ArticleFrontiers in plant science2025
Metabolomic analysis of Yunnan cigar tobacco leaves: impact of geography and climate on flavor characteristics and machine learning-based origin traceability.
Article in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed.
- Elucidating the linkage between non-volatile precursors and particulate-phase flavor compounds in mainstream smokeFrontiers in plant science · 2026Article
- Machine learning-driven identification of key chemical determinants of tobacco leaf sensory irritation.Frontiers in chemistry · 2026Article
- Geographic authentication of premium tobacco extracts using machine learning models trained on LC-HRMS fingerprints.Frontiers in molecular biosciences · 2026Article
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Authors and funding
15 authors.
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Abstract
To investigate how Yunnan's distinctive geographical and climatic conditions shape the unique metabolic profile of its cigar tobacco leaves (CTLs), and to establish a reliable method for origin traceability using machine learning, a non-targeted metabolomics analysis was conducted on 71 CTL samples collected from the Dominican Republic, Indonesia, and Yunnan, including Lincang, Pu'er, and Yuxi within Yunnan. A total of 778 highly reliable metabolites were identified. Influenced by Yunnan's high altitude, large diurnal temperature variation, intense ultraviolet radiation, and relative dryness, its CTLs exhibited characteristic metabolic profiles, with significant enrichment in pathways such as flavone and flavonol biosynthesis and betalain biosynthesis. Elevated levels of polyphenols, indoles, jasmonates, carotenoids, and other compounds were linked to Yunnan CTLs' distinct woody, roasted, and astringent flavor profile. Twelve key biomarkers were selected using Multivariate methods with unbiased variable selection in R (MUVR). Machine learning algorithms-including LDA, LR, GMM, KNN, and SVM-were applied to these biomarkers, achieving highly accurate origin discrimination across national (Yunnan vs. Dominican Republic/Indonesia) and regional (Lincang, Pu'er, Yuxi) scales. Validation results showed a median false classification rate of 0.1 over 100 iterations and an AUC close to 1, confirming the model's high accuracy and robustness for CTLs origin traceability.
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