Advances in Computational Biology Methods and Applications for Nutritional Metabolomics of Vegetable Crops

As an essential component of systems biology, metabolomics provides novel technological approaches and theoretical frameworks for studying nutritional quality in vegetable crops. This review summarizes recent advances in computational biology methods for vegetable crop nutritional metabolomics, including key technologies such as data preprocessing, statistical analysis, machine learning, network analysis, and pathway enrichment analysis. We focus on the applications of these methods in vegetable nutritional composition analysis, quality evaluation, stress response mechanism elucidation, and breeding-assisted selection. The current challenges are analyzed, including data standardization, method standardization, and multi-omics data integration. Future development trends are discussed to provide references for vegetable crop nutritional metabolomics research.