ArticleFrontiers in plant science2022
Optimization of a static headspace GC-MS method and its application in metabolic fingerprinting of the leaf volatiles of 42 citrus cultivars.
Article in Frontiers in plant science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed, 22 citations in OpenAlex.
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- Exploring the Impact of Extraction Methods on the Nutritional and Sensory Profiles of Argan Oil from Mostaganem Kernels.Foods (Basel, Switzerland) · 2026Article
- Analyzing Plant Low-Molecular-Weight Polar Metabolites: A GC-MS Approach.Plants (Basel, Switzerland) · 2026Review
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- Untargeted flower volatilome profiling highlights differential pollinator attraction strategies in muscadine.Frontiers in plant science · 2025Article
- Plant-Derived Terpenoids: A Plethora of Bioactive Compounds with Several Health Functions and Industrial Applications-A Comprehensive Overview.Molecules (Basel, Switzerland) · 2024Review
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Authors and funding
15 authors at 2 institutions in 1 country.
Funding
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Abstract
Citrus leaves, which are a rich source of plant volatiles, have the beneficial attributes of rapid growth, large biomass, and availability throughout the year. Establishing the leaf volatile profiles of different citrus genotypes would make a valuable contribution to citrus species identification and chemotaxonomic studies. In this study, we developed an efficient and convenient static headspace (HS) sampling technique combined with gas chromatography-mass spectrometry (GC-MS) analysis and optimized the extraction conditions (a 15-min incubation at 100 ˚C without the addition of salt). Using a large set of 42 citrus cultivars, we validated the applicability of the optimized HS-GC-MS system in determining leaf volatile profiles. A total of 83 volatile metabolites, including monoterpene hydrocarbons, alcohols, sesquiterpene hydrocarbons, aldehydes, monoterpenoids, esters, and ketones were identified and quantified. Multivariate statistical analysis and hierarchical clustering revealed that mandarin (
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