ArticleFrontiers in plant science2026
Construction of a sensory quality evaluation model for tobacco leaves from Henan Province and its application in tobacco quality assessment.
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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12 authors.
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Abstract
This study was designed to establish a sensory-oriented quality evaluation model for tobacco leaves, systematically elucidate the key chemical basis underlying the sensory attributes of tobacco from representative growing regions in Henan Province, and identify the key aroma components influencing their quality. A total of 47 tobacco leaf samples were collected from three major producing areas in Henan Province, namely Pingdingshan, Sanmenxia, and Nanyang. Professional sensory evaluation and gas chromatography-mass spectrometry (GC-MS) were employed to quantitatively analyze 29 chemical constituents, including nicotine. Multivariate statistical approaches, such as principal component analysis (PCA) and correlation analysis, were applied to construct the sensory quality evaluation model and identify key aroma components. Furthermore, the effects of different agronomic factors (fertilization, harvesting methods, plant growth vigor, and single-leaf weight) on these key components were investigated. PCA revealed that the first two principal components cumulatively accounted for 52% of the total variance, representing sweet/fruity and green/woody characteristics, respectively. Correlation analysis identified 13 key aroma components significantly associated with sensory quality, including 3-hydroxy-2-butanone, benzaldehyde, linalool,
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