The chemistry of taste: Researchers seek the chemical clues that make a tasty peanut
The chemistry of taste: Researchers seek the chemical clues that make a tasty peanut
Georgia grows more peanuts than any other state in the country. But for the breeders developing the next generation of varieties, some of the most consequential work isn’t happening in the field — it’s happening in the lab, where a team of University of Georgia scientists is working to take the guesswork out of roasted peanut flavor.
Meet the Experts
Joonhyuk Suh, Assistant Professor in the Department of Food Science and Technology
Nino Brown, Assistant Professor of Peanut Breeding
Abhinav Mishra, Director of Graduate Studies and Associate Professor in the Online Master of Food Technology program
Koushik Adhikari, Professor
The challenge is a familiar one in crop breeding: Flavor matters enormously to consumers, but it’s one of the last things breeders can afford to test.
Conventional sensory evaluation — the gold standard for measuring roasted flavor quality — either requires a trained panel of 8-10 people or a consumer panel of 100 people, both of which are time-, material- and cost-intensive tests. In a breeding program where promising lines are still being grown, a handful of seeds at a time, that’s simply not practical.
That’s where Joonhyuk Suh comes in. Suh, a food chemist and an assistant professor in the Department of Food Science and Technology, is leading a multidisciplinary team, including Brown, food microbiologist Abhinav Mishra, and sensory scientist Koushik Adhikari, to build a predictive model that can evaluate roasted peanut flavor using chemical data alone. If it works, the required sample size drops to a fraction of what sensory evaluation tests require — roughly one-tenth — and the analysis could be applied as early as seedling trials.
Leading the experimental work is Namhee Lee, a doctoral student in Suh’s lab, who is responsible for the experimental design, chemical analysis and data interpretation behind the project. She is also building initial prediction models from her experimental data, which feed into a broader machine learning comparison led by Mishra.
“Connecting the chemical changes during roasting to what the sensory panel perceives is what will make the prediction model work,” Lee said.
Mishra, whose background is in mathematical modeling and food safety, is building three parallel models: one using aroma compounds and precursors together, one using aroma compounds alone, and one using only sugars and amino acids. Comparing the performance of all three will reveal which chemical profile is the most reliable predictor of sensory quality.
“My role is to build machine learning models that look for correlations between the chemical data and the sensory results, essentially asking whether certain amino acids or other compounds are driving or diminishing the qualities that consumers perceive,” Mishra said.
The project, which began in October 2024, received initial support from UGA’s Institute for Integrative Precision Agriculture and the National Peanut Board. A $650,000 proposal to the U.S. Department of Agriculture National Institute of Food and Agriculture is pending. If funded, the team plans to expand testing from fewer than 10 cultivars to more than 100 and to identify the genetic markers tied to flavor-related compounds, moving beyond variety selection and toward DNA-level breeding decisions.
For now, the team is working toward a prediction model with high accuracy — a level Suh says is achievable and would open the door to using the tool with newly developed varieties across the UGA breeding pipeline and, eventually, across the USDA’s broader peanut variety database.
“Once we get a solid prediction model,” Suh said, “we plan to use it for new varieties being developed — not just UGA varieties, but peanut varieties from across the country.”
Learn more about the Online Master of Food Technology program.