University geneticist working with robot-tasting in berry research ... kind of

Can a computer taste a blueberry?

Well, not exactly, but it can tell scientists which volatiles in these fruits make them taste good, according to a University of Florida news release.

Marcio Resende, breeder and geneticist for the university's Institute of Food and Agricultural Sciences, wants to create what he calls an “artificial intelligence connoisseur,”  a model that tells researchers which volatiles, sugars, acids and other chemical compounds produce the best fruit flavors.

To find out if a fruit or vegetable is worth breeding, scientists sample the crop for taste and smell, going through fields and picking produce individually.

These processes can present logistical issues, said Harry Klee, a UF/IFAS horticultural sciences professor said. Klee is also co-author of a new study that looks at how computer models can use volatiles to measure fruit taste.

“Due to cost and logistical limitations, breeders do not typically employ consumer panels in their programs,” Klee said. “The ideal would be to use a large consumer panel that includes a diverse set of potential consumers. We use 100 people, spanning a range of age and ethnicity. This approach is much more representative of the population of shoppers.”

For years, plant breeders and geneticists helped farmers harvest higher yields because consumer-oriented traits such as flavor are harder to measure. However, high yields are not enough for producers to compete in demanding markets, Patricio Muñoz, a UF/IFAS horticultural sciences associate professor, said in the release. Muñoz is in charge of the blueberry breeding program.

“Growers know that if they do not include varieties that taste good, then their fruit might not sell for a good price or sell at all,” Muñoz said. “With these methods, scientists hope to help producers stay competitive and consumers have a better experience with their produce.”

Resende led the new research that shows ways to get data from volatiles in blueberries and tomatoes into a statistical model. The research findings are now limited to those two fruits but will later be expanded to other crops.

Researchers showed that machine-learning approaches are generally the best predictors of consumer flavor preferences, called metabolomic selection. Accuracies of metabolic selection are superior to models that use genomic data instead, highlighting the potential of this new method in breeding applications, the release said.

“I think the main point is that breeders can screen a larger number of samples,” said Resende. “This way, you have a wider funnel to identify the good-tasting varieties, and at one point, taste-testing panels make a final selection with the sensory data. We expect that these models will enable an earlier incorporation of flavor as a breeding target and encourage selection and release of more flavorful fruit varieties.”