授,研究方向:食品品质评定及原产地鉴别,E-mail:xialiya@126.com。;

中图分类号: TS255.7
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- 文章访问数: 143
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出版历程
- 收稿日期: 2016-06-27

Identification of Ziziphus jujuba origin by multi-element analysis

College of Quality & Technical Supervision,Hebei University
College of Engineering,China Agricultural University

摘要

摘要: 为了探讨利用产地间差异性元素进行产地判别的可行性,测定了不同产地冬枣样本中10种元素的含量,并对数据进行了差异性分析、聚类分析、Fisher判别分析和偏最小二乘判别分析（partial least squares discrimination analysis,PLS-DA）。结果表明,不同产地冬枣中Mg、B、Mn、Fe、Zn元素存在显著差异,是具有产地特征的指纹元素。R型系统聚类分析也证实B、Mn、Fe和Zn元素具有共同特征。基于产地特征元素和Q型聚类、Fisher判别和PLS-DA建立的冬枣产地鉴别模型正确率均高于基于全部元素的分析结果,其中利用特征元素建立的PLS-DA模型鉴别正确率最高,回代检验和交叉检验正确率均为94.0%,Q型聚类模型的判别能力最差,最高的判别正确率为84.06%。本研究证实了产地间差异性元素是有效的产地判别因子,具有监督模式的Fisher判别和PLS-DA算法准确率远高于无监督模式的系统聚类法,更适于产地鉴别分析。
- 冬枣 /
- 元素 /
- 判别分析 /
- 偏最小二乘
Abstract: In order to explore the feasibility of using the feature elements between origins to identify origins,the contents of 10 elements in Ziziphus jujubas which collected from different producing areas were measured,and the data were analyzed by difference analysis,cluster analysis,Fisher discriminant analysis and partial least squares discriminant analysis( PLS- DA).The results showed that there were significant difference in the contents of Mg,B,Mn,Fe,Zn and other elements from the two places of origin,and R- type hierarchical cluster analysis of 10 elements indicated that B,Mn,Fe and Zn element clustered together with similar behaviors.The correct classification rate of geographic origin made by Fisher discriminant analysis,Q- type hierarchical cluster analysis and PLS- DA based on feature elements were closed to the result of total elements.The highest classification rate was made by PLS- DA based on feature elements,which gave an overall correct classification rate of 94.0% and cross- validation rate of 94.0%.The correct rate of Q- type hierarchical cluster analysis was lowest,which was only84.06%.This study confirmed that the feature elements between origins was effective in predictions of geographic origin,and supervised modes Fisher discriminant analysis and PLS- DA were superior to unsupervised mode Q- type hierarchical cluster analysis at origin traceability.
- Ziziphus jujuba /
- element /
- discriminant analysis /
- partial least squares

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