Study of metabolite differences of flue-cured tobacco from Canada (CT157) and Yunnan (Yunyan 87)
Bibliographic record
Abstract
In order to comprehend the dissimilarities in tobacco quality between Canada and Yunnan, a comparison of the aroma components was conducted using GC-MS and HPLC analysis, coupled with orthogonal partial least squares discriminant analysis (OPLS-DA). The study revealed the detection of a total of 81 aroma components and 22 non-volatile components in both varieties of tobacco leaves. Specifically, there were 102 components of Canada tobacco leaves and 103 components of Yunnan tobacco leaves. Subsequently, a screening was performed on these two types of tobacco leaves, identifying 51 differential components, which accounted for approximately 49.5 % of the overall components detected. Among these, Canada tobacco exhibited a higher concentration of 22 components, comprising roughly 36.4 % of the total, which were primarily composed of semi-volatile organic acids and sesquiterpenes. On the other hand, Yunnan tobacco was characterized by a comparatively higher content of 43 components, constituting approximately 63.6 %, including fatty acid esters, phenols, diterpenes, sugars, and amino acids. Comparatively, Canada tobacco demonstrated elevated levels of fatty acids and sesquiterpenes, while the content of fatty acid esters and diterpenes was relatively lower. These distinctions in aroma components potentially contribute to the varied sensory aroma profiles exhibited by the two types of tobacco.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".