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Comprehensive overview of common e-liquid ingredients and how they can be used to predict an e-liquid’s flavour category
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-ciga...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7907577/ https://www.ncbi.nlm.nih.gov/pubmed/32041831 http://dx.doi.org/10.1136/tobaccocontrol-2019-055447 |
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author | Krüsemann, Erna J Z Havermans, Anne Pennings, Jeroen L A de Graaf, Kees Boesveldt, Sanne Talhout, Reinskje |
author_facet | Krüsemann, Erna J Z Havermans, Anne Pennings, Jeroen L A de Graaf, Kees Boesveldt, Sanne Talhout, Reinskje |
author_sort | Krüsemann, Erna J Z |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7907577 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-79075772021-03-11 Comprehensive overview of common e-liquid ingredients and how they can be used to predict an e-liquid’s flavour category Krüsemann, Erna J Z Havermans, Anne Pennings, Jeroen L A de Graaf, Kees Boesveldt, Sanne Talhout, Reinskje Tob Control Original Research 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. BMJ Publishing Group 2021-03 2020-02-10 /pmc/articles/PMC7907577/ /pubmed/32041831 http://dx.doi.org/10.1136/tobaccocontrol-2019-055447 Text en © 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. http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Original Research Krüsemann, Erna J Z Havermans, Anne Pennings, Jeroen L A de Graaf, Kees Boesveldt, Sanne Talhout, Reinskje Comprehensive overview of common e-liquid ingredients and how they can be used to predict an e-liquid’s flavour category |
title | Comprehensive overview of common e-liquid ingredients and how they can be used to predict an e-liquid’s flavour category |
title_full | Comprehensive overview of common e-liquid ingredients and how they can be used to predict an e-liquid’s flavour category |
title_fullStr | Comprehensive overview of common e-liquid ingredients and how they can be used to predict an e-liquid’s flavour category |
title_full_unstemmed | Comprehensive overview of common e-liquid ingredients and how they can be used to predict an e-liquid’s flavour category |
title_short | Comprehensive overview of common e-liquid ingredients and how they can be used to predict an e-liquid’s flavour category |
title_sort | comprehensive overview of common e-liquid ingredients and how they can be used to predict an e-liquid’s flavour category |
topic | Original Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7907577/ https://www.ncbi.nlm.nih.gov/pubmed/32041831 http://dx.doi.org/10.1136/tobaccocontrol-2019-055447 |
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