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Article(id=1153986586694898566, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1153986579971429187, articleNumber=null, orderNo=null, doi=10.19812/j.cnki.jfsq11-5956/ts.20241125001, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1732464000000, receivedDateStr=2024-11-25, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1753061442302, onlineDateStr=2025-07-21, pubDate=1740412800000, pubDateStr=2025-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1753061442302, onlineIssueDateStr=2025-07-21, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1753061442302, creator=13701087609, updateTime=1753061442302, updator=13701087609, issue=Issue{id=1153986579971429187, tenantId=1146029695717560320, journalId=1149652044408987649, year='2025', volume='16', issue='4', pageStart='1', pageEnd='320', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1753061440699, creator=13701087609, updateTime=1758783495950, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1177986619249406427, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1153986579971429187, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1177986619249406428, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1153986579971429187, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=203, endPage=208, ext={EN=ArticleExt(id=1153986587445678997, articleId=1153986586694898566, tenantId=1146029695717560320, journalId=1149652044408987649, language=EN, title=Analysis of characteristics and differences of volatile components in different Wuliangye liquor series based on non targeted metabonomics, columnId=1153986581653349021, journalTitle=Journal of Food Safety & Quality, columnName=Special Topic: Application of Modern Analysis Instrument in Food Detection, runingTitle=null, highlight=null, articleAbstract=

Objective To analyze the characteristics and differences of volatile components in Wuliangye liquor based on non targeted metabonomics technology. Methods The characteristics and differences of volatile components of 5 series of liquor including Wuliangye, Wuliangye alcohol, Wuliangchun, Wuliangtequ and Wuliangtouqu liquor was analyzed by non targeted metabonomics technology. Results A total of 900 kinds of compounds were detected in 5 series of liquor, including 26 kinds of amines, 86 kinds of alcohols, 51 kinds of aromatics, 18 kinds of phenols, 11 kinds of nitrogenous compounds, 11 kinds of sulfur compounds, 3 kinds of halogenated hydrocarbons, 4 kinds of ethers, 47 kinds of aldehydes, 28 kinds of acids, 143 kinds of terpenes, 78 kinds of hydrocarbons, 79 kinds of ketones, 139 kinds of heterocyclic compounds, 172 kinds of esters, and 4 kinds of others. The number and composition of compounds detected in different Wuliangye liquor were different. The number of compounds with significant differences in Wuliangye was significantly higher than that in Wuliangye alcohol, Wuliangchun, Wuliangtequ and Wuliangtouqu. Conclusion The difference of composition and content in different Wuliangye series liquor is an important factor reflecting the quality of liquor. Non targeted metabonomics technology has good application in the analysis and identification of liquor characteristic components, providing reliable data support for scientific differentiation of Wuliangye series liquor.

, correspAuthors=Qi-Le ZHOU, Ya-Juan CUI, authorNote=null, correspAuthorsNote=null, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=null, magXml=null, pdfUrl=null, pdf=null, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=null, mapNumber=null, authorCompany=null, fund=null, authors=null, authorsList=Fan-Hua KONG, Jing-Jing LI, Jia ZHENG, Da LI, Sha-sha BAI, Yan-Ping LU, Qi-Le ZHOU, Ya-Juan CUI), CN=ArticleExt(id=1153986618307367595, articleId=1153986586694898566, tenantId=1146029695717560320, journalId=1149652044408987649, language=CN, title=基于非靶向代谢组学解析五粮液系列白酒的挥发性组分特征及差异, columnId=1153986581842092705, journalTitle=食品安全质量检测学报, columnName=本期专题：现代分析仪器在食品检测中的应用, runingTitle=null, highlight=null, articleAbstract=

目的 基于非靶向代谢组学技术, 解析五粮液系列白酒的挥发性组分特征及差异。方法 利用非靶向代谢组学技术研究五粮液、五粮醇、五粮春、五粮特曲和五粮头曲5个系列白酒中挥发性组分特征及差异。结果 5个系列白酒中共检出900种化合物, 其中胺类26种、醇类86种、芳烃类51种、酚类18种、含氮化合物类11种、含硫化合物类11种、卤代烃类3种、醚类4种、醛类47种、酸类28种、萜类143种、烃类78种、酮类79种、杂环化合物139种、酯类172种、其他类4种。不同五粮液白酒检出化合物的数量和组成存在差别, 五粮液中差异显著化合物数目显著高于五粮醇、五粮春、五粮特曲和五粮头曲。结论 不同五粮液系列白酒中组成和含量的差异是反映白酒品质的重要因素, 非靶向代谢组学技术在白酒特征成分的分析鉴定中具有良好的应用性, 为科学区分五粮液系列白酒提供可靠的数据支持。

, correspAuthors=周琪乐, 崔亚娟, authorNote=null, correspAuthorsNote=
* 周琪乐(1986—), 男, 博士, 副研究员, 主要研究方向为食品功能因子和中药药效物质研究。E-mail:
崔亚娟(1979—), 女, 博士, 研究员, 主要研究方向为食物营养分析检测及应用研究。E-mail:
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孔凡华(1989—), 女, 硕士, 高级工程师, 主要研究方向为食物营养分析检测。E-mail:

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孔凡华(1989—), 女, 硕士, 高级工程师, 主要研究方向为食物营养分析检测。E-mail:

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孔凡华(1989—), 女, 硕士, 高级工程师, 主要研究方向为食物营养分析检测。E-mail:

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Statistics of the number of differential compounds

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差异比较组信息 差异显著化合物数目 下调化合物数目 上调化合物数目
BJ1/BJ2 448 431 17
BJ1/BJ3 173 160 13
BJ1/BJ4 531 523 8
BJ1/BJ5 508 483 25
BJ2/BJ3 218 10 208
BJ2/BJ4 16 8 8
BJ2/BJ5 44 23 21
BJ3/BJ4 327 318 9
BJ3/BJ5 360 331 29
BJ4/BJ5 32 5 27
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差异化合物数目统计表

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差异比较组信息 差异显著化合物数目 下调化合物数目 上调化合物数目
BJ1/BJ2 448 431 17
BJ1/BJ3 173 160 13
BJ1/BJ4 531 523 8
BJ1/BJ5 508 483 25
BJ2/BJ3 218 10 208
BJ2/BJ4 16 8 8
BJ2/BJ5 44 23 21
BJ3/BJ4 327 318 9
BJ3/BJ5 360 331 29
BJ4/BJ5 32 5 27
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基于非靶向代谢组学解析五粮液系列白酒的挥发性组分特征及差异
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孔凡华 1 , 李菁菁 1 , 郑佳 2 , 厉妲 3 , 白沙沙 1 , 卢彦坪 1 , 周琪乐 1, * , 崔亚娟 1, *
食品安全质量检测学报 | 本期专题：现代分析仪器在食品检测中的应用 2025,16(4): 203-208
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食品安全质量检测学报 | 本期专题：现代分析仪器在食品检测中的应用 2025, 16(4): 203-208
基于非靶向代谢组学解析五粮液系列白酒的挥发性组分特征及差异
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孔凡华1 , 李菁菁1, 郑佳2, 厉妲3, 白沙沙1, 卢彦坪1, 周琪乐1, * , 崔亚娟1, *
作者信息
  • 1.北京市营养源研究所有限公司, 北京 100069
  • 2.宜宾五浪液股份有限公司, 宜宾 644000
  • 3.北京城市学院生物医药学部, 北京 100083

    • 孔凡华(1989—), 女, 硕士, 高级工程师, 主要研究方向为食物营养分析检测。E-mail:

通讯作者:

* 周琪乐(1986—), 男, 博士, 副研究员, 主要研究方向为食品功能因子和中药药效物质研究。E-mail:
崔亚娟(1979—), 女, 博士, 研究员, 主要研究方向为食物营养分析检测及应用研究。E-mail:
Analysis of characteristics and differences of volatile components in different Wuliangye liquor series based on non targeted metabonomics
Fan-Hua KONG1 , Jing-Jing LI1, Jia ZHENG2, Da LI3, Sha-sha BAI1, Yan-Ping LU1, Qi-Le ZHOU1, * , Ya-Juan CUI1, *
Affiliations
  • 1. Beijing Institute of Nutritional Resources Co., Ltd., Beijing 100069, China
  • 2. Wuliangye Yibin Co., Ltd., Yibin 644000, China
  • 3. School of Biomedicine, Beijing City University, Beijing 100083, China
出版时间: 2025-02-25 doi: 10.19812/j.cnki.jfsq11-5956/ts.20241125001
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目的 基于非靶向代谢组学技术, 解析五粮液系列白酒的挥发性组分特征及差异。方法 利用非靶向代谢组学技术研究五粮液、五粮醇、五粮春、五粮特曲和五粮头曲5个系列白酒中挥发性组分特征及差异。结果 5个系列白酒中共检出900种化合物, 其中胺类26种、醇类86种、芳烃类51种、酚类18种、含氮化合物类11种、含硫化合物类11种、卤代烃类3种、醚类4种、醛类47种、酸类28种、萜类143种、烃类78种、酮类79种、杂环化合物139种、酯类172种、其他类4种。不同五粮液白酒检出化合物的数量和组成存在差别, 五粮液中差异显著化合物数目显著高于五粮醇、五粮春、五粮特曲和五粮头曲。结论 不同五粮液系列白酒中组成和含量的差异是反映白酒品质的重要因素, 非靶向代谢组学技术在白酒特征成分的分析鉴定中具有良好的应用性, 为科学区分五粮液系列白酒提供可靠的数据支持。

五粮液  /  白酒  /  挥发性组分  /  非靶向代谢组学  /  气相色谱-质谱法

Objective To analyze the characteristics and differences of volatile components in Wuliangye liquor based on non targeted metabonomics technology. Methods The characteristics and differences of volatile components of 5 series of liquor including Wuliangye, Wuliangye alcohol, Wuliangchun, Wuliangtequ and Wuliangtouqu liquor was analyzed by non targeted metabonomics technology. Results A total of 900 kinds of compounds were detected in 5 series of liquor, including 26 kinds of amines, 86 kinds of alcohols, 51 kinds of aromatics, 18 kinds of phenols, 11 kinds of nitrogenous compounds, 11 kinds of sulfur compounds, 3 kinds of halogenated hydrocarbons, 4 kinds of ethers, 47 kinds of aldehydes, 28 kinds of acids, 143 kinds of terpenes, 78 kinds of hydrocarbons, 79 kinds of ketones, 139 kinds of heterocyclic compounds, 172 kinds of esters, and 4 kinds of others. The number and composition of compounds detected in different Wuliangye liquor were different. The number of compounds with significant differences in Wuliangye was significantly higher than that in Wuliangye alcohol, Wuliangchun, Wuliangtequ and Wuliangtouqu. Conclusion The difference of composition and content in different Wuliangye series liquor is an important factor reflecting the quality of liquor. Non targeted metabonomics technology has good application in the analysis and identification of liquor characteristic components, providing reliable data support for scientific differentiation of Wuliangye series liquor.

Wuliangye  /  liquor  /  volatile components  /  non targeted metabolomics  /  gas chromatography-mass spectrometry method
孔凡华, 李菁菁, 郑佳, 厉妲, 白沙沙, 卢彦坪, 周琪乐, 崔亚娟. 基于非靶向代谢组学解析五粮液系列白酒的挥发性组分特征及差异. 食品安全质量检测学报, 2025 , 16 (4) : 203 -208 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20241125001
Fan-Hua KONG, Jing-Jing LI, Jia ZHENG, Da LI, Sha-sha BAI, Yan-Ping LU, Qi-Le ZHOU, Ya-Juan CUI. Analysis of characteristics and differences of volatile components in different Wuliangye liquor series based on non targeted metabonomics[J]. Journal of Food Safety & Quality, 2025 , 16 (4) : 203 -208 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20241125001
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0 引言
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白酒的酿造和饮用在中国已经有几千年的历史, 是世界上最古老的蒸馏酒之一[1], 主要以小麦、高粱等为原料采用固态发酵制成, 距今已有2000多年的发展历史[2]。白酒作为我国的国酒, 不仅承载着悠久的中华文明和厚重的酒文化, 而且具有健康促进作用, 现代医学表明饮酒可以降低冠心病的死亡率[3-4], 降低空腹血糖含量[5-6], 预防心血管疾病[7-8], 预防癌症[9-10], 增强人体免疫功能[11-14]。白酒中98%的成分是水和乙醇, 其他约2%的微量成分决定了白酒的香型和风格, 赋予白酒独特的风味特征和生物活性[15]。微量成分中主要包括挥发性成分醇类、酚类、酸类、酯类、含氮化合物、含硫化合物, 其中不同香型白酒的挥发性成分的种类、香气阈值、相互作用等已经进行了系统的研究报道[16], 非挥发性成分包括游离氨基酸、矿物质、维生素、肽等[17], 近年来, 随着“健康中国2030纲要”的实施, 非挥发性成分作为白酒中重要的微量成分也逐渐成为学者们研究的热点[18]。
随着现代分析技术的发展, 如高效的前处理方法, 高精密度和低检出限的液相色谱法、液相色谱-质谱联用法、气相色谱-质谱联用法(gas chromatography-mass spectrometry, GC-MS)等检测技术[19-23]的应用使得白酒中的健康因子的鉴定成为可能。白酒中的健康因子在近些年得到了较为系统的研究。据统计, 白酒微量成分已多达1874种[24]。由于白酒中各物质的含量、极性、沸点、溶解度、挥发性差异较大, 因此分析不同化合物没有固定的预处理方法。目前, 样品提取的主要方法有直接进样法、液液萃取法、固相萃取法、液液微萃取法和固相微萃取法等[25-29]。
白酒健康因子的鉴定和功能性验证, 为白酒的健康作用提供了科学依据。白酒中的微量成分在摄入人体后作为整体发挥健康促进作用, 研究白酒中非挥发性成分的组成差异并验证其相应的生物活性具有重要意义。本研究以五粮液集团五粮系列白酒为研究对象, 基于组学技术分析不同系列白酒的质谱测定结果, 通过分层聚类和主成分分析确定不同系列白酒的非挥发性成分差异性, 为不同系列白酒的分类提供技术支持, 有利于优质白酒产品的开发和工艺改进, 能够有效提高白酒企业的经济效益。
1 材料与方法
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1.1 材料与试剂
采购宜宾五粮液股份有限公司酒精度相近的白酒产品5款, 分别为: 五粮液普五第八代(52%vol)(编号: BJ1)、五粮醇红装(50%vol)(编号: BJ2)、五粮春第二代(52%vol)(编号: BJ3)、五粮特曲(52%vol)(编号: BJ4)、五粮头曲醇品(52%vol)(编号: BJ5), 生产日期均为2022年, 采购于京东商城, 所有样品均保存在干燥避光室温环境中。
正己烷(色谱纯, 德国默克公司); 氯化钠(色谱纯, 中国医药集团有限公司); 超纯水由实验室制备。
1.2 仪器与设备
GCMS-TQ 8050气相色谱质谱联用仪(日本岛津公司); Vortex2涡旋混合器(德国艾卡公司); DB-5MS毛细管柱(30 m× 0.25 mm, 0.25 μm)(美国安捷伦公司)。
1.3 实验方法
1.3.1 样品前处理
取5 mL白酒样品分别加入饱和氯化钠溶液中, 将5种五粮液白酒等体积混合作为质量控制样品, 在相同处理条件下进行样品的重复性测定。全自动顶空固相微萃取(headspace solid-phase microextraction, HS-SPME), 以GC-MS分析。
1.3.2 HS-SPME萃取条件
在60 ℃恒温条件下, 振荡5 min, 120 μm DVB/ CWR/PDMS萃取头插入样品顶空瓶, 顶空萃取15 min, 于250 ℃下解析5 min, 然后进行GC-MS分离鉴定。采样前萃取头在老化装置中250 ℃下老化5 min。
1.3.3 色谱条件
DB-5MS毛细管柱, 载气为高纯氨气(纯度不小于99.999%),流速1.2 mL/min, 进样口温度250 ℃, 不分流进样, 溶剂延迟3.5 min。程序升温: 40 ℃保持3.5 min, 以10 ℃/min升至100 ℃, 再以7 ℃/min升至180 ℃, 最后以25 ℃/min升至280 ℃, 保持5 min。
1.3.4 质谱条件
电子轰击离子源(electron impact ionization, EI), 离子源温度230 ℃, 四极杆温度150 ℃, 质谱接口温度280 ℃, 电子能量70 eV, 扫描方式为选择离子检测模式(selected ion monitoring, SIM)。
1.3.5 定性定量条件
基于多物种、文献、部分标品以及保留指数, 自主建立数据库, 包含确定的保留时间以及定性定量离子进行选择离子检测模式进行精准扫描, 每种化合物分别选择1个定量离子, 2~3个定性离子。每组所有需要检测的离子按照出峰顺序, 分时段分别检测, 如果检出的色谱峰的保留时间与标准参考相一致, 并且在扣除背景后的样品质谱图中, 所选择的离子均出现, 判定为该物质, 选择定量离子进行色谱峰的积分和校正工作, 增强定量的准确性。
1.4 数据处理
对于GC-MS平台, 用MassHunter定量软件处理样本下机质谱文件, 选择定量离子进行色谱峰的积分和校正工作。
为了比较所有检测到的代谢物中每个代谢物在不同样本中的物质含量差异, 根据代谢物保留时间与峰型的信息, 对每个代谢物在不同样本中检测到的质谱峰进行校正, 以确保定性定量的准确性。
主成分分析(principal component analysis, PCA): 通过统计函数prcomp在R软件(www.r-project.org)内进行无监督PCA, 在无监督PCA前对数据进行单位方差缩放。
聚类分析(cluster analysis, CA): 一种分类的多变量统计分析方法。按照个体或样品的特征将它们分类, 使同一类别内的个体具有尽可能高的同质性, 而类别之间则应具有尽可能高的异质性。代谢物含量数据采用单位方差缩放(unit variance scaling, UV)处理, 通过R软件ComplexHeatmap包绘制热图, 对代谢物在不同样本间的积累模式进行层次聚类分析(hierarchical cluster analysis, HCA)。HCA和皮尔逊相关系数均采用R包ComplexHeatmap进行。样品和化合物的HCA结果以带有树状图的热图表示, 样品之间的皮尔逊相关系数通过R中的相关函数计算。
差异化合物筛选: 根据差异倍数值进行差异化合物筛选, 选取差异倍数≥2和差异倍数≤0.5的组分, 化合物在两组中的差异为2倍以上或0.5以下, 判定为差异显著。
2 结果与分析
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2.1 化合物总体分析
经过非靶向代谢组学分析, 5款白酒共检出900种化合物, 见图1。其中胺类26种、醇类86种、芳烃类51种、酚类18种、含氮化合物类11种、含硫化合物类11种、卤代烃类3种、醚类4种、醛类47种、酸类28种、萜类143种、烃类78种、酮类79种、杂环化合物139种、酯类172种、其他类4种。
2.2 主成分分析
采用多变量统计分析, 可以在最大程度保留原始信息的基础上将高维复杂的数据进行“简化和降维”, 建立可靠的数学模型对研究对象的代谢谱特点进行归纳和总结。其中, PCA是一种无监督模式识别的多维数据统计分析方法, 通过正交变换将一组可能存在相关性的变量转换为一组线性不相关的变量, 转换后的这组变量叫主成分。这个分析方法常用来研究如何通过少数几个主成分来揭示多个变量间的内部结构, 即从原始变量中导出少数几个主成分, 使它们尽可能多地保留原始变量的信息, 且彼此间互不相关, 通常数学上的处理就是将原来多人指标作线性组合, 作为新的综合指标。
PCA的数据处理原理是将原始数据压缩成n个主成分来描述原始数据集的特征, PC1表示能描述多维数据矩阵中最明显的特征, PC2表示除PC1之外的所能描述数据矩阵中最显著的特征, PC3......PCn以此类推。PCA用R软件的内置统计prcomp函数, 设置prcomp函数参数scale=True, 表示对数据进行UV处理。5款白酒PCA结果见图2。
由PCA结果可知, 图中的每个点表示一个样品, 同一个组的样品使用同一种颜色表示。5款白酒均具有较好地区分, 在第1主成分上, 五粮液和五粮春两款白酒明显区别于五粮醇、五粮特曲和五粮头曲3款白酒, 且五粮液与其他4款白酒在主成分二维分布图中距离均非常明显, 具有很好的区分特性。
2.3 聚类分析
GC-MS测定结果经过数据预处理, 聚类算法根据距离进行判断类别, 需要在聚类之前进行标准化处理, 白酒中化合物CA热图见图3, 白酒样品CA热图见图4。
化合物CA热图中不同颜色代表不同相对含量标准化处理后得到的不同数值进行填充的颜色, 红色代表高含量, 绿色代表低含量。图中左侧的聚类线为代谢物聚类线, 图中上方的聚类线为样品聚类线。由化合物CA热图可以看出, 五粮液白酒在所有类别化合物中含量均比较显著, 其次是五粮春白酒, GC-MS测定结果与白酒品鉴所呈现出的口味具有一定的相关性, 可以侧面反应白酒的品质。
由样品CA结果可知, 五粮特曲和五粮头曲首先聚为一类, 然后与五粮醇聚为一类, 再次与五粮春聚为一类, 最后是与五粮液聚类。由样品聚类结果也可看出5款白酒的品质存在显著性差异, 五粮液和五粮春在多数化合物中均存在含量优势。
2.4 重复相关性评估
通过样品之间的相关性分析可以观察组内样品之间的生物学重复。同时组内样品相对组间样品的相关系数越高, 获得的差异代谢物越可靠。将皮尔逊相关系数r作为生物学重复相关性的评估指标。皮尔逊相关系数利用R软件的内置cor函数计算, |r|越接近1, 说明两个重复样品相关性越强。样品间相关性图见图5, 纵向和对角线上分别代表不同样品的样品名称, 不同的颜色代表不同的皮尔逊相关系数大小, 颜色越红代表正相关性越强, 颜色越绿代表相关性越弱, 颜色越蓝代表负相关性越强, 同时两个样品之间的相关性系数大小标注在方格内。由图5可知, 样品的生物学重复的皮尔逊相关系数均为1, 说明两个重复样品相关性强。
2.5 差异化合物分析结果
由表1可知, 五粮液和五粮醇的差异显著化合物数目是448个, 其中431种化合物在五粮液中的含量显著高于五粮醇, 17种化合物的含量显著低于五粮醇; 五粮液和五粮春的差异显著化合物数目是173个, 其中160种化合物在五粮液中的含量显著高于五粮春, 13种化合物的含量显著低于五粮春; 五粮液和五粮特曲的差异显著化合物数目是531个, 其中523种化合物在五粮液中的含量显著高于五粮特曲, 8种化合物的含量显著低于五粮特曲; 五粮液和五粮头曲的差异显著化合物数目是508个, 其中483种化合物在五粮液中的含量显著高于五粮头曲, 25种化合物的含量显著低于五粮头曲, 五粮液中差异显著化合物数目显著高于五粮醇、五粮春、五粮特曲和五粮头曲。
3 讨论与结论
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基于非靶向代谢组学技术解析五粮液、五粮醇、五粮春、五粮特曲和五粮头曲5个系列白酒中挥发性组分特征及差异, 结果显示5款白酒共检出900种化合物, 不同五粮液白酒检出化合物的数量和组成存在差别。PCA结果显示五粮液和五粮春两款白酒在第1主成分上明显区别于五粮醇、五粮特曲和五粮头曲3款白酒, 且五粮液与其他4款白酒在主成分二维分布图中距离均非常明显, 具有很好的区分特性。五粮液白酒在所有类别化合物中含量均比较显著, 其次是五粮春白酒。由样品聚类结果可看出5款白酒的品质存在显著性差异, 五粮液和五粮春在多数化合物中均存在含量优势。五粮液中差异显著化合物数目显著高于五粮醇、五粮春、五粮特曲和五粮头曲。
科学解析并阐明白酒中微量成分的组成与作用并加以控制是白酒现代化的重要任务, 将为丰富白酒酿造理论, 实现白酒品质提升奠定基础。白酒中的微量成分物质不仅赋予了白酒重要的特征风味与品质, 同时也让白酒具有一定的功能, 因此关于白酒微量成分的深入研究将为中国白酒风味与健康双导向酿造提供重要的技术保证, 提升白酒健康的科学内涵, 对于白酒品质与安全的提升, 以及传统白酒酿造产业的技术升级都具有重要的意义。“健康化”将是中国白酒的发展方向, 深入研究中国白酒中各种功能因子, 不断开发各种健康白酒将有助于消除消费者对白酒的误解。正确认识中国白酒, 并对中国白酒树立正确的价值观和消费观, 这不仅是中国白酒行业进一步发展的契机, 也会是提升我们民族品牌在国际上竞争力的有利时机, 引导广大消费者正确认识白酒, 并对白酒树立正确的价值观和消费观, 对于促进白酒行业进一步发展, 提升我们的民族品牌在国际上的竞争力非常重要[30]。
基金
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  • 中国轻工业浓香型白酒固态发酵重点实验室开放基金项目(2021JJ007)
文献
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2025年第16卷第4期
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doi: 10.19812/j.cnki.jfsq11-5956/ts.20241125001
  • 接收时间：2024-11-25
  • 首发时间：2025-07-21
  • 出版时间：2025-02-25
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  • 收稿日期：2024-11-25
基金
中国轻工业浓香型白酒固态发酵重点实验室开放基金项目(2021JJ007)
作者信息
    1.北京市营养源研究所有限公司, 北京 100069
    2.宜宾五浪液股份有限公司, 宜宾 644000
    3.北京城市学院生物医药学部, 北京 100083

通讯作者:

* 周琪乐(1986—), 男, 博士, 副研究员, 主要研究方向为食品功能因子和中药药效物质研究。E-mail:
崔亚娟(1979—), 女, 博士, 研究员, 主要研究方向为食物营养分析检测及应用研究。E-mail:
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2种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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