Whisky is distilled to around 70% alcohol by volume (vol-%) then diluted to about 40 vol-%, and often drunk after further slight dilution to enhance its taste. The taste of whisky is primarily associated with amphipathic molecules, such as guaiacol, but why and how dilution enhances the taste is not well understood. We carried out computer simulations of water-ethanol mixtures in the presence of guaiacol, providing atomistic details on the structure of the liquid mixture. We found that guaiacol is preferentially associated with ethanol, and, therefore, primarily found at the liquid-air interface in mixtures that contain up to 45 vol-% of ethanol. At ethanol concentrations of 59 vol-% or higher, guaiacol is increasingly surrounded by ethanol molecules and is driven to the bulk. This indicates that the taste of guaiacol in the whisky would be enhanced upon dilution prior to bottling. Our findings may apply to other flavour-giving amphipathic molecules and could contribute to optimising the production of spirits for desired tastes. Furthermore, it sheds light on the molecular structure of water-alcohol mixtures that contain small solutes, and reveals that interactions with the water may be negligible already at 89 vol-% of ethanol.
Smells are known to be composed of thousands of chemicals with various concentrations, and thus, the extraction of specific information from such a complex system is still challenging. Herein, we report for the first time that the nanomechanical sensing combined with machine learning realizes the specific information extraction, e.g. alcohol content quantification as a proof-of-concept, from the smells of liquors. A newly developed nanomechanical sensor platform, a Membrane-type Surface stress Sensor (MSS), was utilized. Each MSS channel was coated with functional nanoparticles, covering diverse analytes. The smells of 35 liquid samples including water, teas, liquors, and water/EtOH mixtures were measured using the functionalized MSS array. We selected characteristic features from the measured responses and kernel ridge regression was used to predict the alcohol content of the samples, resulting in successful alcohol content quantification. Moreover, the present approach provided a guideline to improve the quantification accuracy; hydrophobic coating materials worked more effectively than hydrophilic ones. On the basis of the guideline, we experimentally demonstrated that additional materials, such as hydrophobic polymers, led to much better prediction accuracy. The applicability of this data-driven nanomechanical sensing is not limited to the alcohol content quantification but to various fields including food, security, environment, and medicine.
In consumer food-sensory studies, sorting and closely related methods (for example, projective mapping) have often been applied to large product sets which are complex and fatiguing for panelists. Analysis of sorting by Multi-Dimensional Scaling (MDS) is common, but this method discards relevant individual decisions; analysis by DISTATIS, which accounts for individual differences, is gaining acceptance. This research posits that replication can improve DISTATIS analysis by stabilizing consumer sensory maps, which are often extremely unstable. As a case study a fatiguing product set was sorted: 10 American whiskeys-5 bourbons and 5 ryes-were sorted into groups by 21 consumers over 2 replications. These products were chosen because American whiskeys are some of the most important distilled beverages in today’s market; in particular, “bourbon” (mashbill more than 50% corn) and “rye” (more than 50% rye) whiskeys are important and assumed to be products with distinct sensory attributes. However, there is almost no scientific information about their sensory properties. Data were analyzed using standard and aggregated DISTATIS and MDS. No significant relationship between mashbill and consumer categorization in whiskeys was found; instead, there was evidence of producer and aging effects. aggregated DISTATIS was found to provide more stable results than without replication, and DISTATIS results provided a number of benefits over MDS, including bootstrapped confidence intervals for product separation. In addition, this is the first published evidence that mashbill does not determine sensory properties of American whiskey: bourbons and ryes, while legally distinct, were not separated by consumers.
Over the last few decades, wine makers have been producing wines with a higher alcohol content, assuming that they are more appreciated by consumers. To test this hypothesis, we used functional magnetic imaging to compare reactions of human subjects to different types of wine, focusing on brain regions critical for flavor processing and food reward. Participants were presented with carefully matched pairs of high- and low-alcohol content red wines, without informing them of any of the wine attributes. Contrary to expectation, significantly greater activation was found for low-alcohol than for high-alcohol content wines in brain regions that are sensitive to taste intensity, including the insula as well as the cerebellum. Wines were closely matched for all physical attributes except for alcohol content, thus we interpret the preferential response to the low-alcohol content wines as arising from top-down modulation due to the low alcohol content wines inducing greater attentional exploration of aromas and flavours. The findings raise intriguing possibilities for objectively testing hypotheses regarding methods of producing a highly complex product such as wine.
Increases in glass sizes and wine strength over the last 25 years in the UK are likely to have led to an underestimation of alcohol intake in population studies. We explore whether this probable misclassification affects the association between average alcohol intake and risk of mortality from all causes, cardiovascular disease and cancer.
Although beer and liquor have been associated with risk of incident gout, wine has not. Yet anecdotally, wine is thought to trigger gout attacks. Further, how much alcohol intake is needed to increase the risk of gout attack is not known. We examined the quantity and type of alcohol consumed on risk of recurrent gout attacks.
Past studies of relationships between alcohol and hip fracture have generally focused on total alcohol consumed and not type of alcohol. Different types of alcohol consist of varying components which may affect risk of hip fracture differentially. This study seeks to examine the relationship between alcohol consumption, with a focus on type of alcohol consumed (e.g. beer, wine, or hard liquor) and hip fracture risk in post-menopausal women.
Successful discrimination of 14 representative liquors (including scotch, bourbon and rye whiskies, brandy, and vodka) was achieved using a 36-element colorimetric sensor array comprising multiple classes of cross-reactive, chemically responsive inks. In combination with a palm-sized image analyzer, the sensor array permits real-time identification of liquor products based on vapor analysis within 2 min. Changes in sensor spot colors before and after exposure to the vapors of the liquors that are partially oxidized as they are pumped over the sensor array provides a unique color difference pattern for each analyte. Facile identification of each liquor was demonstrated using several different multivariate analyses of the digital data library, including principal component, hierarchical cluster, and support vector machine analysis. The sensor array is also able to detect dilution (i.e., “watering”) of liquors even down to 1% addition of water. This colorimetric sensor array is a promising portable adjunct to other available techniques for quality assurance of liquors and other alcoholic beverages.
Increasingly, English local authorities have encouraged the implementation of an intervention called ‘Reducing the Strength’ (RtS) whereby off-licences voluntarily stop selling inexpensive ‘super-strength’ (≥6.5% alcohol by volume (ABV)) beers and ciders. We conceptualised RtS as an event within a complex system in order to identify pathways by which the intervention may lead to intended and unintended consequences.
A colorimetric sensor array has been designed for the identification of aldehydes and ketones in vapor phase. Due to rapid chemical reactions between the solid-state sensor elements and gaseous analytes, distinct color difference patterns were produced and digitally imaged for chemometric analysis. The sensor array was developed from classical spot tests using aniline and phenylhydrazine dyes that enable molecular recognition of a wide variety of aliphatic or aromatic aldehydes and ketones, as demonstrated by hierarchical cluster, principal component, and support vector machine analyses. The aldehyde/ketone-specific sensors were further employed for differentiation among ten liquor samples (whiskies, brandy, vodka) and ethanol controls, showing its potential applications in the beverage industry.