Discovery of quality markers in Rubus Chingii Hu using UPLC-ESI-QTOF-MS.

Raspberry, the fruit of Rubus Chingii Hu, has been used as a traditional Chinese medicine (TCM) to nourish kidney and strengthen Yang-qi. In order to determine the quality of raspberry, the quality markers (Q-markers) of raspberry that can improve renal function were investigated using UPLC-ESI-QTOF-MS in this study. The results of serum pharmacochemistry indicated that six components rutin, ellagic acid, kaempferol-3-rutinoside, astragalin, tiliroside, and goshonoside F5 in raspberry were absorbed into rat blood. The HEK293 cells treated with cisplatin were used to evaluate the kidney-protecting activity of these absorbed components. All these components could markedly inhibit cell damage induced by cisplatin and restore the levels of malondialdehyde (MDA) and catalase (CAT) in the cells, suggesting that these components may be the Q-markers of raspberry. More importantly, except for ellagic acid, other five Q-markers in raspberries from Dexing of Jiangxi province were higher than those from most of other areas. It is well known that Dexing raspberry is the Dao-di herbs raspberry used in the clinic of Chinese Medicine, demonstrating that these components could be used as Q-markers of raspberry. This study provides a reliable and valuable method for quality evaluation of raspberry.

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.