Context matters: Neural processing of food-flavored e-cigarettes and the influence of smoking.

E-cigarettes are harmful, addictive, and popular. In e-cigarettes, nicotine is often paired with food-flavors. How this pairing of nicotine and food cues influences neural processing warrants investigation, as in smokers, both types of cues activate similar brain regions. Additionally, while most e-cigarettes are sweet, savory e-cigarettes are seemingly absent, although savory flavors are commonly liked in food. To understand how smoking status and type of flavor modulate reactions to food-flavored e-cigarettes, in comparison to actual food, neural and subjective responses to food odors were measured in a 2 (sweet vs. savory odor) x2 (food vs. e-cigarette context) x2 (smokers vs. non-smokers) design in 22 occasional/light smokers and 25 non-smokers. During fMRI scanning, participants were exposed to sweet and savory odors and pictures creating the two contexts. Liking and wanting were repeatedly measured on a 100-unit visual-analogue-scale. Results show that sweet e-cigarettes were liked (Δ = 14.2 ± 1.7) and wanted (Δ = 39.5 ± 3.1) more than savory e-cigarettes, and their cues activated the anterior cingulate more (cluster-level qFDR = 0.003). Further, we observed context-dependent variations in insula response to odors (cluster-level qFDR = 0.023, and = 0.030). Savory odors in an e-cigarette context were wanted less than the same odors in a food-context (Δ = 32.8 ± 3.1). Smokers and non-smokers reacted similarly to flavored product cues. Our results indicate that the principles of flavor preference in food cannot directly be applied to e-cigarettes and that it is challenging to design sweet and savory e-cigarettes to appeal to smokers only.

Reducing attractiveness of e-liquids: proposal for a restrictive list of tobacco-related flavourings.

OBJECTIVE: Electronic cigarettes are addictive and harmful, and flavour is a key factor determining their abuse liability. Both adult smokers and young non-smokers like sweet and fruity flavours in particular. In order to discourage e-cigarette use among youth, the Dutch government announced in 2020 to only allow tobacco flavours in e-liquids. We propose a restrictive list of flavourings that will only enable the production of e-liquids with a tobacco flavour. METHODS: We used e-liquid ingredient data notified via the European Common Entry Gate system before the government's announcement. First, we classified all e-liquids into flavour categories, and continued with the set of flavourings present in tobacco e-liquids. Five selection criteria related to prevalence of use, chemical composition, flavour description and health effects were defined to compile a restrictive list of tobacco flavourings. RESULTS: E-liquids marketed as having tobacco flavour contained 503 different flavourings, some with tobacco flavour, but also other (such as sweet) flavours. We excluded (1) 330 flavourings used in <0.5% of e-liquids, (2) 77 used less frequently in tobacco than in all e-liquids, (3) 13 plant extracts, (4) 60 that are sweet or not associated with a tobacco flavour and (5) 7 flavourings with hazardous properties. This resulted in a final list of 16 flavourings. CONCLUSIONS: Implementing this restrictive list will likely discourage e-cigarette use among youth, but could also make e-cigarettes less attractive as smoking cessation aid.

Sensory methods to evaluate perception of flavours in tobacco and other nicotine-containing products: a review.

OBJECTIVES: Sensory methods use human senses to evaluate product attributes. This review provides an overview of the types of sensory methods used to evaluate the perception of flavour in tobacco and other nicotine-containing (ToNic) products and to discuss how sensory data could inform flavoured ToNic product policy. DATA SOURCES: PubMed, Embase and Web of Science. STUDY SELECTION: All peer-reviewed studies evaluating ToNic products using a sensory method published before 23 May 2020. DATA EXTRACTION: Two independent coders completed title/abstract and full-text screening to choose articles for inclusion (Cohen's kappa=0.85, strong agreement). Each coder completed data extraction on half the articles, recording relevant information (eg, sensory methods used, results). The coders categorised sensory methods and generated overarching themes. DATA SYNTHESIS: Of 110 articles identified, we included 29 articles containing 35 studies that used sensory methods to investigate ToNic products. The sensory methods included analytic methods such as discrimination and descriptive tests and hedonic methods such as liking tests. Six themes emerged regarding how sensory methods can be used to understand consumer perception and liking of ToNic products and to inform ToNic product policy. CONCLUSIONS: The identified studies highlight that sensory data can inform ToNic product policy. Analytic and sensory hedonic ratings can be used to assess a ToNic product's ability to promote addiction in the user (ie, abuse liability). Lastly, hedonic ratings can provide information to assess potential use behaviours.

Comprehensive Dutch market data analysis shows that e-liquids with nicotine salts have both higher nicotine and flavour concentrations than those with free-base nicotine.

OBJECTIVES: Recent years have seen an increase in e-liquids containing nicotine salts. Nicotine salts are less harsh and bitter than free-base nicotine and therefore can facilitate inhalation. Because inhalation-facilitating ingredients are banned in the European Union, we assessed the occurrence and characteristics of nicotine salt-containing e-liquids notified for the Netherlands. METHODS: We analysed data for 39 030 products, submitted by manufacturers in the European Union Common Entry Gate system, as extracted on 30 June 2020. RESULTS: Nicotine salts were present in 13% of e-liquids, especially in pod-related e-liquids (73%) and e-liquids registered from 2018 onwards (over 25%). We found six nicotine salt ingredients (NSIs): nicotine lactate, salicylate, benzoate, levulinate, ditartrate and malate. Nicotine salts also occurred as nicotine-organic acid ingredient combination (NAIC), like nicotine and benzoic acid. Nicotine concentrations were twofold higher in e-liquids with NSI (median 14 mg/mL) and NAIC (11 mg/mL) than for free-base nicotine (6 mg/mL). E-liquids with NSI contained a fourfold higher number (median n=17) and concentration (median 31.0 mg/mL) of flavour ingredients than e-liquids with free-base nicotine (n=4, 7.4 mg/mL). In NAIC-containing e-liquids, these were threefold higher (n=12, 21.5 mg/mL). E-liquids with nicotine salts were less often tobacco flavoured but more often had fruity or sweet flavours. CONCLUSIONS: A substantial and increasing share of e-liquids in the Netherlands contains nicotine salts. Their characteristics can make such e-liquids more addictive and more attractive, especially to young and beginning users. Policymakers are advised to consider regulating products containing nicotine salts.

Improving the Analysis of E-Cigarette Emissions: Detecting Human "Dry Puff" Conditions in a Laboratory as Validated by a Panel of Experienced Vapers.

INTRODUCTION: E-cigarette product regulation requires accurate analyses of emissions. User behavior, including device power setting selection, should be mimicked closely when generating e-cigarette emissions in a laboratory. Excessively high power settings result in an adverse burnt off-taste, called "dry puff flavor". This should be avoided because it results in an overestimation of toxicant levels (especially certain carbonyls). This study presents a human volunteer-validated approach to detect excessively high e-cigarette power settings by HPLC-DAD (high-performance liquid chromatography-diode array detection) carbonyl analysis. METHODS: Thirteen experienced e-cigarette users evaluated whether the "dry puff flavor" was present at different power settings (10 W-25 W), recording their assessment on a 100-unit visual analog scale (VAS). They assessed e-cigarettes equipped with 1.2 Ω or 1.6 Ω coils containing menthol, vanilla or fruit-flavored e-liquids. In a machine-vaping experiment, emissions from the same liquid/coil/power setting combinations were subjected to HPLC-DAD analysis of dinitrophenol hydrazine (DNPH)-derivatized carbonyls, such as lactaldehyde and formaldehyde. A simple algorithm, based on the cutoff values for each marker, was applied to relate the dry puff flavor (as assessed by the human volunteers) to the laboratory measurements. RESULTS: Eleven carbonyl compounds were found to agree with the human assessments. Based on the amounts of these compounds in the emissions, the dry-puff flavor did match at all combinations of e-liquids and coils examined. Dry-puff flavor was observed at different power levels with the different liquids tested. CONCLUSIONS: The described method can detect dry puff conditions and is therefore a useful tool to ensure user-relevant conditions in laboratory analyses of e-cigarette emissions. IMPLICATIONS: This study improves the chemical analysis of e-cigarette emissions. It offers a method to select an appropriate (i.e., user-relevant) power setting for e-cigarettes, which is a critical parameter for emission analysis and therefore important for regulatory purposes and risk assessments. Compared to the approach of using human volunteers to select appropriate power settings for different products by taste, the described method is cheaper, faster, more practical and more ethical.

Both Nonsmoking Youth and Smoking Adults Like Sweet and Minty E-liquid Flavors More Than Tobacco Flavor.

Smokers may reduce their health risk by switching to electronic cigarette (e-cigarette) use. As e-cigarettes are not harmless, concerns exist about e-cigarette use by nonsmokers and youth. E-liquids are available in many different flavors that increase sensory appeal. Flavor preferences may differ between user groups, which could open doors for product regulation. We investigated which e-liquid flavors are attractive to specific user groups by comparing liking between adolescent nonsmokers (n = 41; mean age 16.9 ± 0.8), young adult nonsmokers (n = 42; mean age 22.7 ± 1.7), and adult smokers (n = 56; mean age 39.7 ± 11.1). Participants smelled tobacco- (n = 6) and nontobacco (n = 24)-flavored e-liquids and rated liking on a 9-point labeled hedonic scale, and familiarity, overall intensity, perceived sweetness, perceived bitterness, and irritation of the odors on a 100-unit Visual Analog Scale. Mean liking ranged from 2.3 (whiskey) to 6.7 (peppermint). Within all groups, the typically sweet and minty flavors (e.g., wine gum, watermelon, peppermint, menthol) were liked significantly more than the tobacco-flavored e-liquids. The set of tobacco-flavored e-liquids was significantly, but slightly, less disliked by adult smokers (3.9 ± 0.2) than adolescent (3.1 ± 0.3) and young adult (3.4 ± 0.3) nonsmokers (P < 0.001). No between-group differences were observed for sweet and minty flavors. Liking correlated significantly positively with odor sweetness (R = 0.49) and familiarity (R = 0.48) and negatively with odor bitterness (R = -0.58), irritation (R = -0.47), and overall intensity (R = -0.27). Thus, sweet- and minty-flavored e-liquids are liked equally by young nonsmokers and adult smokers, and more than tobacco flavors. Banning all flavors except tobacco will likely reduce e-cigarette appeal; potentially more for young nonsmokers than adult smokers.

Nearly 20 000 e-liquids and 250 unique flavour descriptions: an overview of the Dutch market based on information from manufacturers.

OBJECTIVES: Flavours increase attractiveness of electronic cigarettes and stimulate use among vulnerable groups such as non-smoking adolescents. It is important for regulators to monitor the market to gain insight in, and regulate the range of e-liquid flavours that is available to consumers. E-liquid manufacturers are required to report key product information to authorities in the European Member States in which they plan to market their products. This information was used to provide an overview of e-liquid flavour descriptions marketed in the Netherlands in 2017. METHODS: Two researchers classified 19 266 e-liquids into the 16 main categories of the e-liquid flavour wheel, based on information from four variables in the European Common Entry Gate system. Flavour descriptions were further specified in subcategories. RESULTS: For 16 300 e-liquids (85%), sufficient information was available for classification. The categories containing the highest number of e-liquids were fruit (34%), tobacco (16%) and dessert (10%). For all e-liquids, excluding unflavoured ones, 245 subcategories were defined within the main categories. In addition to previously reported subcategories, various miscellaneous flavours such as sandwich, buttermilk and lavender were identified. CONCLUSIONS: In 2017, ~20 000 e-liquids were reported to be marketed in the Netherlands, in 245 unique flavour descriptions. The variety of marketed flavour descriptions reflects flavour preference of e-cigarette users as described in literature. Our systematic classification of e-liquids by flavour description provides a tool for organising the huge variety in market supply, serves as an example for other countries to generate similar overviews and can support regulators in developing flavour regulations.

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-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.

GC-MS analysis of e-cigarette refill solutions: A comparison of flavoring composition between flavor categories.

OBJECTIVES: Electronic cigarette refill solutions (e-liquids) are available in various flavor descriptions that can be categorized as fruit, tobacco, and more. Flavors increase sensory appeal, thereby stimulating e-cigarette use, and flavoring ingredients can contribute to e-cigarette toxicity. We aim to inform toxicologists, sensory scientists, and regulators by determining flavoring compounds in e-liquids with various flavors, and compare results between flavor categories. METHODS: Gas chromatography - mass spectrometry (GC-MS) was used to identify 79 flavorings in 320 e-liquids, classified in 15 flavor categories. Ten flavorings highly prevalent in e-liquids according to information from manufacturers were quantified. Flavoring prevalence was defined as the number of e-liquids with the flavoring as percentage of the total number of e-liquids. The method was validated in terms of specificity, linearity, repeatability, recovery, and sensitivity. RESULTS: The mean number of flavorings per e-liquid was 6 ± 4. Flavoring prevalence was highest for vanillin (creamy/vanilla flavor), ethyl butyrate (ethereal/fruity), and cis-3-hexenol (fresh/green). Based on similarities in flavoring prevalence, four clusters of categories were distinguished: (1) fruit, candy, alcohol, beverages; (2) dessert, coffee/tea, nuts, sweets; (3) menthol/mint; and (4) spices, tobacco, and unflavored. Categories from cluster 4 generally had less flavorings per e-liquid than fruit, candy, alcohol, beverages (cluster 1) and dessert (cluster 2) (p < 0.05). Flavoring concentrations varied between e-liquids within the categories. CONCLUSIONS: We evaluated flavoring compositions of 320 e-liquids using a simple GC-MS method. Flavoring prevalence was similar within four clusters of typically fresh/sweet, warm/sweet, fresh/cooling, and non-sweet flavor categories. To compare flavoring concentrations between individual flavor categories, additional research is needed.

Sensory Evaluation of E-Liquid Flavors by Smelling and Vaping Yields Similar Results.

INTRODUCTION: Sensory research on e-liquid flavors can be performed by means of smelling and vaping. However, data comparing smelling versus vaping e-liquid flavors are lacking. This study aims to investigate if smelling could be an alternative to vaping experiments by determining the correlation for hedonic flavor assessment between orthonasal smelling and vaping of e-liquids, for smokers and nonsmokers. METHODS: Twenty-four young adult smokers (mean age 24.8 ± 9.3) and 24 nonsmokers (mean age 24.9 ± 7.7) smelled and vaped 25 e-liquids in various flavors. Participants rated liking, intensity, familiarity, and irritation on a 100-mm Visual Analog Scale. Pearson correlations within and between smelling and vaping were calculated. Differences between user groups were calculated using t tests. RESULTS: Correlation coefficients between smelling and vaping based on mean group ratings were 0.84 for liking, 0.82 for intensity, 0.84 for familiarity, and 0.73 for irritation. Means of the within-subjects correlation coefficients were, respectively, 0.51, 0.37, 0.47, and 0.25. Correlations between smelling and vaping varied across individuals (ranging from -0.27 to 0.87) and flavors (-0.33 to 0.81). Correlations and mean liking ratings did not differ between smokers and nonsmokers. CONCLUSIONS: The strong group-level correlations between orthonasal smelling and vaping e-liquid flavors justify the use of smelling instead of vaping in future research. For example, smelling could be used to investigate differences in e-liquid flavor liking between (potential) user groups such as nicotine-naïve adolescents. The more modest within-subject correlations and variation across individuals and flavors merit caution in using smelling instead of vaping in other types of experiments. IMPLICATIONS: This study supports the use of orthonasal smelling (instead of vaping) e-liquids to measure hedonic flavor perception in some studies where vaping would be inappropriate or not feasible. Examples of research situations where smelling e-liquids may be sufficient are (1) investigating nicotine-naïve individuals (ie, nonusers), (2) investigating individuals under legal age for e-cigarette use (ie, youth and adolescents), (3) investigating brain responses to exposure of e-liquid flavors using functional magnetic resonance imaging or electroencephalogram, and (4) comparing hedonic flavor assessment between adolescent nonusers and current smokers to provide support for future regulations on e-liquid flavors.

An E-Liquid Flavor Wheel: A Shared Vocabulary Based on Systematically Reviewing E-Liquid Flavor Classifications in Literature.

INTRODUCTION: E-liquids are available in a high variety of flavors. A systematic classification of e-liquid flavors is necessary to increase comparability of research results. In the food, alcohol, and fragrance industry, flavors are classified using flavor wheels. We systematically reviewed literature on flavors related to electronic cigarette use, to investigate how e-liquid flavors have been classified in research, and propose an e-liquid flavor wheel to classify e-liquids based on marketing descriptions. METHODS: The search was conducted in May 2017 using PubMed and Embase databases. Keywords included terms associated with electronic cigarette, flavors, liking, learning, and wanting in articles. Results were independently screened and reviewed. Flavor categories used in the articles reviewed were extracted. RESULTS: Searches yielded 386 unique articles of which 28 were included. Forty-three main flavor categories were reported in these articles (eg, tobacco, menthol, mint, fruit, bakery/dessert, alcohol, nuts, spice, candy, coffee/tea, beverages, chocolate, sweet flavors, vanilla, and unflavored). Flavor classifications of e-liquids in literature showed similarities and differences across studies. Our proposed e-liquid flavor wheel contains 13 main categories and 90 subcategories, which summarize flavor categories from literature to find a shared vocabulary. For classification of e-liquids using our flavor wheel, marketing descriptions should be used. CONCLUSIONS: We have proposed a flavor wheel for classification of e-liquids. Further research is needed to test the flavor wheels' empirical value. Consistently classifying e-liquid flavors using our flavor wheel in research (eg, experimental, marketing, or qualitative studies) minimizes interpretation differences and increases comparability of results. IMPLICATIONS: We reviewed e-liquid flavors and flavor categories used in research. A large variation in the naming of flavor categories was found and e-liquid flavors were not consistently classified. We developed an e-liquid flavor wheel and provided a guideline for systematic classification of e-liquids based on marketing descriptions. Our flavor wheel summarizes e-liquid flavors and categories used in literature in order to create a shared vocabulary. Applying our flavor wheel in research on e-liquids will improve data interpretation, increase comparability across studies, and support policy makers in developing rules for regulation of e-liquid flavors.

Sensory analysis of characterising flavours: evaluating tobacco product odours using an expert panel.

OBJECTIVES: Tobacco flavours are an important regulatory concept in several jurisdictions, for example in the USA, Canada and Europe. The European Tobacco Products Directive 2014/40/EU prohibits cigarettes and roll-your-own tobacco having a characterising flavour. This directive defines characterising flavour as 'a clearly noticeable smell or taste other than one of tobacco […]'. To distinguish between products with and without a characterising flavour, we trained an expert panel to identify characterising flavours by smelling. METHODS: An expert panel (n=18) evaluated the smell of 20 tobacco products using self-defined odour attributes, following Quantitative Descriptive Analysis. The panel was trained during 14 attribute training, consensus training and performance monitoring sessions. Products were assessed during six test sessions. Principal component analysis, hierarchical clustering (four and six clusters) and Hotelling's T-tests (95% and 99% CIs) were used to determine differences and similarities between tobacco products based on odour attributes. RESULTS: The final attribute list contained 13 odour descriptors. Panel performance was sufficient after 14 training sessions. Products marketed as unflavoured that formed a cluster were considered reference products. A four-cluster method distinguished cherry-flavoured, vanilla-flavoured and menthol-flavoured products from reference products. Six clusters subdivided reference products into tobacco leaves, roll-your-own and commercial products. CONCLUSIONS: An expert panel was successfully trained to assess characterising odours in cigarettes and roll-your-own tobacco. This method could be applied to other product types such as e-cigarettes. Regulatory decisions on the choice of reference products and significance level are needed which directly influences the products being assessed as having a characterising odour.

The Sensory Difference Threshold of Menthol Odor in Flavored Tobacco Determined by Combining Sensory and Chemical Analysis.

Cigarettes are an often-used consumer product, and flavor is an important determinant of their product appeal. Cigarettes with strong nontobacco flavors are popular among young people, and may facilitate smoking initiation. Discriminating flavors in tobacco is important for regulation purposes, for instance to set upper limits to the levels of important flavor additives. We provide a simple and fast method to determine the human odor difference threshold for flavor additives in a tobacco matrix, using a combination of chemical and sensory analysis. For an example, the human difference threshold for menthol odor, one of the most frequently used tobacco flavors, was determined. A consumer panel consisting of 20 women compared different concentrations of menthol-flavored tobacco to unflavored cigarette tobacco using the 2-alternative forced choice method. Components contributing to menthol odor were quantified using headspace GC-MS. The sensory difference threshold of menthol odor corresponded to a mixture of 43 (37-50)% menthol-flavored tobacco, containing 1.8 (1.6-2.1) mg menthol, 2.7 (2.3-3.1) µg menthone, and 1.0 (0.9-1.2) µg neomenthyl acetate per gram of tobacco. Such a method is important in the context of the European Tobacco Product Directive, and the US Food and Drug Administration Tobacco Control Act, that both prohibit cigarettes and roll-your-own tobacco with a characterizing flavor other than tobacco. Our method can also be adapted for matrices other than tobacco, such as food.