Riter Leah S, Lehotay Steven J, Swarthout John
Bayer U.S.─Crop Science, 700 Chesterfield Parkway West, Chesterfield, Missouri 63017, United States.
U.S. Department of Agriculture, Agricultural Research Service, Eastern Regional Research Center, 600 East Mermaid Lane, Wyndmoor, Pennsylvania 19038, United States.
J Agric Food Chem. 2025 Jun 18;73(24):14776-14782. doi: 10.1021/acs.jafc.5c04574. Epub 2025 Jun 6.
Validation of analytical methods to assess figures of merit and other key performance parameters is a fundamental requirement within the fitness-for-purpose concept. By combining generative AI and subject matter review, this perspective article provides insights into analytical trends, technological advancements, and the current state of analytical reporting with respect to validation of published pesticide residue methods involving mass spectrometry in agricultural applications. Reporting trends of analytical parameters and technological advancements were evaluated across a data set of 391 studies published in the from 1970 to 2024. This feasibility study demonstrated that with properly optimized prompts and performance verification, AI can efficiently and accurately evaluate scientific literature.
验证分析方法以评估品质因数和其他关键性能参数是“适用目的”概念中的一项基本要求。通过结合生成式人工智能和主题审查，这篇观点文章深入探讨了分析趋势、技术进步以及在农业应用中涉及质谱法的已发表农药残留方法验证方面的分析报告现状。我们对1970年至2024年发表的391项研究的数据集进行了分析参数报告趋势和技术进步的评估。这项可行性研究表明，通过适当优化提示和性能验证，人工智能可以高效、准确地评估科学文献。
