Stirling Distillery has two years to figure it out. Breakthroughs, discoveries, and DIY tips sent six days a week. Scotland's smallest whisky distillery also hopes to be one of the most innovative in time for its first batch's debut. But with only around two years until Sterling Distillery's inaugural liquor matures, it remains to be seen if the company can ditch traditional glass bottles for a material associated more with cheap beer than fine whisky--aluminum. Any serious distillery uses glass bottles for the good stuff.

Microwave Vacuum Drying, or MVD, may be a real MVP for snack foods. Breakthroughs, discoveries, and DIY tips sent every weekday. It's hard to stop after eating a single potato chip --and that's kind of their whole problem. The deep-fried, popular salty snack is loaded with unhealthy fats, oils, and other unwanted ingredients that are linked with numerous health problems. Unfortunately, those are also the flavor profiles humans are evolutionarily wired to crave.

Flavor development in the food industry is increasingly challenged by the need for rapid innovation and precise flavor profile creation. Traditional flavor research methods typically rely on iterative, subjective testing, which lacks the efficiency and scalability required for modern demands. This paper presents three contributions to address the challenges. Firstly, we define a new problem domain for scientific agents in flavor science, conceptualized as the generation of hypotheses for flavor profile sourcing and understanding. To facilitate research in this area, we introduce the FoodPuzzle, a challenging benchmark consisting of 978 food items and 1,766 flavor molecules profiles. We propose a novel Scientific Agent approach, integrating in-context learning and retrieval augmented techniques to generate grounded hypotheses in the domain of food science. Experimental results indicate that our model significantly surpasses traditional methods in flavor profile prediction tasks, demonstrating its potential to transform flavor development practices.

Ewan Morgan, National Luxury Ambassador and Head of Whisky Outreach at Diageo North America, told Fox News Digital how SmokeDNAi technology is being used to understand the aging process of whisky. For two years, Diageo analyzed various Scotch whiskies using AI and algorithms. Diageo, an alcohol beverage company, invested 230 million into a portfolio of whisky tourism projects. Of this lump sum, more than 44 million was dedicated to the exploration of whisky maturation using technology called SmokeDNAi. Using SmokeDNAi, teams tested and analyzed the flavor profiles and mouthfeel of non-identical twin whiskies distilled in different casks – one remnant and one original.

Coffee cups are pictured in the tasting area of the Vanibel cocoa and vanilla production facility, a ... [ ] former 18th Century sugar refinery in Vieux-Habitants, Guadeloupe, on April 9, 2018. Artificial intelligence (AI) is predicted to reach $126 billion by 2025. It is showing up in every industry, from healthcare and agriculture to education, finance and shipping. And now, AI has made a move to the food industry to discover and develop new flavors in food and drink. In 2018, Danish brewer, Carlsburg used AI to map and predict flavors from yeast and other ingredients in beer.

Coffee cups are pictured in the tasting area of the Vanibel cocoa and vanilla production facility, a ... [ ] former 18th Century sugar refinery in Vieux-Habitants, Guadeloupe, on April 9, 2018. Artificial intelligence (AI) is predicted to reach $126 B by 2025. It is showing up in every industry, from healthcare and agriculture to education, finance and shipping. And now, AI has made a move to the food industry to discover and develop new flavors in food and drink. In 2018, Danish brewer, Carlsburg used AI to map and predict flavors from yeast and other ingredients in beer.

According to a Nielsen report, brick-and-mortar alcohol dollar sales were up 21% in April 2020 compared to the same period a year ago. Online alcohol sales skyrocketed by 234% over the same period in 2019. However, despite the increase, global sales are decreasing due to the shutdowns in restaurants, bars, live events and travel. Next Century Spirits is a liquor technology startup with $9.6 M in funding. The company uses big data and machine learning to create and filter bespoke distilled spirits.

Digital disruption is affecting nearly every industry, from financial services to healthcare -- and the food and beverage sector is no exception. Historically, flavor profiles, trends and new food products have largely been attributed to chefs and product developers, and it would take months or years before an idea could be translated into a product and introduced to the market. In more recent years, however, the answer to the next big food or flavor trend has had less to do with humans and more with the power of big data and artificial intelligence (AI), which learns and mimics human behavior by collecting and analyzing millions of data sets concurrently. So how does harnessing technology translate into the next flavor or trend? As an example, spice company McCormick partnered with IBM in 2019 to leverage AI to predict new flavor combinations.

Wine has incredible diversity; there exist over 10,000 different varieties of wine grapes worldwide, and each can be processed in a hundred thousand unique ways. Sommeliers-- those who dedicate their lives to the art of wine tasting-- work to craft flavor profiles for the wines they are given to analyze, using their extensive experience to provide nuanced evaluations of countless bottles of wine every year. But the majority of people have neither the time nor the money to try a variety of wines and develop their palate. Typically, the only claim one can make about a given glass of wine is whether or not it was enjoyable, and without the ability to identify ones taste preferences in wine, it is incredibly difficult for one to discover new wine, and nearly impossible to find wine that directly matches their individual flavor profile. We hope to develop an algorithm to address both of these issues, becoming a personal sommelier for the user. Our algorithm takes a history of the wine a user has tasted as input, and returns a set of optimal wines for the user to try next, as well as a description of the flavor profile that inspired the recommendations. Thus, the algorithm could become an avenue for the user to confidently explore wine, and understand more quickly what they do and do not like in wine. Formally, we define our problem as an unsupervised learning problem.