Pesticide residue detection techniques for increasing productivity and yield: recent progress and future outlooks - Umer, Muhammad; Naseer, Abid; Mubeen, Mustansar; Iftikhar, Yasir; Umer, Rafia; Akram, Ayesha; Altaf, Muhammad Tanveer; Ibrahim, Essam H.; Ahmed, Ahmed Ezzat; Duan, Mingzheng (Frontiers in Plant Science, 2025)
The intensive use of pesticides in modern agriculture has significantly improved crop yield and food security but introduced serious health concerns due to the accumulation of pesticide residues in fruits and vegetables and the environment, posing serious health risks. This review comprehensively explores the various residue detection techniques and plant metabolomics as an emerging tool to unravel the biochemical and physiological consequences of pesticide exposure. The article critically evaluates current methodologies for pesticide residue analysis, encompassing sampling strategies, storage considerations, and a wide range of extraction techniques, including QuEChERS, solid-phase extraction (SPE), and emerging green alternatives such as supercritical fluid extraction and ultrasound-assisted extraction. A detailed comparison of analytical techniques particularly gas chromatography (GC), liquid chromatography (LC), mass spectrometry (MS), and novel non-separative methods such as biosensors and spectroscopy is presented, emphasizing sensitivity, specificity, and adaptability to complex matrices. Furthermore, the integration of metabolomics with advanced platforms such as machine learning, green chemistry principles, and microfluidic innovations is discussed as a transformative direction for future pesticide residue monitoring. The review is a novel compilation of conventional residue detection methods and emerging omics-driven, artificial intelligence (AI)-assisted approaches and identifies current limitations, including matrix interferences and regulatory disparities, and advocates for the harmonization of residue standards, alongside the development of cost-effective, high-throughput analytical platforms to ensure food safety, improve risk assessment, and enhance understanding of plant metabolic responses under pesticide stress. Moreover, multi-omics approaches can be more reliable in evaluating the quality of claimed organic farming products.