Suppr超能文献

杏(L.)果实中的营养保健品成分特征分析

Nutraceutical Profile Characterization in Apricot ( L.) Fruits.

作者信息

Ortuño-Hernández Germán, Silva Marta, Toledo Rosa, Ramos Helena, Reis-Mendes Ana, Ruiz David, Martínez-Gómez Pedro, Ferreira Isabel M P L V O, Salazar Juan Alfonso

机构信息

Fruit Breeding Group, Department of Plant Breeding, CEBAS-CSIC (Centro de Edafología y Biología Aplicada del Segura-Consejo Superior de Investigaciones Científicas), Campus Universitario Espinardo, E-30100 Murcia, Spain.

LAQV/REQUIMTE, Departamento de Ciências Químicas, Laboratório de Bromatologia e Hidrologia, Faculdade de Farmácia, Universidade do Porto, Rua de Jorge Viterbo Ferreira n°. 228, 4050-313 Porto, Portugal.

出版信息

Plants (Basel). 2025 Mar 22;14(7):1000. doi: 10.3390/plants14071000.

Abstract

This study characterizes the metabolomic profiles of three reference apricot cultivars ('Bergeron', 'Currot', and 'Goldrich') using H NMR spectroscopy and untargeted UPLC-QToF MS/MS to support plant breeding by correlating metabolomic data with fruit phenotyping. The primary objective was to identify and quantify the key metabolites influencing fruit quality from a nutraceutical perspective. The analysis revealed significant differences in primary and secondary metabolites among the cultivars. 'Bergeron' and 'Goldrich' exhibited higher concentrations of organic acids (109 mg/g malate in 'Bergeron' and 202 mg/g citrate in 'Goldrich'), flavonoids such as epicatechin (0.44 mg/g and 0.79 mg/g, respectively), and sucrose (464 mg/g and 546 mg/g), contributing to their acidity-to-sugar balance. Conversely, 'Currot' showed higher levels of amino acids (24.44 mg/g asparagine) and sugars, particularly fructose and glucose (79 mg/g and 180 mg/g), enhancing its characteristic sweetness. These findings suggest that metabolomic profiling can provide valuable insights into the biochemical pathways underlying apricot quality traits, aiding in the selection of cultivars with desirable characteristics. The integration of phenotyping data with H NMR and UPLC-QToF MS/MS offers a comprehensive approach to understanding apricot metabolomic diversity, crucial for breeding high-quality, nutritionally enriched fruits that meet market demands.

摘要

本研究利用核磁共振氢谱(H NMR)光谱和非靶向超高效液相色谱-四极杆飞行时间串联质谱(UPLC-QToF MS/MS)对三个参考杏品种(‘伯杰龙’、‘库罗特’和‘戈尔德里奇’)的代谢组学图谱进行了表征,通过将代谢组学数据与果实表型相关联来支持植物育种。主要目标是从营养保健角度识别和量化影响果实品质的关键代谢物。分析揭示了各品种之间初级和次级代谢物的显著差异。‘伯杰龙’和‘戈尔德里奇’表现出较高浓度的有机酸(‘伯杰龙’中苹果酸为109毫克/克,‘戈尔德里奇’中柠檬酸为202毫克/克)、表儿茶素等黄酮类化合物(分别为0.44毫克/克和0.79毫克/克)以及蔗糖(464毫克/克和546毫克/克),这有助于它们的酸度与糖分平衡。相反,‘库罗特’显示出较高水平的氨基酸(天冬酰胺为24.44毫克/克)和糖类,特别是果糖和葡萄糖(79毫克/克和180毫克/克),增强了其特有的甜度。这些发现表明,代谢组学分析可以为杏品质性状背后的生化途径提供有价值的见解,有助于选择具有理想特性的品种。将表型数据与核磁共振氢谱和超高效液相色谱-四极杆飞行时间串联质谱相结合,为理解杏代谢组学多样性提供了一种全面的方法,这对于培育满足市场需求的高品质、营养丰富的果实至关重要。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b7fd/11990447/c55a2374f9e8/plants-14-01000-g001.jpg

相似文献

1
Nutraceutical Profile Characterization in Apricot ( L.) Fruits.
Plants (Basel). 2025 Mar 22;14(7):1000. doi: 10.3390/plants14071000.
2
Comparative study of primary and secondary metabolites in apricot (Prunus armeniaca L.) cultivars.
J Sci Food Agric. 2011 Mar 30;91(5):860-6. doi: 10.1002/jsfa.4257.
4
Genetic linkage maps of two apricot cultivars ( Prunus armeniaca L.), and mapping of PPV (sharka) resistance.
Theor Appl Genet. 2002 Aug;105(2-3):182-191. doi: 10.1007/s00122-002-0936-y. Epub 2002 Jun 14.
5
Analysis of Metabolites and Gene Expression Changes Relative to Apricot ( L.) Fruit Quality During Development and Ripening.
Front Plant Sci. 2020 Aug 19;11:1269. doi: 10.3389/fpls.2020.01269. eCollection 2020.

本文引用的文献

1
Monitoring Fruit Growth and Development in Apricot ( L.) through Gene Expression Analysis.
Int J Mol Sci. 2024 Aug 21;25(16):9081. doi: 10.3390/ijms25169081.
9
Stone Fruits: Growth and Nitrogen and Organic Acid Metabolism in the Fruits and Seeds-A Review.
Front Plant Sci. 2020 Sep 25;11:572601. doi: 10.3389/fpls.2020.572601. eCollection 2020.
10
Analysis of Metabolites and Gene Expression Changes Relative to Apricot ( L.) Fruit Quality During Development and Ripening.
Front Plant Sci. 2020 Aug 19;11:1269. doi: 10.3389/fpls.2020.01269. eCollection 2020.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验