The power of fingerprinting of volatiles constituents in fighting illicit and flavoured tobacco products
Bibliographic record
Abstract
Manufacturers of tobacco products aim to attribute dedicated, by the consumer recognisable characteristics to their products. Volatile and semi-volatile organic compounds, are to a large degree determining the sensorial identity of the product. Their composition is influenced by both the tobacco blend and additives added to the product during production. However, for protecting the product identity, manufacturers do not disclose their composition. Producers of illicit tobacco products will hardly be able to mimic the sensorial characteristics of genuine products. This fact offered the opportunity to discriminate genuine from counterfeit tobacco products by chemical analysis of the volatile and semi-volatile constituents, applying gas chromatography high resolution time of flight mass spectrometry. The developed multivariate statistical models allowed to distinguish branded cigarettes from each other, and from counterfeit products. Chemical analysis of the volatile and semi-volatile fraction of tobacco products offers also the possibility to identify sensorial active substances at concentrations, which might attribute a characterising flavour to the respective tobacco product. Respective models were developed at the JRC for discriminating cigarettes with characterising flavours from cigarettes currently on the EU market. The chemical analysis of the cigarettes has shown to be economic and reliable, leading to models with high accuracy. In this sense, it can be used either as a screening tool in monitoring exercises, and/or for confirmation of characteristics identified by a sensory panel.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".