文献类型： 外文期刊

作者： Zhi Liu ; Yuwei Yuan +; Tongzhou Xie ; Yongzhi Zhang ; Shengzhi Shao ; Jing Nie ; Wei Xia ; Karyne M. Rogers *; Weixing Zhang *

作者机构：

关键词： Organic Rice; Stable Isotope; Trace Element; Pls-Da Modeling Organic Authentication; Climate Effects

期刊名称： JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY

ISSN： 0021-8561

年卷期： 2020 年 68 卷 5 期

页码：

收录情况： JCR（2021版） ; EI（2021版）

摘要： Organically farmed rice is believed to be healthier, safer, and eco-friendlier than its conventionally farmed counterparts and sells for a premium price in global markets. Deliberate mislabeling of organic rice has become a critical consumer concern in China and elsewhere, and there is an increased risk of buying fraudulent organic rice in the market place. In this study, stable isotopic and multielemental analysis combined with chemometrics was used to differentiate organically farmed rice from green and conventional rice in a 4-year experimental field trial from 2014 to 2017. A total of 108 rice samples and their associated soils were collected during the study from three farming (fertilization) systems to investigate whether there are long-term changes in the rice farming classification accuracy from climate effects. Stable carbon and nitrogen isotopic ratios (i.e., delta C-13 and delta N-15) and 27 elemental contents (e.g., Na, K, Ca, Fe, and Zn) of rice and soil samples were determined and then evaluated using statistical analysis [i.e., one-way analysis of variance, multivariable correlation analysis, and modeling of partial least-squares discriminant analysis]. Although delta N-15 values can be an effective indicator for organic rice authentication during one crop rotation, both delta C-13 and delta N-15 values of rice were easily affected by rice cultivar and interannual soil fertilization and localized agroclimatic variations. These two isotopes were not able to separate organic rice from green and conventional rice accurately. Elemental contents of green and conventional rice (especially K and Ca) were found at higher levels due to the abundant application of synthetic fertilizers (e.g., KNO3, KH2PO4, and CaHPO4), unlike organically farmed rice, which primarily used animal manure and composts. Partial least-squares discriminant analysis modeling combined isotopic and elemental signatures to correctly differentiate organic rice from green and conventional counterparts, with an accuracy up to 100% over the 4-year study. Therefore, this multi-isotope and -element strategy proposes a more rigorous, alternative tool to combat fraudulent mislabeling of organic rice, increasing the trust of organically labeled rice products and supporting the integrity of the organic sector worldwide.

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