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Enregistrement W2198329211 · doi:10.2478/cttr-2013-0859

Determination of “Hoffmann Analytes” in Cigarette Mainstream Smoke. The Coresta 2006 Joint Experiment

2009· article· en· W2198329211 sur OpenAlexaff
M Intorp, S Purkis, M. Whittaker, William Wright

Notice bibliographique

RevueBeiträge zur Tabakforschung international · 2009
Typearticle
Langueen
DomaineMedicine
ThématiqueSmoking Behavior and Cessation
Établissements canadiensD2L (Canada)
Organismes subventionnairesnon disponible
Mots-clésAnalyteSidestream smoketar (computing)Cigarette smokeStrengths and weaknessesComputer scienceSmokeBiochemical engineeringChemistryEngineeringChromatographyPsychologyWaste managementEnvironmental healthMedicine

Résumé

récupéré en direct d'OpenAlex

Abstract Regulatory authorities are currently discussing the measurement of and imposition of ceilings on certain smoke analytes, the so called ‘Hoffmann analytes’. However, as a pre-requisite, the measurement methods and the tolerances around the measurements first need to be established. In 1999, the Cooperation Centre for Scientific Research Relative to Tobacco (CORESTA) set up a Task Force ‘Special Analytes’ to deal with analytical methodology for measuring ‘Hoffmann analytes’ under International Standard (ISO) smoking and to work towards the standardisation of methods. This paper describes the output and conclusions from a 2005-2006 joint experiment made within the Task Force representing laboratories currently able to analyse these compounds. Data were obtained on most ‘Hoffmann analytes’ from reference cigarettes (2R4F and 1R5F), collecting data according to the existing methods used by the nineteen participating laboratories, in order to describe the within and among laboratory variability and to see which methods could most benefit from more rigorous standardisation work. In some cases, the applied statistical analysis found that methods could not well differentiate the 1R5F and 2R4F cigarettes of differing ‘tar’ yield. This was explained, in part, by the broad range of methods used by the participating laboratories but also indicated that there were significant inadequacies in the choice of some methods or weaknesses in their application. Results indicate that ‘Hoffmann analyte’ data are generally more variable both within and among laboratories than nicotine free dry particulate matter (NFDPM); nicotine and carbon monoxide due to their lower smoke yields. Accordingly, tolerances around methods adopted for regulatory purposes will need to be proportionately higher. Methods for benzo[ a ]pyrene (B[ a ]P) and tobacco-specific nitrosamines (TSNAs), already taken to CORESTA recommended methods or ISO standardised methods through the efforts of this Task Force, give some of the most reproducible results, showing the value of this process. However, these data strongly suggest that even these analytes have much higher among-laboratory variability than for NFDPM, nicotine and CO and, based on the only two available one point in time studies, may need tolerances in the range of 35-45% for B[a]P and 26-55% for TSNAs, if they are to be measured for regulatory purposes. The collected data is useful to participating laboratories for internal method validation and laboratory accreditation, and data comparisons with others allow laboratories to identify strengths and weaknesses in their current methods. However, much work still needs to be carried out to take most of the methods towards standardisation. Although some fundamental differences or areas of concern around the methodology are discussed herein, they are not comprehensive and there may be others that need to be addressed before methods can be considered ready to take to a Recommended Method and/or to an ISO Standard. These methodological issues are being addressed in further CORESTA work within this Task Force. Smoke analytes with the highest variability found in this study and those analytes that are currently of highest regulatory interest are being prioritised and after further joint experiments, the results are intended to be published.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,088
Score d'incertitude au seuil0,552

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,033
Tête enseignante GPT0,318
Écart entre enseignants0,286 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations41
Publié2009
Routes d'admission1
Résumé présentoui

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