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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...

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Autores principales: Krüsemann, Erna J Z, Havermans, Anne, Pennings, Jeroen L A, de Graaf, Kees, Boesveldt, Sanne, Talhout, Reinskje
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2021
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.
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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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