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Metabolomic selection for enhanced fruit flavor
Author(s) -
Vincent Colantonio,
Luís Felipe Ventorim Ferrão,
Denise M. Tieman,
Nikolay Bliznyuk,
Charles A. Sims,
Harry J. Klee,
Patricio Muñoz,
M. D. V. de Resende
Publication year - 2022
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2115865119
Subject(s) - flavor , umami , trait , food science , selection (genetic algorithm) , metabolomics , biology , microbiology and biotechnology , computer science , machine learning , bioinformatics , programming language
Significance Consumers often regard heirloom fruit varieties grown in the garden as more flavorful than commercial varieties purchased at the grocery store. While plant breeders have historically focused on improving producer-orientated traits such as yield, consumer-oriented traits such as flavor have regularly been neglected. This is, in part, due to the difficulty associated with measuring the sensory perceptions of flavor. Here, we combine fruit chemical and consumer sensory panel information to train machine learning models that can predict how flavorful a fruit will be from its chemistry. By increasing the throughput of flavor evaluations, these models will help plant breeders to integrate flavor earlier in the breeding pipeline and aid in the design of varieties with exceptional flavor profiles.

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