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结合多元素和稳定同位素数据及多变量分析对亚洲栽培稻(Oryza sativa L.)进行地理溯源

Geographic authentication of Asian rice (Oryza sativa L.) using multi-elemental and stable isotopic data combined with multivariate analysis.

作者信息

Chung Ill-Min, Kim Jae-Kwang, Lee Kyoung-Jin, Park Sung-Kyu, Lee Ji-Hee, Son Na-Young, Jin Yong-Ik, Kim Seung-Hyun

机构信息

Department of Crop Science, College of Sanghuh Life Science, Konkuk University, Seoul 05029, Republic of Korea.

Division of Life Sciences, College of Life Sciences and Bioengineering, Incheon National University, Incheon 22012, Republic of Korea.

出版信息

Food Chem. 2018 Feb 1;240:840-849. doi: 10.1016/j.foodchem.2017.08.023. Epub 2017 Aug 4.

Abstract

Rice (Oryza sativa L.) is the world's third largest food crop after wheat and corn. Geographic authentication of rice has recently emerged asan important issue for enhancing human health via food safety and quality assurance. Here, we aimed to discriminate rice of six Asian countries through geographic authentication using combinations of elemental/isotopic composition analysis and chemometric techniques. Principal components analysis could distinguish samples cultivated from most countries, except for those cultivated in the Philippines and Japan. Furthermore, orthogonal projection to latent structure-discriminant analysis provided clear discrimination between rice cultivated in Korea and other countries. The major common variables responsible for differentiation in these models were δS, Mn, and Mg. Our findings contribute to understanding the variations of elemental and isotopic compositions in rice depending on geographic origins, and offer valuable insight into the control of fraudulent labeling regarding the geographic origins of rice traded among Asian countries.

摘要

水稻(Oryza sativa L.)是继小麦和玉米之后的世界第三大粮食作物。通过食品安全和质量保证来促进人类健康,水稻的地理认证最近已成为一个重要问题。在此,我们旨在通过元素/同位素组成分析和化学计量技术的组合,通过地理认证来区分六个亚洲国家的水稻。主成分分析可以区分大多数国家种植的样本,但菲律宾和日本种植的样本除外。此外,正交投影到潜在结构判别分析提供了韩国种植的水稻与其他国家种植的水稻之间的清晰区分。这些模型中导致差异的主要共同变量是δS、Mn和Mg。我们的研究结果有助于理解水稻中元素和同位素组成随地理来源的变化,并为控制亚洲国家间交易水稻的地理来源欺诈性标签提供有价值的见解。

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