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Identification of Cigarette Brands by Soft Independent Modeling of Class Analogy of Volatile Substances.

ABSTRACT: INTRODUCTION:This study aimed to develop a method for discriminating cigarette brands based on the profiles of volatile components extracted from the tobacco fraction of the finished cigarettes to authenticate branded cigarettes of unknown origin. METHODS:An analytical method comprising direct thermal desorption coupled with gas chromatography-quadrupole time-of-flight mass spectrometry was developed for acquiring volatile profiles of cigarettes. About 290 samples of commercially available cigarettes were analyzed. Within this batch, 123 samples represented four popular cigarette brands. They were selected for in-depth characterization. Multivariate analysis was used to investigate the interrelations among volatile compounds of cigarettes and to identify characteristic markers for the cigarette discrimination. Supervised pattern recognition techniques were used for designing classification models. RESULTS:Principal component analysis covering all detected volatiles allowed the differentiation of cigarettes based on the brand. A number of 56 volatile components were identified as markers with high discrimination power. These compounds were used for establishing classification models. A method of soft independent modeling of class analogy developed for the four studied cigarette brands proved to be efficient in the classification of unknown cigarettes, with accuracy between 95.9% and 100%. CONCLUSIONS:The data evaluation by soft independent modeling of class analogy was highly accurate in classification of unknown cigarettes with a low rate of false positives and false negatives. The developed models can be used for discrimination of genuine from non-genuine products with high level of probability. IMPLICATIONS:Profiling of volatiles, which is commonly used for authentication of different food commodities, was applied for the characterization of cigarette tobacco for the purpose of authentication a cigarette brand. Volatile components with a high discrimination power were identified by means of multivariate statistical methods and used for establishing of a classification model. The classification model was able to discriminate genuine from non-genuine cigarettes with a high level of prediction accuracy. This model could be a powerful tool for tobacco control to judge the authenticity of cigarettes.

SUBMITTER: Zelinkova Z

PROVIDER: S-EPMC7249919 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:IntroductionA significant portion of the increased risk of cancer and respiratory disease from exposure to cigarette smoke is attributed to volatile organic compounds (VOCs). In this study, 21 VOCs were quantified in mainstream cigarette smoke from 50U.S. domestic brand varieties that included high market share brands and 2 Kentucky research cigarettes (3R4F and 1R5F).MethodsMainstream smoke was generated under ISO 3308 and Canadian Intense (CI) smoking protocols with linear smoking machines with a gas sampling bag collection followed by solid phase microextraction/gas chromatography/mass spectrometry (SPME/GC/MS) analysis.ResultsFor both protocols, mainstream smoke VOC amounts among the different brand varieties were strongly correlated between the majority of the analytes. Overall, Pearson correlation (r) ranged from 0.68 to 0.99 for ISO and 0.36 to 0.95 for CI. However, monoaromatic compounds were found to increase disproportionately compared to unsaturated, nitro, and carbonyl compounds under the CI smoking protocol where filter ventilation is blocked.ConclusionsOverall, machine generated "vapor phase" amounts (µg/cigarette) are primarily attributed to smoking protocol (e.g., blocking of vent holes, puff volume, and puff duration) and filter ventilation. A possible cause for the disproportionate increase in monoaromatic compounds could be increased pyrolysis under low oxygen conditions associated with the CI protocol.ImplicationsThis is the most comprehensive assessment of volatile organic compounds (VOCs) in cigarette smoke to date, encompassing 21 toxic VOCs, 50 different cigarette brand varieties, and 2 different machine smoking protocols (ISO and CI). For most analytes relative proportions remain consistent among U.S. cigarette brand varieties regardless of smoking protocol, however the CI smoking protocol did cause up to a factor of 6 increase in the proportion of monoaromatic compounds. This study serves as a basis to assess VOC exposure as cigarette smoke is a principle source of overall population-level VOC exposure in the United States.

Project description:BackgroundFlavored electronic cigarettes (e-cigarettes) have become very popular in recent years. e-Cigarette users like to share their e-cigarette products and e-cigarette use (vaping) experiences on social media. e-Cigarette marketing and promotions are also prevalent online.ObjectiveThis study aims to develop a method to identify new e-cigarette brands and flavors mentioned on Twitter and to monitor e-cigarette brands and flavors mentioned on Twitter from May 2021 to December 2021.MethodsWe collected 1.9 million tweets related to e-cigarettes between May 3, 2021, and December 31, 2021, by using the Twitter streaming application programming interface. Commercial and noncommercial tweets were characterized based on promotion-related keywords. We developed a depletion method to identify new e-cigarette brands by removing the keywords that already existed in the reference data set (Twitter data related to e-cigarettes from May 3, 2021, to August 31, 2021) or our previously identified brand list from the keywords in the target data set (e-cigarette-related Twitter data from September 1, 2021, to December 31, 2021), followed by a manual Google search to identify new e-cigarette brands. To identify new e-cigarette flavors, we constructed a flavor keyword list based on our previously collected e-cigarette flavor names, which were used to identify potential tweet segments that contain at least one of the e-cigarette flavor keywords. Tweets or tweet segments with flavor keywords but not any known flavor names were marked as potential new flavor candidates, which were further verified by a web-based search. The longitudinal trends in the number of tweets mentioning e-cigarette brands and flavors were examined in both commercial and noncommercial tweets.ResultsThrough our developed methods, we identified 34 new e-cigarette brands and 97 new e-cigarette flavors from commercial tweets as well as 56 new e-cigarette brands and 164 new e-cigarette flavors from noncommercial tweets. The longitudinal trend of the e-cigarette brands showed that JUUL was the most popular e-cigarette brand mentioned on Twitter; however, there was a decreasing trend in the mention of JUUL over time on Twitter. Menthol flavor was the most popular e-cigarette flavor mentioned in the commercial tweets, whereas mango flavor was the most popular e-cigarette flavor mentioned in the noncommercial tweets during our study period.ConclusionsOur proposed methods can successfully identify new e-cigarette brands and flavors mentioned on Twitter. Twitter data can be used for monitoring the dynamic changes in the popularity of e-cigarette brands and flavors.

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Identification of Cigarette Brands by Soft Independent Modeling of Class Analogy of Volatile Substances.

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