Impact of Internal and External Factors on Terpenoid Profiles in Cannabis sativa Leaves: Metabolomic Approach
DOI:
https://doi.org/10.48048/tis.2026.12528Keywords:
Cannabis sativa, Terpenoid, Caryophyllene, Secondary metabolite, Metabolomic, Machine learning, Smart Farming, Yield prediction, Decision tree, AdaBoostAbstract
Leaf maturity levels, cultivation plans, and growing seasons affected monoterpene and sesquiterpene production in Cannabis sativa leaves. Twenty-two terpenoid compounds were investigated from fresh leaves using headspace GC-MS. Among them, the major monoterpenes were α-pinene, β-myrcene, limonene, and β-ocimene, and the major sesquiterpenes were β-(E)-caryophyllene, (Z,E)-α-farnesene, β-bisabolene, (E)-α-bisabolene, aromadendrene, α-humulene, and α-bisabolol. All compounds showed significant correlations. However, all 3 factors influenced variation in 9 compounds, excluding α-pinene and β-pinene. β-(E)-caryophyllene, α-humulene, and β-ocimene were affected more by growing season than by cultivation plan or leaf maturity and were particularly abundant during the cool-dry season. Cultivation method (evaporative greenhouse, net greenhouse, or outdoor) significantly affected key terpenoids, including β-myrcene and limonene. Leaf maturity level significantly affected the production of aromadendrene and α-humulene. Machine learning algorithms were used to predict terpenoid levels based on internal and external factors. Model performance was evaluated using RMSE, R2, and correlation. The decision tree (DT) achieved the lowest prediction error for β-(E)-caryophyllene and was therefore the best model for this compound, whereas AdaBoost (ADA) performed best for α-humulene and 4 other major compounds.
HIGHLIGHTS
- Cannabis sativa Hang Krarok exhibits a distinct terpenoid profile compared with other varieties.
- Terpenoid variation in leaves is driven by leaf maturity, cultivation practices, and growing season.
- Machine learning improves prediction of mono- and sesquiterpenoid profiles in sativa leaves.
- The model supports farm management strategies to optimize secondary metabolite production for industrial applications.
GRAPHICAL ABSTRACT
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