人工智能驱动的洞察：农药残留文献中方法验证的程度分析

IF 6.2 1区农林科学 Q1 AGRICULTURE, MULTIDISCIPLINARY

Journal of Agricultural and Food Chemistry Pub Date : 2025-06-06 DOI:10.1021/acs.jafc.5c0457410.1021/acs.jafc.5c04574

Leah S. Riter*, Steven J. Lehotay and John Swarthout,

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引用次数: 0

摘要

验证分析方法以评估价值数字和其他关键绩效参数是适用性概念中的基本要求。通过结合生成式人工智能和主题审查，这篇观点文章提供了分析趋势、技术进步和分析报告的现状，这些分析报告涉及农业应用中涉及质谱的农药残留方法的验证。分析参数和技术进步的报告趋势是根据1970年至2024年发表在《农业与食品化学杂志》上的391项研究的数据集进行评估的。该可行性研究表明，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。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Insights Powered by Artificial Intelligence: Analyzing the Extent of Method Validation in Pesticide Residue Literature

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 Journal of Agricultural and Food Chemistry from 1970 to 2024. This feasibility study demonstrated that with properly optimized prompts and performance verification, AI can efficiently and accurately evaluate scientific literature.

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来源期刊

Journal of Agricultural and Food Chemistry 农林科学-农业综合

CiteScore

9.90

自引率

8.20%

发文量

1375

审稿时长

2.3 months

期刊介绍： The Journal of Agricultural and Food Chemistry publishes high-quality, cutting edge original research representing complete studies and research advances dealing with the chemistry and biochemistry of agriculture and food. The Journal also encourages papers with chemistry and/or biochemistry as a major component combined with biological/sensory/nutritional/toxicological evaluation related to agriculture and/or food.