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Metabolomic Selection for Enhanced Fruit Flavor

Vincent Colantonio, Luis Felipe V. Ferrão, Denise Tieman, Nikolay Bliznyuk, Charles Sims, Harry Klee, Patricio R. Munoz, Marcio F. R. Resende Jr
doi: https://doi.org/10.1101/2020.09.17.302802
Vincent Colantonio
1Horticultural Sciences Department, University of Florida, Gainesville, FL, USA
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Luis Felipe V. Ferrão
1Horticultural Sciences Department, University of Florida, Gainesville, FL, USA
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Denise Tieman
1Horticultural Sciences Department, University of Florida, Gainesville, FL, USA
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Nikolay Bliznyuk
2Agricultural and Biological Engineering, University of Florida, Gainesville, FL, USA
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Charles Sims
3Food Science and Human Nutrition, University of Florida
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Harry Klee
1Horticultural Sciences Department, University of Florida, Gainesville, FL, USA
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  • For correspondence: mresende@ufl.edu pmunoz@ufl.edu hjklee@ufl.edu
Patricio R. Munoz
1Horticultural Sciences Department, University of Florida, Gainesville, FL, USA
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  • For correspondence: mresende@ufl.edu pmunoz@ufl.edu hjklee@ufl.edu
Marcio F. R. Resende Jr
1Horticultural Sciences Department, University of Florida, Gainesville, FL, USA
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  • For correspondence: mresende@ufl.edu pmunoz@ufl.edu hjklee@ufl.edu
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Abstract

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 Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted September 18, 2020.
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Metabolomic Selection for Enhanced Fruit Flavor
Vincent Colantonio, Luis Felipe V. Ferrão, Denise Tieman, Nikolay Bliznyuk, Charles Sims, Harry Klee, Patricio R. Munoz, Marcio F. R. Resende Jr
bioRxiv 2020.09.17.302802; doi: https://doi.org/10.1101/2020.09.17.302802
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Metabolomic Selection for Enhanced Fruit Flavor
Vincent Colantonio, Luis Felipe V. Ferrão, Denise Tieman, Nikolay Bliznyuk, Charles Sims, Harry Klee, Patricio R. Munoz, Marcio F. R. Resende Jr
bioRxiv 2020.09.17.302802; doi: https://doi.org/10.1101/2020.09.17.302802

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