ArticleFrontiers in plant science2026
Discriminating flue-cured tobacco cultivars based on sensory quality and their underlying volatile and chemical profiles.
Article in Frontiers in plant science, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Machine learning-driven identification of key chemical determinants of tobacco leaf sensory irritation.Frontiers in chemistry · 2026Article
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9 authors.
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Abstract
Tobacco is a globally significant agricultural commodity, in which analytical chemistry plays a pivotal role for quality assessment. This study aimed to develop an integrated analytical strategy to decipher the quality traits of flue-cured tobacco cultivars. An integrated framework combining sensory evaluation, biochemical characterization, and volatile organic compound (VOC) profiling was applied to eleven flue-cured tobacco cultivars. Statistical analyses included hierarchical cluster analysis (HCA) and partial least squares-discriminant analysis (PLS-DA) based on VOC data, and Spearman correlation analysis (with Bonferroni correction) to explore relationships between sensory attributes, biochemical components, and VOCs. Sensory analysis categorized the cultivars into two groups: Group I (YN228, YN105, YN223, GY20, and YN87) scored higher in aftertaste, offensive taste, and moistness, while Group II (YN222, XY7, NC103, TZ113, GY2, and XZ01) excelled in aroma quality and cleanness. Biochemical profiling also revealed two distinct groups: Group I (YN228, TZ113, NC103, and YN87) had higher nitrogen and alkaloid contents, whereas Group II (YN222, XY7, GY20, XZ01, GY2, YN105, and YN223) exhibited elevated sugar and potassium levels. VOC-based HCA and PLS-DA identified three chemical clades and highlighted 75 VOCs (VIP > 1.0) as key differentiators. Significant correlations were established between biochemical components and sensory attributes; notably, total sugar content was positively correlated with irritancy, aroma quality, and smoke concentration. Specific VOCs, such as (furan-2-yl)methanol and 3-oxo-α-ionol, showed significant positive correlations with moistness and smoke concentration, respectively, while phenol was negatively correlated with aroma quality. This study establishes a reproducible sensory-omics framework that provides a robust analytical foundation for the quality assessment of agricultural products. The findings demonstrate the practical value of integrated analytical approaches in addressing complex quality assessment challenges, offering actionable insights for the evaluation and potential improvement of tobacco cultivars.
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