Looking for that perfect recipe? Or a new flavor combination that delights the senses? Increasingly, players in the food industry are embracing artificial intelligence (AI) to better understand the dynamics of flavor, aroma and other factors that go into making a food product a success.
Earlier this year, IBM became a surprise entrant to the food sector, announcing a partnership with seasonings maker McCormick to "explore flavor territories more quickly and efficiently using AI to learn and predict new flavor combinations" by collected data from millions of data points.
The partnership highlights how technology is being used to disrupt the food industry by helping develop new products and respond to consumer preferences and offer improved nutrition and flavor. "More and more, food companies are embracing digitization and becoming data-driven," said Bernard Lahousse, co-founder of Foodpairing, a startup with offices in Belgium and New York which develops digital food "maps" and algorithms to recommend food and drink combinations. Lahousse said his company has "the largest flavor database in the world" that enables better food predictions based on both human preference and data analysis. "Instead of using an expert panel or consumer panel we develop algorithms that can translate into how consumers view this product," he said.
New York-based Analytical Flavor Systems uses AI to create a model or "gastrograph" of flavor, aroma and texture to predict consumer preferences of food and beverage products. The platform, which recently raised $4 million in funding, aims to help companies "create better, more targeted and healthy products for consumers," according to founder Jason Cohen.
It's not clear how much funding is going into AI food ventures, although overall food tech investment amounted to $16.9 billion in 2018, according to data from the investment platform AgTech Funder.
Brita Rosenheim, a food tech analyst and investor in Analytical Flavor Systems through the firm Better Food Ventures, said technology can help "digitize existing data" from human taste panels and speed up the process for developing new food products. "The typical food product development process is long, and there are a lot of holes where there is no clear feedback on how the market is reaction, so this kind of technology can help," Rosenheim said.
Foodpairing, for example, offers its "flavor intelligence" map based on molecular analysis: A Spanish dry-cured ham, for example, has elements described as "cheesy" or acidic while beetroot has a "woody" and "caramellic" flavor profile.
Basil from MIT
Researchers at the Massachusetts Institute of Technology (MIT) illustrated how AI can be useful in determining optimal growing conditions by growing basil with supercharged flavor, and hope to adapt that for other products.
"AI could give us the ability to utilize vast datasets of detailed agricultural information for the improvement of our food crops, faster than e
ver before," said John de la Parra, head of MIT's Open Agriculture Initiative. Better data collection and standards are essential to enabling innovations in food, said Matthew Lange, a lecturer at the University of California at Davis and head of the IC3 Foods research center on food informatics working on data standards for food properties. "I see a lot of people applying machine learning around flavor and nutrition in recipe development," Lange said.