ArticleFrontiers in nutrition2022
Comprehensive lipidomics analysis reveals the changes in lipid profile of camellia oil affected by insect damage.
Article in Frontiers in nutrition, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed, 12 citations in OpenAlex.
- Multi-omics analysis reveals flavor biosynthesis in Camellia seed oil: phenylpropanoid pathway activation and antioxidant modulation of lipoxygenase (LOX).Food chemistry: X · 2026Article
- Advancing quality evaluation and origin tracing of HainanFood chemistry: X · 2026Article
- Integration of lipidomics and widely targeted metabolomics provides a comprehensive metabolic landscape of Poa pratensis under cadmium stress.BMC plant biology · 2025Article
- Unraveling the metabolic profile regulation of camellia oilseeds under insect and heat stress: Insights into functional effects and mechanistic basis.Food chemistry: X · 2024Article
- Evaluation of Lipid Quality in Fruit: Utilizing Lipidomic Approaches for Assessing the Impact of Biotic Stress on Pecans (Foods (Basel, Switzerland) · 2024Article
- Profiling of phenolic composition in camellia oil and its correlative antioxidant properties analysis.Frontiers in nutrition · 2024Article
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6 authors at 1 institution in 1 country.
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Abstract
Information on changes in lipid composition of seed oils under biotic stresses is scare. The camellia weevil, Curculio chinensis (Coleoptera: Curculionidae) as a notorious seed predator of Camellia species, has caused huge economic losses in China. Lipidomics is used in this study to reveal the lipid composition of camellia oil and its changes after insect damage. 278 lipids including glycerolipids (GL) (221), glycerophospholipids (GP) (34), fatty acyls (FA) (13), sphingolipids (SP) (8), prenol lipids (PR) (1) and sterol lipids (ST) (1) were determined in camellia oils. Insect damage had a significant impact on lipids, particularly FA and GL. Ten significantly different lipids [FFA(18:2), FFA(24:6), TG(14:1/18:2/18:2), TG(16:0/23:0/18:2), TG(20:1/24:1/18:2), TG(18:2/24:0/18:2), TG(16:3/18:2/22:5), PI(16:1/18:1), PE(16:0/18:1), PE(18:1/18:2)] were identified as potential biomarkers for distinguishing oil extracted from non-infested oilseeds and oil from infested oilseeds. We also detected four most important metabolic pathways by bioinformatics analysis to explore the mechanisms underlying changes. Our findings may be useful for future camellia oil production and may provide new insight into improving of nutritional quality of camellia oil.
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