Back to Search Start Over

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

Authors :
Krüsemann EJZ
Havermans A
Pennings JLA
de Graaf K
Boesveldt S
Talhout R
Source :
Tobacco control [Tob Control] 2021 Mar; Vol. 30 (2), pp. 185-191. Date of Electronic Publication: 2020 Feb 10.
Publication Year :
2021

Abstract

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.<br />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.<br />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%.<br />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.<br />Competing Interests: Competing interests: None declared.<br /> (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Details

Language :
English
ISSN :
1468-3318
Volume :
30
Issue :
2
Database :
MEDLINE
Journal :
Tobacco control
Publication Type :
Academic Journal
Accession number :
32041831
Full Text :
https://doi.org/10.1136/tobaccocontrol-2019-055447