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广泛靶向代谢组学用于揭示不同葡萄酒中非挥发性化合物的差异及其与感官特性的关联。

Widely Targeted Metabolomics Was Used to Reveal the Differences between Non-Volatile Compounds in Different Wines and Their Associations with Sensory Properties.

作者信息

Cao Weiyu, Shu Nan, Wen Jinli, Yang Yiming, Wang Yanli, Lu Wenpeng

机构信息

Institute of Special Animal and Plant Sciences of Chinese Academy of Agricultural Sciences, Changchun 130117, China.

出版信息

Foods. 2023 Jan 8;12(2):290. doi: 10.3390/foods12020290.

DOI:10.3390/foods12020290
PMID:36673382
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9857859/
Abstract

In this study, metabolites from six varieties of wines, including 'Haasan' (A1), 'Zuoshaner' (A2), 'Beibinghong' (A3), 'Shuanghong' (A4), 'Zijingganlu' (A5), and 'Cabernet Sauvignon' (A6), were identified and quantified using widely targeted metabolomics analysis techniques. Based on the test results, 1172 metabolites were detected and classified into 18 categories. These include 62 amino acids, 178 alkaloids, 189 flavonoids, 106 phenols, 148 terpenoids, etc. Comparing the differential metabolites between the comparison groups of each variety, differences between varieties based on P-values and VIP values were shown. Among these differential metabolites, Trimethoprim and Crotonoside were screened out as core differential metabolites. Multiple comparisons also screened the biomarkers for each species. We used widely targeted metabolomics to reveal the differences between non-volatile compounds in different wines and their associations with sensory properties. We also used the simultaneous weighted gene co-expression network analysis (WGCNA) to correlate metabolites with sensory traits, including color difference values and taste characteristics. Two of the six key modules were screened by WGCNA for relevance to sensory traits (brown module and turquoise module). This study provides a high-throughput method for linking compounds to various sensory characteristics of food, opening up new avenues for explaining differences in different varieties of wine.

摘要

在本研究中,使用广泛靶向代谢组学分析技术对包括“哈桑”(A1)、“左山二”(A2)、“北冰红”(A3)、“双红”(A4)、“紫晶甘露”(A5)和“赤霞珠”(A6)在内的六个葡萄酒品种的代谢物进行了鉴定和定量。根据测试结果,共检测到1172种代谢物,并将其分为18类。这些代谢物包括62种氨基酸、178种生物碱、189种黄酮类化合物、106种酚类、148种萜类化合物等。比较各品种比较组之间的差异代谢物,基于P值和VIP值显示了品种间的差异。在这些差异代谢物中,筛选出甲氧苄啶和巴豆苷作为核心差异代谢物。多重比较还筛选出了每个品种的生物标志物。我们使用广泛靶向代谢组学来揭示不同葡萄酒中非挥发性化合物之间的差异及其与感官特性的关联。我们还使用同步加权基因共表达网络分析(WGCNA)将代谢物与感官特征相关联,包括色差和味觉特征。通过WGCNA筛选出六个关键模块中的两个与感官特征相关(棕色模块和绿松石模块)。本研究提供了一种将化合物与食品的各种感官特征联系起来的高通量方法,为解释不同品种葡萄酒的差异开辟了新途径。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604f/9857859/1f6491554773/foods-12-00290-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604f/9857859/140a027ae145/foods-12-00290-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604f/9857859/56ada341d2a0/foods-12-00290-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604f/9857859/d64acaf7514e/foods-12-00290-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604f/9857859/6806d634e485/foods-12-00290-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604f/9857859/2456153aa7cb/foods-12-00290-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604f/9857859/009638ac12c4/foods-12-00290-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604f/9857859/2f52e6b33590/foods-12-00290-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604f/9857859/af422df86175/foods-12-00290-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604f/9857859/1f6491554773/foods-12-00290-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604f/9857859/140a027ae145/foods-12-00290-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604f/9857859/56ada341d2a0/foods-12-00290-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604f/9857859/d64acaf7514e/foods-12-00290-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604f/9857859/6806d634e485/foods-12-00290-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604f/9857859/2456153aa7cb/foods-12-00290-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604f/9857859/009638ac12c4/foods-12-00290-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604f/9857859/2f52e6b33590/foods-12-00290-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604f/9857859/af422df86175/foods-12-00290-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/604f/9857859/1f6491554773/foods-12-00290-g009.jpg

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