Many natural product chemists are working to identify a wide variety of novel secondary metabolites from natural materials and are eager to avoid repeatedly discovering known compounds. Here, we developed liquid chromatography/mass spectrometry (LC/MS) data-processing protocols for assessing high-throughput spectral data from natural sources and scoring the novelty of unknown metabolites from natural products. This approach automatically produces representative MS spectra (RMSs) corresponding to single secondary metabolites in natural sources. In this study, we used the RMSs of Agrimonia pilosa roots and aerial parts as models to reveal the structural similarities of their secondary metabolites and identify novel compounds, as well as isolation of three types of nine new compounds including three pilosanidin- and four pilosanol-type molecules and two 3-hydroxy-3-methylglutaryl (HMG)-conjugated chromones. Furthermore, we devised a new scoring system, the Fresh Compound Index (FCI), which grades the novelty of single secondary metabolites from a natural material using an in-house database constructed from 466 representative medicinal plants from East Asian countries. We expect that the FCIs of RMSs in a sample will help natural product chemists to discover other compounds of interest with similar chemical scaffolds or novel compounds and will provide insights relevant to the structural diversity and novelty of secondary metabolites in natural products.

Plants produce numerous secondary metabolites with diverse physicochemical properties. Because different parts of a single plant produce various components, several spectroscopic methods are necessary to inspect their chemical profiles. Mass spectral data are recognized as one of the most useful tools for analyzing components with a wide range of polarities. However, interpreting mass spectral data generated from positive and negative ionization modes is a challenging task because of the diverse chemical profiles of secondary metabolites. Herein, we combine and analyze mass spectral data generated in two ionization modes to detect as many metabolites as possible using the molecular networking approach. We selected different parts of a single plant, Morus alba (Moraceae), which are used in the functional food and medicinal herb industries. The mass spectral data generated from two ionization modes were combined and analyzed using various molecular networking workflows. We confirmed that our approach could be applied to simultaneously analyze the different types of secondary metabolites with different physicochemical properties.

The husks and fruits of Zanthoxylum species (Rutaceae) are the popular pungent and spicy ingredients of foods and the traditional medicines in many countries. Three Zanthoxylum species, Z. bungeanum, Z. schinifolium, and Z. piperitum, are distributed and intermixed with each other as “Zanthoxyli Pericarpium” in Korean markets. In the present study, we analyzed the ethyl acetate-soluble and nonpolar fractions of Zanthoxylum samples by ¹H NMR spectrometry and performed a multivariate analysis for finding the discriminant markers between three species. Xanthoxylin was identified as the metabolic marker for the discrimination of Zanthoxylum species and quantified by the qNMR approach.

For centuries, four Rosaceae species—Malus sieboldii, Sorbus commixta, Duchesnea indica, and Prunus sargentii—have been prized for their pharmacological properties. In this study, solvent extracts from the seeds of these species were prepared, and their total polyphenol and flavonoid contents were measured. Antioxidant capacity was evaluated using 1,1-diphenyl-2-picrylhydrazyl (DPPH) and 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical scavenging assays, as well as ferric reducing antioxidant power (FRAP) and Fe²⁺ chelation tests. Compounds in the extracts were identified through molecular networking with the Global Natural Products Social Molecular Networking (GNPS) platform. Among all samples, D. indica extract contained the highest polyphenol and flavonoid concentrations (335.63 ± 0.03 mg gallic acid equivalents (GAE) per gram of extract and 230.14 ± 2.90 mg rutin equivalents (RE) per gram of extract, respectively). It also exhibited the strongest antioxidant activity in DPPH, ABTS, and FRAP assays, with statistically significant outcomes. Liquid chromatography/mass spectrometry analysis and molecular networking revealed a diverse metabolite profile corresponding to seven biosynthetic pathways in the extracts. Notably, D. indica extract was rich in shikimates, phenylpropanoids, and ellagic acid derivatives, which have potent antioxidant effects. These results suggest a strong relationship between the extract’s chemical profile and its biological activity, offering promising opportunities to use D. indica seeds as functional ingredients across various fields.

The fragmentation information of ions from high-resolution mass spectrometry (HRMS) is gaining popularity as chemometric data for inspecting molecular distribution and changes in metabolites in food ingredients. Efficient analytical approaches are needed to handle the numerous signals from HRMS. Molecular networking (MN) methods were developed to generate the network between metabolites based on pattern similarities between MS spectra. We combined MN with in silico annotation methods and the multivariate analysis to discover the discriminative markers among four Citrus samples. Six chemical markers suggested by these approaches were confirmed via HPLC-UV methods and quantitatively monitored based on the species and maturity.