A strategy combining solid-phase extraction, multiple mass defect filtering and molecular networking for rapid structural classification and annotation of natural products: characterization of chemical diversity in Citrus aurantium as a case study

Medicinal plants are complex chemical systems containing thousands of secondary metabolites. The rapid classification and characterization of the components in medicinal plants using mass spectrometry (MS) remains an immense challenge. Herein, a novel strategy is presented for MS through the combination of solid-phase extraction (SPE), multiple mass defect filtering (MMDF) and molecular networking (MN). This strategy enables efficient classification and annotation of natural products. When combined with SPE and MMDF, the improved analytical method of MN can perform the rapid annotation of diverse natural products in Citrus aurantium according to the tandem mass spectrometry (MS/MS) fragments. In MN, MS2LDA can be initially applied to recognize substructures of natural products, according to the common fragmentation patterns and neutral losses in multiple MS/MS spectra. MolNetEnhancer was adopted here to obtain chemical classifications provided by ClassyFire. The results suggest that the integrated SPE-MMDF-MN method was capable of rapidly annotating a greater number of natural products from Citrus aurantium than the classical MN strategy alone. Moreover, SPE and MMDF enhanced the effectiveness of MN for annotating, classifying and distinguishing different types of natural products. Our workflow provides the foundation for the automated, high-throughput structural classification and annotation of secondary metabolites with various chemical structures. The developed approach can be widely applied in the analysis of constituents in natural products.

Automated summary

A novel strategy using mass spectrometry (MS) combined with solid-phase extraction (SPE), multiple mass defect filtering (MMDF), and molecular networking (MN) was developed for rapid classification and annotation of components in medicinal plants. By integrating SPE and MMDF with MN, a greater number of natural products from Citrus aurantium could be rapidly annotated, classified, and distinguished. This workflow provides a foundation for automated structural classification and annotation of secondary metabolites with various chemical structures, and can be widely applied in the analysis of constituents in natural products.