Analysis, Application, and Verification of a Genetically Modified Seed Purity Determination Model Based on Binomial Distribution and F Distribution
LIU Yan-Lai1, LI Zi-He1, YANG Zhan-Sen1, HAN Tian-Yi1, REN Xue-Zhen2, JIN Fang2, YI Hong-Mei3, GAO Hong-Fei4, WU Gang4, CHENG Nan1,*, JIN Shi-Qiao2,*
1 College of Food Science and Nutritional Engineering, China Agricultural University, Beijing 100083, China; 2 National Agro-Tech Extension and Service Center, Beijing 100125, China; 3 Maize Research Center, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China; 4 Oil Crops Research Institute, Chinese Academy of Agricultural Sciences/Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture and Rural Affairs/Inspection and Testing Center (Wuhan) for Plant Ecological Environment Safety, Ministry of Agriculture and Rural Affairs/Key Laboratory of Agricultural Genetically Modified Organisms Traceability, Ministry of Agriculture and Rural Affairs, Wuhan 430062, China
Abstract：With the regular cultivation of genetically modified crops worldwide and the rapid development of genetically modified seeds, some international organizations and relevant countries have established standards for the purity detection of genetically modified seeds. However, the mathematical and logical analysis of the detection schemes in these standards is still insufficient. This study aimed to provide theoretical support and design guidance for understanding and using detection schemes based on binomial distribution and F-distribution through systematic formula derivation, model analysis, application and verification. This study first started from the sources of risks in the experimental design, analyzed the key factors affecting the detection of seed transgenic purity, and proposed solutions. Secondly, a mathematical model was constructed based on the binomial distribution to quantify producer risk and user risk, and these risks were evaluated through operation curves. The third briefly described the principles of single-step and dual-step schemes. Through the analysis of mathematical models and operation curves, this study quantified the producer risk and user risk in the purity detection of genetically modified seeds, and deduced calculation formulas applicable to different detection scenarios. The scientificity and applicability of this mathematical model were verified through representative cases of germination detection of genetically modified corn and herbicide-tolerant seeds. This study systematically demonstrated the scientific nature of the mathematical model for seed transgenic purity detection based on binomial distribution and F-distribution, enhanced the transparency and reproducibility of transgenic seed purity detection methods, facilitated the correct understanding and application of standards by detection personnel. This study provides mathematical support for the formulation and revision of relevant detection norms in the future.
刘燕来, 李梓赫, 杨湛森, 韩天意, 任雪贞, 晋芳, 易红梅, 高鸿飞, 吴刚, 程楠, 金石桥. 基于二项分布和F分布的种子转基因纯度测定模型分析、应用及验证[J]. 农业生物技术学报, 2026, 34(2): 407-419.
LIU Yan-Lai, LI Zi-He, YANG Zhan-Sen, HAN Tian-Yi, REN Xue-Zhen, JIN Fang, YI Hong-Mei, GAO Hong-Fei, WU Gang, CHENG Nan, JIN Shi-Qiao. Analysis, Application, and Verification of a Genetically Modified Seed Purity Determination Model Based on Binomial Distribution and F Distribution. 农业生物技术学报, 2026, 34(2): 407-419.
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