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.