Rapid identification of radical scavenging compounds from Camellia japonica leaves through the integration of feature-based molecular networking and statistical approach
Camellia japonica L. is widely cultivated as an ornamental plant, yet its potential as a source of bioactive compounds has been underexplored. Building on our previous research into the bioactive triterpenoids of C. japonica leaves water extract, this study combines UPLC-Q-TOF untargeted analysis, multivariate statistical methods, and classical phytochemistry to rapidly identify the radical scavenging compounds. Mass spectrometry data provided global compound profiling and relative quantification, and PLS-DA combined with correlation analysis identified 76 potential antioxidant compounds from 537 detected features. Molecular networking revealed their distribution within the complex chemical matrix, and 44 compounds were annotated. 11 representative phenolic compounds were isolated, and their structures confirmed by NMR spectroscopy. The DPPH • scavenging assay showed that all 11 compounds exhibited radical scavenging activities, with six outperforming resveratrol and four surpassing ascorbic acid. In conclusion, a method for rapidly identifying antioxidant active compounds from the complex chemical systems of C. japonica was presented, and its effectiveness was confirmed. • Building on previous work, this study explores DPPH scavengers in C. japonica. • UPLC-MS 2 and statistical analysis identified 76 potential DPPH radical scavengers. • Molecular Networking mapped active molecule distribution in chemical space. • 44 compounds were annotated through mass spectrometry library matching. • 11 compounds were confirmed by NMR, with 4 exhibiting stronger activity than ascorbic acid.
Authors
- Lisha Bi
- Qi-Run Chen
- Zhenyu Guo (ORCID: https://orcid.org/0000-0001-5595-6251)
- Hui Liu (ORCID: https://orcid.org/0000-0003-0578-4062)
- Hai‐Yang Liu (ORCID: https://orcid.org/0000-0002-1050-6254)
- Feifei Wang (ORCID: https://orcid.org/0000-0002-4878-0638)
- Ruiqi Liu (ORCID: https://orcid.org/0000-0003-2929-0569)
- Huan Yan
- Wei Ni (ORCID: https://orcid.org/0000-0002-5729-827X)
Institutions
- Shenyang Pharmaceutical University (CN)
- Liaoning University (CN)
- Kunming Institute of Botany (CN)
- Yunnan University (CN)
- University of Chinese Academy of Sciences (CN)
Publication Details
- Journal
- LWT
- Published
- 2025-04-03
- DOI
- https://doi.org/10.1016/j.lwt.2025.117730
- Citations
- 3
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 4.51