Comparative analysis of fatty acids, volatile and non-volatile components in red huajiao (Zanthoxylum bungeanum maxim.) and green huajiao (Zanthoxylum armatum DC.) using GC-MS, UPLC-LTQ-Orbitrap-MS/MS and HPLC-DAD

In China, huajiao, a traditional industrial crop, is categorized as red huajiao (RHJ; Zanthoxylum bungeanum maxim.) and green huajiao (GHJ; Zanthoxylum armatum DC.) based on the peel color. There were significant differences in sensory and biological activities between RHJ and GHJ in practical application. However, there is no sufficient and comprehensive metabolome-based explanation for these differences so far. The gas chromatography-mass spectrometry (GC-MS), ultra-performance liquid chromatography-linear trap quadrupole-orbitrap mass spectrometry (UPLC-LTQ-Orbitrap-MS/MS), and high-performance liquid chromatography-photodiode array detection (HPLC-DAD) were employed in this study to analyze the differences in fatty acids (FAs), volatile components (VCs) and non-volatile components (non-VCs) between 6 RHJ and 9 GHJ. The findings demonstrated that 12 FAs, 68 VCs, and 101 non-VCs were detected in huajiao, of which 5 FAs, 28 VCs, and 46 non-VCs were identified as differential metabolites (DMs) between RHJ and GHJ. And the DMs were enriched in multiple metabolic pathways by KEGG analysis. Moreover, kaempferol, kaempferide, berberrubine, scoulerine, saponaretin, naringenin, and 3-P-coumaroylquinic acid were identified as key bioactive DMs based on the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and analysis platform. In this study, the possible reasons for the differences in sensory and biological activities between RHJ and GHJ were comprehensively revealed at the metabolic level using non-targeted and targeted metabolomics, and the results provide a better theoretical understanding of the different application of them.

Automated summary

This study comprehensively revealed the differences in sensory and biological activities between red huajiao and green huajiao at the metabolic level using non-targeted and targeted metabolomics. Key bioactive metabolites were identified and potential reasons for the differences were explored through metabolic pathway analysis.