Chemical chemical derivatization-assisted LC-MS for food analysis: Analytical principles, performance enhancement,...

The increasing complexity of food matrices and the demand for comprehensive foodomics have exposed the limitations of conventional LC-MS workflows in chemical coverage, structural annotation, and quantitative analysis. Chemical derivatization provides an effective strategy to address these challenges through selective functional-group modification and integration with advanced analytical approaches, enabling improved chromatographic performance, ionization efficiency, and structural characterization. Recent advances integrating derivatization with high-resolution mass spectrometry, stable isotope encoding, retention time prediction, and computational annotation have further expanded its role from sensitivity enhancement to information-rich analysis. This review summarizes recent developments in derivatization-assisted LC-MS strategies for food analysis from 2013 to 2026. Derivatization approaches targeting amino, hydroxyl, carbonyl, carboxyl, and other functional groups are systematically discussed, with emphasis on reaction principles, analytical advantages, representative applications, and methodological limitations. Particular attention is given to the role of derivatization in improving molecular coverage, structural confidence, and quantitative reliability in complex food matrices. Remaining challenges include balancing reaction selectivity and applicability, improving workflow standardization, and enhancing automation compatibility. Future advances will rely on integrating selective derivatization chemistry with advanced MS technologies and computational approaches to achieve more comprehensive, reliable, and accurate characterization of chemically diverse compounds in complex food systems.