Integrating LC‐MS/MS and Molecular Networking for Advance Analysis and Comprehensive Flavonoids Annotation

ABSTRACT Flavonoids, a subclass of phenolic compounds, exhibit diverse therapeutic properties, including antioxidant, anti‐inflammatory, and wound healing properties. These health‐promoting effects of flavonoids are greatly dependent on the variation in their structural diversity, which are generally perceived as complex metabolomic datasets. Among detection techniques used, state‐of‐the‐art high‐resolution liquid chromatography hyphenated with tandem mass spectrometry (LC‐MS/MS) has become a popular analytical method of choice for the analysis of flavonoids from various plant tissue extracts. Despite its broad applicability, the vast amount and complexity of fragmentation data produced have made the comprehensive identification of flavonoids remains a key challenge. An offshoot of metabolomics, currently, molecular networking (MN), a computational approach based on MS/MS data, has emerged as a revolutionary technique for identifying and characterizing numerous flavonoid molecular families. By visualizing the spectral similarities of flavonoid fingerprints, MN enables rapid dereplication, efficient in assisting annotation of unknown features with known chemical scaffolds, and demonstrates high precision in resolving structurally diverse flavonoid isomers. Various MN tools, i.e., classical molecular networking (CLMN), feature‐based molecular networking (FBMN), and substructure‐based MN (MS2LDA), streamline the identification process and improve the understanding of flavonoids biosynthesis. This review aimed to describe the recent advancement in MS‐based strategy for flavonoids characterization, starting with an overview on the application of LC‐MS/MS and its limitation in the typical dereplication workflow, followed by specific sections on MN techniques, highlighting the aspects of general principles, workflow, and its application in flavonoid research.