Profiles volatile compounds in citrus leaves, suggesting diverse applications in food and health.
High Resolution Image Download MS PowerPoint Slide Citrus plant parts and especially citrus leaves are an underexplored but promising source of volatile substances, with applications in food, nutraceuticals, and biocontrol. In this study, we systematically profiled leaf volatile organic compounds from 21 citrus species and cultivars grown under uniform conditions. Mature leaves were air-dried, homogenized, extracted, and analyzed by gas chromatography–mass spectrometry, leading to compound identification supported by spectral database matching, relative retention indices, and verification by using commercial reference standards. More than 100 volatile constituents were identified, dominated by monoterpene hydrocarbons (notably limonene, β-pinene, sabinene, β-myrcene) and species-selective oxygenated monoterpenes (e.g., linalool, citronellal, linalyl acetate). Absolute quantification was achieved using gas chromatography-flame ionization detection with external calibration and an internal standard for 25 major volatile constituents. Concentrations were mostly in the ng·mg –1 range, with many compounds at moderate levels of 10–100 ng·mg –1, and others reaching high concentrations of 100–1,000 ng·mg –1 across most taxa, while pronounced chemotypic variation was revealed: limonene was omnipresent in all samples as the most frequently occurring compound overall, ranging from 3.70 ± 0.06 ng·mg –1 in F. japonica to more than 1 μg·mg –1 in limes and citron. Other volatiles showed a much more diagnostic presence, as, for instance, methyl N -methylanthranilate and linalyl acetate were diagnostic markers for mandarin and C. myrtifolia . Methyl N -methylanthranilate reached the highest quantified level of a volatile, with more than 7 μg·mg –1 in leaves of C. myrtifolia . Principal component analysis of gas chromatography–mass spectrometry data resolved chemometric clusters that largely reflected phylogenetic relationships.
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Sebastian Beck (2026) studied this question.
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