Recent Advances in Omics Technology in Food Flavor

Abstract

Flavor is a key determinant of food quality, and elucidating its formation mechanisms is of great significance for product optimization and satisfying consumer preferences. Traditional sensory analysis is inherently subjective, while instrumental analysis can objectively assess flavor components but provides limited insight into the formation mechanisms. This article adopts a narrative literature review to summarize the applications of omics technologies in food flavor research in recent years. It focuses on genomics, transcriptomics, proteomics, metabolomics, lipidomics, and multi-omics technologies. It points out that current studies remain largely confined to identifying differential molecular features, lacking in-depth exploration from molecular interactions to causal mechanisms. At the same time, single-omics methods can only provide partial information and cannot capture the multi-level regulatory networks of flavor formation. A causal inference framework for analyzing flavor mechanisms at the omics level through multi-omics integration and intervention experiments has been proposed. It emphasizes that artificial intelligence technology provides a promising solution for the interactive analysis of complex multi-omics datasets. Future research should focus on developing low-cost, high-throughput real-time analysis methods, cross-omics data fusion strategies, and the rigorous exploration of scientific causal relationships.