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<p>The phenotypic diversity in fruit quality traits was systematically analyzed across 35 sweet cherry (<i>Prunus avium</i> L.) cultivars. Significant inter-cultivar differences were observed in appearance, flavor, and texture, with coefficients of variation (CVs) exceeding 30% for pedicel weight, skin color, and the contents of sorbitol and shikimic acid. Analysis of total soluble solids revealed significant negative correlations with both single fruit weight (<i>r</i> = − 0.71) and fruit diameter (<i>r</i> = − 0.61). Among textural attributes, chewiness displayed the highest CV of 70.67%, whereas hardness showed an inverse relationship with elasticity, reflecting a biological trade-off in mechanical properties. Electronic-nose (e-nose) analysis showed that skin retention boosts volatile detection; PCA of textural data separated cultivars into storage-suitable (“Summit”), premium-texture (“Victory”), and processing (“Coral Champagne”) types. Fruit skin modulated volatile compound release, with e-nose sensors (W1C, W5C, and W1W) exhibiting higher responsiveness to skin-associated volatiles. Electronic tongue (e-tongue) analysis quantified taste attributes, identifying sourness as having the highest CV at 87.3%. Cluster analysis classified cultivars into high-sweetness-umami (for example, “Tieton”), high-acidity-astringency (for example, “Bato”), and balanced (for example, “Summit”) groups. E-tongue analysis revealed three taste clusters (high-sweet, high-acid, and balanced). Our findings indicate that integration of a texture analyzer, e-tongue, and e-nose represents an efficient approach for breeding and market classification of sweet cherry cultivars.</p>

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Integrated phenotyping of 35 sweet cherry cultivars for texture, aroma, and taste diversity using a texture analyzer, e-nose, and e-tongue

  • 期刊论文

  • 发表日期：

    2025年12月20日

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  • Yao Chen,
  • Feng Cai,
  • Jiayin Ai,
  • Mingli He,
  • Qijing Zhang

摘要

The phenotypic diversity in fruit quality traits was systematically analyzed across 35 sweet cherry (Prunus avium L.) cultivars. Significant inter-cultivar differences were observed in appearance, flavor, and texture, with coefficients of variation (CVs) exceeding 30% for pedicel weight, skin color, and the contents of sorbitol and shikimic acid. Analysis of total soluble solids revealed significant negative correlations with both single fruit weight (r = − 0.71) and fruit diameter (r = − 0.61). Among textural attributes, chewiness displayed the highest CV of 70.67%, whereas hardness showed an inverse relationship with elasticity, reflecting a biological trade-off in mechanical properties. Electronic-nose (e-nose) analysis showed that skin retention boosts volatile detection; PCA of textural data separated cultivars into storage-suitable (“Summit”), premium-texture (“Victory”), and processing (“Coral Champagne”) types. Fruit skin modulated volatile compound release, with e-nose sensors (W1C, W5C, and W1W) exhibiting higher responsiveness to skin-associated volatiles. Electronic tongue (e-tongue) analysis quantified taste attributes, identifying sourness as having the highest CV at 87.3%. Cluster analysis classified cultivars into high-sweetness-umami (for example, “Tieton”), high-acidity-astringency (for example, “Bato”), and balanced (for example, “Summit”) groups. E-tongue analysis revealed three taste clusters (high-sweet, high-acid, and balanced). Our findings indicate that integration of a texture analyzer, e-tongue, and e-nose represents an efficient approach for breeding and market classification of sweet cherry cultivars.