PT  - JOURNAL ARTICLE
AU  - Colantonio, Vincent
AU  - Ferrão, Luis Felipe V.
AU  - Tieman, Denise
AU  - Bliznyuk, Nikolay
AU  - Sims, Charles
AU  - Klee, Harry
AU  - Munoz, Patricio R.
AU  - Resende, Marcio F. R.
TI  - Metabolomic Selection for Enhanced Fruit Flavor
AID  - 10.1101/2020.09.17.302802
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.09.17.302802
4099  - http://biorxiv.org/content/early/2020/09/18/2020.09.17.302802.short
4100  - http://biorxiv.org/content/early/2020/09/18/2020.09.17.302802.full
AB  - Although they are staple foods in cuisines globally, commercial fruit varieties have become progressively less flavorful over time. Due to the cost and difficulty associated with flavor phenotyping, many breeding programs have long been challenged in selecting for this complex trait. To address this issue, we leveraged targeted metabolomics of diverse tomato and blueberry accessions and their corresponding consumer panel ratings to create statistical and machine learning models that can predict sensory perceptions of fruit flavor. Using these models, a breeding program can assess flavor ratings for a large number of varieties, previously limited by the low-throughput and high cost of consumer sensory panels. The ability to predict consumer ratings of liking, sweet, sour, umami, and flavor intensity was evaluated by a 10-fold cross-validation and the accuracies of 18 different models are assessed. The best performing models were used to infer the flavor compounds (sugars, acids, and volatiles) that contribute most to each flavor attribute. The prediction accuracies were high for most attributes in both blueberries and tomatoes. We expect that these models will enable an earlier incorporation of flavor as breeding targets and encourage selection and release of more flavorful fruit varieties.Competing Interest StatementThe authors have declared no competing interest.