Metabolomic selection for enhanced fruit flavor.

Abstract

Although they are staple foods in cuisines globally, many commercial fruit varieties have become progressively less flavorful over time. Due to the cost and difficulty associated with flavor phenotyping, 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 genotypes, previously limited by the low throughput 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 were assessed. The prediction accuracies were high for most attributes and ranged from 0.87 for sourness intensity in blueberry using XGBoost to 0.46 for overall liking in tomato using linear regression. Further, the best-performing models were used to infer the flavor compounds (sugars, acids, and volatiles) that contribute most to each flavor attribute. We found that the variance decomposition of overall liking score estimates that 42% and 56% of the variance was explained by volatile organic compounds in tomato and blueberry, respectively. 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.

Plant phenotyping relevance

果実の風味という植物器官形質を、メタボロームから予測する統計・機械学習モデルを開発し、交差検証で性能評価している。育種に利用可能な風味表現型推定法が中心であり、単なる代謝測定ではない。

Code and data availability

The paper provides public author analysis scripts on GitHub and paper-specific phenotype data (sensory panel ratings, metabolite concentrations, model accuracies) in Datasets S1–S7 within the PNAS supporting information. The caret R package is a generic library and excluded.