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Metabolomics and machine learning integrated analysis of flavor quality in ‘Legacy’ and a mutant blueberry

Year: 2025
Type: article
Abstract: Blueberry flavor quality, as a core determinant of its commercial value and consumer acceptance, has become an important goal of blueberry breeding. This study compared flavor quality characteristics of the 'Legacy' and a natural mutant blueberry. The results revealed that mutant fruits showed a reduction in fruit weight and anthocyanin content. Further primary metabolism analysis revealed that the content of amino acids in mutant fruit was increased compared to the 'Legacy' fruit, while the content of organic acids was reduced. Based on the HS-SPME-GC–MS analysis, the content of volatile organic compounds (VOCs) in the mutant blueberry was doubled compared to the 'Legacy' fruit, with terpenoids increasing by 8 times, and alcohols, esters, and aldehydes increasing by 1.5-2.2 times. Further analysis revealed that 189 VOCs were significantly upregulated in mutant fruit (including 40 alcohols, 30 esters, and 23 terpenoids). Metabolomics analysis indicated that the accumulation of amino acids and specific VOCs, as well as the reduction of citric acid in mutant fruit, enhanced sweetness and aroma profiles through a synergistic effect. Random forest algorithm identified characteristic metabolites such as alanine and 3-cyclohexene-1-carbaldehyde, indicating their potential as candidate targets for blueberry flavor breeding. Our results provided new insights into the mechanisms of flavor formation and molecular design breeding of high-flavor quality blueberries. • Mutant fruit exhibited reduced fruit weight, size, and anthocyanin content compared to 'Legacy'. • Mutant fruit accumulated more amino acids and less citric acid. • The content of VOCs in mutant fruit was significantly elevated, especially alcohols, esters and terpenoids. • Five primary metabolites and five VOCs were identified as characteristic metabolites.
Authors: Mingfei Zhang, Yuhan Shen, Liang Fang, Jiayi Yan, Yuming Ren, Fuyun Yang, Yu Zong, Xun Wang
Language: English
Subfield: Plant Science
Open Access status: gold
Estimated APC paid: $3,170
OpenAlex ID: W4417488614
FWCI:
Cites: 47
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