Can humans smell tastants?

Although studies have shown that olfaction may contribute to the perception of tastant, literature is scarce or circumstantial, especially in humans. This study aims to (i) explore whether humans can perceive solutions of basic prototypical tastants through orthonasal and retronasal olfaction and (ii) to examine what volatile odor compounds (VOCs) underlie this ability. Solutions of 5 basic tastants (sucrose, sodium chloride, citric acid, monosodium glutamate [MSG], quinine) dissolved in water, and 2 fatty acids (oleic and linoleic acid) dissolved in mineral oil were prepared. Triangle discrimination tests were performed (n = 41 in duplicate) to assess whether the tastant solutions can be distinguished from blanks (solvents) through ortho- and retronasal olfaction. Participants were able to distinguish all tastant solutions from blank through orthonasal olfaction. Only sucrose, sodium chloride, oleic acid, and linoleic acid were distinguished from blank by retronasal olfaction. Ethyl dichloroacetate, methylene chloride, and/or acetone were identified in the headspace of sucrose, MSG, and quinine solutions but not in the headspace of water, sodium chloride, and citric acid solutions. Fat oxidation compounds such as alcohols and aldehydes were detected in the headspace of the oleic and linoleic acid solutions but not the mineral oil. We conclude that prototypical tastant solutions can be discriminated from water and fatty acid solutions from mineral oil through orthonasal olfaction. Differences in the volatile headspace composition between blanks and tastant solutions may have facilitated the olfactory discrimination. These findings can have methodological implications for future studies assessing gustatory perception using these prototypical taste compounds.

An ingredient co-occurrence network gives insight into e-liquid flavor complexity.

INTRODUCTION: Part of the appeal of e-cigarettes lies in their available flavors. To achieve attractive flavors, e-liquids contain many different flavoring agents, which allow many flavoring combinations. To advance our knowledge of e-liquid flavors and compositions and to evaluate the effect of legislation, we determined whether there are ingredient combinations that are frequently used together. METHODS: We used e-cigarette ingredient data from the European Common Entry Gate system (EU-CEG) as available on 31 December 2022. RESULTS: In e-liquids, we found 214 ingredient pairs with a co-occurrence odds ratio greater than 10. Together, these consisted of 62 unique ingredients. Network analysis revealed that ingredients were grouped together based on their flavor and/or chemical structure. We identified two densely connected regions (clusters) in the network. One consisted of six ingredients with sweet-vanilla-creamy flavors. The second cluster consisted of 13 ingredients. While some of these have fruity flavors, others, such as alkyl carboxylic acids and dimethyl sulfide, are known to have unpleasant flavors. Additional data and literature analyses indicated that alkyl carboxylic acids can contribute to a creamy and sweet-fruity taste, whereas dimethyl sulfide can contribute to a more refined fruity taste. CONCLUSIONS: These results exemplify that the flavor of e-liquids is not just the sum of its parts. Big data analyses on product data can be used to detect such patterns, but expert knowledge and additional data are needed for further interpretation. Monitoring of e-liquid flavors as well as ingredients will remain important to regulate e-liquid product attractiveness.

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.