Predicting isoscapes based on an environmental similarity model for the geographical origin of Chinese rice
文献类型： 外文期刊
作者： Sheng, Meiling 1 ; Zhang, Weixing 2 ; Nie, Jing 1 ; Li, Chunlin 1 ; Zhu, A-Xing 4 ; Hu, Hao 1 ; Lou, Weidong 1 ; Deng, Xunfei 1 ; Lyu, Xiaonan 1 ; Ren, Zhouqiao 1 ; Rogers, Karyne M. 1 ; Wadood, Syed Abdul 1 ; Zhang, Yongzhi 1 ; Yuan, Yuwei 1 ;
作者机构： 1.Zhejiang Acad Agr Sci, State Key Lab Managing Biot & Chem Threats Qual &, Hangzhou 310021, Peoples R China
2.China Natl Rice Res Inst, Hangzhou 310006, Peoples R China
3.Minist Agr & Rural Affairs, Key Lab Informat Traceabil Agr Prod, Hangzhou 310021, Peoples R China
4.Nanjing Normal Univ, Minist Educ, Key Lab Virtual Geog Environm, Nanjing 210023, Peoples R China
5.Natl Isotope Ctr, GNS Sci, 30 Gracefield Rd, Lower Hutt 5040, New Zealand
6.Univ Home Econ Lahore, Dept Nutr & Hlth Promot, Lahore, Pakistan
关键词： Isoscapes; Rice; Geographical origin; Environmental similarity; Stable isotope
期刊名称：FOOD CHEMISTRY （ 影响因子：9.231； 五年影响因子：8.795 ）
ISSN： 0308-8146
年卷期： 2022 年 397 卷
页码：
收录情况： SCI
摘要： The authentication of geographical origin of food is important using stable isotope analysis. However, the isotopic databank is still short of comprehensive. The isoscapes model based on environmental similarity is used for the first time to predict the geospatial distribution of delta C-13, delta H-2 and delta O-18 in Chinese rice in 2017 and 2018. 794 rice samples in 2017 were used to build isoscapes model. Independent verification shows that the predicted isotope distribution from this new approach is of high accuracy, with a root mean square error (RMSE) of 0.51 parts per thousand, 7.09 parts per thousand and 2.06 parts per thousand for delta C-13, delta H-2 and delta O-18 values for 2017, respectively. Our results indicate that it is possible to predict the spatial distribution of stable isotopes in rice using an isoscapes model based on environmental similarity. This novel strategy can enrich and complement a stable isotope reference database for rice origin identification at regional scale.
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