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.
Food Chem. 2019 Jun 15;283:305-314. doi: 10.1016/j.foodchem.2018.12.129. Epub 2019 Jan 14.
Herein, we improve the procedure for organic rice authentication using compound-specific δC and δN analyses of fatty acids and amino acids, addressing the increasing demand for accurate methods to confirm organic authenticity. Organic rice (OR) and pesticide-free rice (PFR) featured higher values of δN than conventional rice (CR), whereas the corresponding differences between OR and PFR were insignificant. Additionally, OR, PFR, and CR could be discriminated based on some δC and δN values. δC was correlated with most δC (r ≥ 0.596) values, and δN was strongly correlated with most δN (r ≥ 0.834) values. The first component in the orthogonal projection to latent structure-discriminant analysis model allowed for a clear separation between OR and PFR, and good predictability (QY = 0.506). Thus, the present study improves the reliability of organic authentication when bulk stable isotope ratio analysis alone is insufficient for the accurate discrimination of OR, PFR, and CR.
本文通过对脂肪酸和氨基酸的复合特异性 δC 和 δN 分析，改进了有机大米认证的程序，以满足对准确确认有机真实性方法的需求不断增加。与常规大米（CR）相比，有机大米（OR）和无农药大米（PFR）的 δN 值更高，而 OR 和 PFR 之间的相应差异并不显著。此外，还可以根据一些 δC 和 δN 值来区分 OR、PFR 和 CR。δC 与大多数 δC（r≥0.596）值相关，δN 与大多数 δN（r≥0.834）值高度相关。正交投影偏最小二乘判别分析模型的第一个分量允许 OR 和 PFR 之间的清晰分离，并且具有良好的可预测性（QY=0.506）。因此，当单独进行批量稳定同位素比分析不足以准确区分 OR、PFR 和 CR 时，本研究提高了有机认证的可靠性。
