Comprehensive overview of common e-liquid ingredients and how they can be used to predict an e-liquid's flavour category

被引:61
作者
Krusemann, Erna J. Z. [1 ,2 ]
Havermans, Anne [1 ]
Pennings, Jeroen L. A. [1 ]
de Graaf, Kees [2 ]
Boesveldt, Sanne [2 ]
Talhout, Reinskje [1 ]
机构
[1] Rijksinst Volksgezondheid Milieu, Ctr Hlth Protect, NL-3721 MA Bilthoven, Netherlands
[2] Wageningen Univ, Div Human Nutr & Hlth, Wageningen, Netherlands
关键词
electronic nicotine delivery devices; public policy; surveillance and monitoring; tobacco industry; ELECTRONIC CIGARETTES; CHEMICAL-COMPOSITION; TOBACCO PRODUCT; EXPOSURE;
D O I
10.1136/tobaccocontrol-2019-055447
中图分类号
R194 [卫生标准、卫生检查、医药管理];
学科分类号
摘要
Objectives Flavours increase e-cigarette attractiveness and use and thereby exposure to potentially toxic ingredients. An overview of e-liquid ingredients is needed to select target ingredients for chemical analytical and toxicological research and for regulatory approaches aimed at reducing e-cigarette attractiveness. Using information from e-cigarette manufacturers, we aim to identify the flavouring ingredients most frequently added to e-liquids on the Dutch market. Additionally, we used flavouring compositions to automatically classify e-liquids into flavour categories, thereby generating an overview that can facilitate market surveillance. Methods We used a dataset containing 16 839 e-liquids that were manually classified into 16 flavour categories in our previous study. For the overall set and each flavour category, we identified flavourings present in more than 10% of the products and their median quantities. Next, quantitative and qualitative ingredient information was used to predict e-liquid flavour categories using a random forest algorithm. Results We identified 219 unique ingredients that were added to more than 100 e-liquids, of which 213 were flavourings. The mean number of flavourings per e-liquid was 10 +/- 15. The most frequently used flavourings were vanillin (present in 35% of all liquids), ethyl maltol (32%) and ethyl butyrate (28%). In addition, we identified 29 category-specific flavourings. Moreover, e-liquids' flavour categories were predicted with an overall accuracy of 70%. Conclusions Information from manufacturers can be used to identify frequently used and category-specific flavourings. Qualitative and quantitative ingredient information can be used to successfully predict an e-liquid's flavour category, serving as an example for regulators that have similar datasets available.
引用
收藏
页码:185 / 191
页数:7
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