Article(id=1321170176201044211, tenantId=1146029695717560320, journalId=1260987677001138203, issueId=1321170163563614210, articleNumber=null, orderNo=null, doi=10.13386/j.issn1002-0306.2025020063, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1739289600000, receivedDateStr=2025-02-12, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1792921116161, onlineDateStr=2026-10-25, pubDate=1769875200000, pubDateStr=2026-02-01, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1792921116161, onlineIssueDateStr=2026-10-25, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1792921116161, creator=13701087609, updateTime=1792921116161, updator=13701087609, issue=Issue{id=1321170163563614210, tenantId=1146029695717560320, journalId=1260987677001138203, year='2026', volume='47', issue='3', pageStart='1', pageEnd='497', issueExtLink='null', onlineDate='null', pubDate='1769875200000', pubDateStr='2026-02-01', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1792921113148, creator='13701087609', updateTime=1792921113148, updator='13701087609', preIssue=null, nextIssue=null, articleTotal=null, ext=null, issueFiles=null, downloadFileDto=null}, startPage=1, endPage=11, ext={EN=ArticleExt(id=1321170176414953716, articleId=1321170176201044211, tenantId=1146029695717560320, journalId=1260987677001138203, language=EN, title=Widely Targeted Metabolomics Analysis of Effects of Different Honey Treatment Processing Methods on Non-volatile Compounds in Yunnan Arabica Coffee, columnId=1261336279195726298, journalTitle=Science and Technology of Food Industry, columnName=Papers Invited by Guest Editors, runingTitle=null, highlight=null, articleAbstract=

To investigate the metabolic differences in non-volatile compounds across honey processing methods, UPLC-MS/MS-based widely targeted metabolomics was employed to analyze green coffee beans systematically under different mucilage removal rates (CH, 100%; WH, approximately 80%; YH, 40%~50%; RH, 20%~25%; BH, 0%). The results revealed that 1682 metabolites were identified in green coffee beans subjected to five mucilage removal rate treatments; multivariate statistical analysis demonstrated that the removal rate significantly altered the metabolic profiles of non-volatile compounds. A total of 192 differential metabolites were screened using VIP>1, log2|FC|>1, and P<0.05 as thresholds. Among them, amino acids and their derivatives, organic acids, phenolic acids, and saccharides emerged as the most dominant metabolite categories, playing pivotal roles in influencing the flavor characteristics of coffee beans. Trend analysis detected 93 non-volatile compounds exhibiting progressively increased relative contents in correlation with decreasing mucilage removal rates. The Kyoto Encyclopedia of Genes and Genomes (KEGG) results indicated that these compounds exhibited a significant enrichment (P<0.05) in inositol phosphate metabolism, pentose and glucuronate interconversions, galactose metabolism, amino sugar and nucleotide sugar metabolism, streptomycin biosynthesis, indole alkaloid biosynthesis, and carbohydrate digestion and absorption. Partial least squares-discriminant analysis (PLS-DA) model showed that ten potential marker compounds predominantly composed of N-acetyl amino acids were distinguished between honey processing methods with varying mucilage removal rates. The findings revealed the regulatory impact of coffee mucilage on the non-volatile compound profiles in coffee beans, providing a theoretical foundation for optimizing coffee flavor attributes through fermentation control strategies.

, authors=Haohao YU1, 2, Xingfei FU1, 2, Xiaofei BI1, 2, Zhongxian LI1, Wenjiang DONG3, Guiping LI1, 2, Yanan LI1, 2, Faguang HU1, 2, *, authorsList=Haohao YU, Xingfei FU, Xiaofei BI, Zhongxian LI, Wenjiang DONG, Guiping LI, Yanan LI, Faguang HU, authorCompany=null, correspAuthors=Faguang HU, authorNote=null, correspAuthorsNote=null, copyrightStatement=Copyright © 2026 Science and Technology of Food Industry. All rights reserved., 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, fund=null), CN=ArticleExt(id=1321170179296440577, articleId=1321170176201044211, tenantId=1146029695717560320, journalId=1260987677001138203, language=CN, title=广泛靶向代谢组学分析不同蜜处理加工方法对小粒咖啡非挥发性化合物的影响, columnId=1321170164360531972, journalTitle=食品工业科技, columnName=特邀主编专栏—咖啡、可可、茶等特色饮料作物加工(客座主编:董文江、许勇泉、付才力), runingTitle=null, highlight=null, articleAbstract=

为探究不同蜜处理加工方法中非挥发性化合物的代谢差异,采用UPLC-MS/MS广泛靶向代谢组学对不同脱胶率下(CH,脱胶率100%;WH,脱胶率约80%;YH,脱胶率为40%~50%;RH,脱胶率为20%~25%;BH,脱胶率0%)的咖啡生豆进行系统分析。结果显示,在5种不同脱胶率处理的咖啡生豆中共鉴定到1682种代谢物,多元统计分析表明脱胶率明显改变了非挥发性化合物的代谢轮廓。以VIP>1,log2|FC|>1和P<0.05为阈值,筛选得到192种差异代谢物,其中氨基酸及其衍生物、有机酸、酚酸以及糖类是最主要的代谢物类别,在影响咖啡豆风味特性方面起着重要的作用。通过趋势分析,鉴定到93种非挥发性化合物的相对含量随脱胶率的降低而持续增加。京都基因与基因组百科全书(KEGG)结果显示,这些化合物在肌醇磷酸代谢、戊糖与葡萄糖酸的相互转化、半乳糖代谢、氨基糖和核苷酸糖代谢、链霉素的生物合成、吲哚生物碱生物合成以及碳水化合物消化吸收中显著富集(P<0.05)。根据最小二乘判别分析(PLS-DA)模型,确定了以N-乙酰氨基酸为主的10种潜在标志性化合物以区分不同脱胶率的蜜处理方法。研究结果揭示了咖啡果胶对咖啡豆的非挥发性化合物的影响,为通过发酵控制提升咖啡风味特性提供了理论参考。

, authors=喻好好1, 2, 付兴飞1, 2, 毕晓菲1, 2, 李忠贤1, 董文江3, 李贵平1, 2, 李亚男1, 2, 胡发广1, 2, *, authorsList=喻好好, 付兴飞, 毕晓菲, 李忠贤, 董文江, 李贵平, 李亚男, 胡发广, authorCompany=null, correspAuthors=胡发广, authorNote=

喻好好(1998−)(ORCID:0009−0003−7802−1557),男,硕士,助理研究员,研究方向:咖啡生理学与生物化学,E-mail:

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胡发广(1979−),男,本科,研究员,研究方向:咖啡关键技术研究与应用推广,E-mail:。
, copyrightStatement=版权所有 © 2026《食品工业科技》编辑部, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=czuMHu64ZIWGf2L9ciELSQ==, magXml=1XL2FFo0kHjrKkzpLo3mcA==, pdfUrl=null, pdf=305RqFkaERhJvJ6I0kWDHg==, pdfFileSize=3576195, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=8y4VDm4uQtZOC55E0/fm8Q==, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=M4++7Fm6hOHNLglKiWOf8g==, mapNumber=null, fund=null)}, authors=[Author(id=1321170179749425420, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1321170176201044211, orderNo=0, firstName=null, middleName=null, lastName=null, nameCn=null, orcid=null, stid=null, country=null, authorPic=null, dead=0, email=y550570@163.com, emailSecond=null, emailThird=null, correspondingAuthor=0, authorType=1, ext={EN=AuthorExt(id=1321170179824922895, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1321170176201044211, authorId=1321170179749425420, language=EN, stringName=Haohao YU, firstName=Haohao, middleName=null, lastName=YU, prefix=null, suffix=null, authorComment=null, nameInitials=null, affiliation=null, department=null, xref=1, 2, address=1Tropical and Subtropical Cash Crops Institute, Yunnan Academy of Agricultural Sciences, Baoshan 678000, China
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Journal of Agricultural and Food Chemistry, 2001, 49(11): 5556−5559., articleTitle=null, refAbstract=null), Reference(id=1321170191745134976, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1321170176201044211, doi=null, pmid=null, pmcid=null, year=null, volume=null, issue=null, pageStart=null, pageEnd=null, url=null, language=null, rfNumber=41, rfOrder=46, authorNames=null, journalName=null, refType=null, unstructuredReference=YILMAZ C, GÖKMEN V. Neuroactive compounds in foods: Occurrence, mechanism and potential health effects[J]. 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注:(a)代谢物一级分类饼图;(b)主成分分析图;(c)层次聚类热图。

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注:(a)CH vs. WH;(b)CH vs. YH;(c)CH vs. RH;(d)CH vs. BH。

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注:(a)CH vs. WH;(b)CH vs. YH;(c)CH vs. RH;(d)CH vs. BH。

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注:(a)韦恩图;(b)峰面积图。

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注:(a)氨基酸及其衍生物;(b)酚酸;(c)有机酸;(d)糖类。

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注:(a)差异代谢物趋势分析;(b)目标趋势谱的KEGG分析。

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Classification of differential metabolites in different comparison groups

, figureFileSmall=null, figureFileBig=null, tableContent=
一级分类物质CH vs. WHCH vs. YHCH vs. RHCH vs. BH所有差异
代谢物
氨基酸及其
衍生物
2022385876
酚酸10581423
有机酸4051720
糖0341417
生物碱227612
黄酮02248
萜类化合物11768
核苷酸及其
衍生物
31337
脂质01146
木脂素和
香豆素
01124
其它3221011
总计434078138192
), ArticleFig(id=1321170184732258638, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1321170176201044211, language=CN, label=表1, caption=

不同对比组中差异代谢物的分类

, figureFileSmall=null, figureFileBig=null, tableContent=
一级分类物质CH vs. WHCH vs. YHCH vs. RHCH vs. BH所有差异
代谢物
氨基酸及其
衍生物
2022385876
酚酸10581423
有机酸4051720
糖0341417
生物碱227612
黄酮02248
萜类化合物11768
核苷酸及其
衍生物
31337
脂质01146
木脂素和
香豆素
01124
其它3221011
总计434078138192
), ArticleFig(id=1321170184816144719, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1321170176201044211, language=EN, label=Table 2, caption=

Top 10 metabolites with the highest VIP score in the target trend profile

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编号物质物质一级分类离子模式VIP得分
mws0520N-乙酰-L-酪氨酸氨基酸及其衍生物[M+H]+2.94
MWSslk120N-乙酰基-DL-苯丙氨酸氨基酸及其衍生物[M+H]+2.84
Yamp0023431-[2-(呋喃-2-基)-2-氧代乙基]哌啶-2-one生物碱[M+H]+2.75
MW0107715L-亮氨酸-L-半胱氨酸-L-精氨酸氨基酸及其衍生物[M+H]+2.56
Zmzn000079D-赤藓糖-4-磷酸糖类[M-H]−2.52
MW0153675甲硫氨酰-苏氨酰-谷氨酸氨基酸及其衍生物[M+H]+2.51
MW0105683L-精氨酰-L-缬氨酰-L-亮氨酸氨基酸及其衍生物[M+H]+2.46
pme0075N-乙酰-L-谷氨酸氨基酸及其衍生物[M-H]−2.46
pme0253N-乙酰-L-亮氨酸氨基酸及其衍生物[M+H]+2.43
Wayn000341N-(1-脱氧-1-果糖基)天冬酰胺氨基酸及其衍生物[M-H]−2.42
), ArticleFig(id=1321170184887447888, tenantId=1146029695717560320, journalId=1260987677001138203, articleId=1321170176201044211, language=CN, label=表2, caption=

目标趋势谱中VIP值最高的10个代谢物

, figureFileSmall=null, figureFileBig=null, tableContent=
编号物质物质一级分类离子模式VIP得分
mws0520N-乙酰-L-酪氨酸氨基酸及其衍生物[M+H]+2.94
MWSslk120N-乙酰基-DL-苯丙氨酸氨基酸及其衍生物[M+H]+2.84
Yamp0023431-[2-(呋喃-2-基)-2-氧代乙基]哌啶-2-one生物碱[M+H]+2.75
MW0107715L-亮氨酸-L-半胱氨酸-L-精氨酸氨基酸及其衍生物[M+H]+2.56
Zmzn000079D-赤藓糖-4-磷酸糖类[M-H]−2.52
MW0153675甲硫氨酰-苏氨酰-谷氨酸氨基酸及其衍生物[M+H]+2.51
MW0105683L-精氨酰-L-缬氨酰-L-亮氨酸氨基酸及其衍生物[M+H]+2.46
pme0075N-乙酰-L-谷氨酸氨基酸及其衍生物[M-H]−2.46
pme0253N-乙酰-L-亮氨酸氨基酸及其衍生物[M+H]+2.43
Wayn000341N-(1-脱氧-1-果糖基)天冬酰胺氨基酸及其衍生物[M-H]−2.42
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广泛靶向代谢组学分析不同蜜处理加工方法对小粒咖啡非挥发性化合物的影响
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喻好好 1, 2 , 付兴飞 1, 2 , 毕晓菲 1, 2 , 李忠贤 1 , 董文江 3 , 李贵平 1, 2 , 李亚男 1, 2 , 胡发广 1, 2, *
食品工业科技 | 特邀主编专栏—咖啡、可可、茶等特色饮料作物加工(客座主编:董文江、许勇泉、付才力) 2026,47(3): 1-11
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食品工业科技 |特邀主编专栏—咖啡、可可、茶等特色饮料作物加工(客座主编:董文江、许勇泉、付才力) 2026 , 47 (3) : 1 -11
广泛靶向代谢组学分析不同蜜处理加工方法对小粒咖啡非挥发性化合物的影响
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喻好好1, 2 , 付兴飞1, 2, 毕晓菲1, 2, 李忠贤1, 董文江3, 李贵平1, 2, 李亚男1, 2, 胡发广1, 2, *
作者信息
  • 1云南省农业科学院热带亚热带经济作物研究所,云南保山 678000
  • 2云南省咖啡重点实验室,云南保山 678000
  • 3中国热带农业科学院香料饮料研究所,海南万宁 571533
通讯作者:
胡发广(1979−),男,本科,研究员,研究方向:咖啡关键技术研究与应用推广,E-mail:。
作者简介:

喻好好(1998−)(ORCID:0009−0003−7802−1557),男,硕士,助理研究员,研究方向:咖啡生理学与生物化学,E-mail:

Widely Targeted Metabolomics Analysis of Effects of Different Honey Treatment Processing Methods on Non-volatile Compounds in Yunnan Arabica Coffee
Haohao YU1, 2 , Xingfei FU1, 2, Xiaofei BI1, 2, Zhongxian LI1, Wenjiang DONG3, Guiping LI1, 2, Yanan LI1, 2, Faguang HU1, 2, *
Affiliations
  • 1Tropical and Subtropical Cash Crops Institute, Yunnan Academy of Agricultural Sciences, Baoshan 678000, China
  • 2Yunnan Key Laboratory of Coffee, Baoshan 678000, China
  • 3Spice and Beverage Research Institute, Chinese Academy of Tropical Agricultural Sciences, Wanning 571533, China
出版时间: 2026-02-01 doi: 10.13386/j.issn1002-0306.2025020063
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为探究不同蜜处理加工方法中非挥发性化合物的代谢差异,采用UPLC-MS/MS广泛靶向代谢组学对不同脱胶率下(CH,脱胶率100%;WH,脱胶率约80%;YH,脱胶率为40%~50%;RH,脱胶率为20%~25%;BH,脱胶率0%)的咖啡生豆进行系统分析。结果显示,在5种不同脱胶率处理的咖啡生豆中共鉴定到1682种代谢物,多元统计分析表明脱胶率明显改变了非挥发性化合物的代谢轮廓。以VIP>1,log2|FC|>1和P<0.05为阈值,筛选得到192种差异代谢物,其中氨基酸及其衍生物、有机酸、酚酸以及糖类是最主要的代谢物类别,在影响咖啡豆风味特性方面起着重要的作用。通过趋势分析,鉴定到93种非挥发性化合物的相对含量随脱胶率的降低而持续增加。京都基因与基因组百科全书(KEGG)结果显示,这些化合物在肌醇磷酸代谢、戊糖与葡萄糖酸的相互转化、半乳糖代谢、氨基糖和核苷酸糖代谢、链霉素的生物合成、吲哚生物碱生物合成以及碳水化合物消化吸收中显著富集(P<0.05)。根据最小二乘判别分析(PLS-DA)模型,确定了以N-乙酰氨基酸为主的10种潜在标志性化合物以区分不同脱胶率的蜜处理方法。研究结果揭示了咖啡果胶对咖啡豆的非挥发性化合物的影响,为通过发酵控制提升咖啡风味特性提供了理论参考。

广泛靶向代谢组学  /  蜜处理法  /  云南小粒咖啡  /  非挥发性化合物

To investigate the metabolic differences in non-volatile compounds across honey processing methods, UPLC-MS/MS-based widely targeted metabolomics was employed to analyze green coffee beans systematically under different mucilage removal rates (CH, 100%; WH, approximately 80%; YH, 40%~50%; RH, 20%~25%; BH, 0%). The results revealed that 1682 metabolites were identified in green coffee beans subjected to five mucilage removal rate treatments; multivariate statistical analysis demonstrated that the removal rate significantly altered the metabolic profiles of non-volatile compounds. A total of 192 differential metabolites were screened using VIP>1, log2|FC|>1, and P<0.05 as thresholds. Among them, amino acids and their derivatives, organic acids, phenolic acids, and saccharides emerged as the most dominant metabolite categories, playing pivotal roles in influencing the flavor characteristics of coffee beans. Trend analysis detected 93 non-volatile compounds exhibiting progressively increased relative contents in correlation with decreasing mucilage removal rates. The Kyoto Encyclopedia of Genes and Genomes (KEGG) results indicated that these compounds exhibited a significant enrichment (P<0.05) in inositol phosphate metabolism, pentose and glucuronate interconversions, galactose metabolism, amino sugar and nucleotide sugar metabolism, streptomycin biosynthesis, indole alkaloid biosynthesis, and carbohydrate digestion and absorption. Partial least squares-discriminant analysis (PLS-DA) model showed that ten potential marker compounds predominantly composed of N-acetyl amino acids were distinguished between honey processing methods with varying mucilage removal rates. The findings revealed the regulatory impact of coffee mucilage on the non-volatile compound profiles in coffee beans, providing a theoretical foundation for optimizing coffee flavor attributes through fermentation control strategies.

widely targeted metabolomics  /  honey processing method  /  Yunnan Arabica coffee  /  non-volatile compounds
喻好好, 付兴飞, 毕晓菲, 李忠贤, 董文江, 李贵平, 李亚男, 胡发广. 广泛靶向代谢组学分析不同蜜处理加工方法对小粒咖啡非挥发性化合物的影响. 食品工业科技, 2026 , 47 (3) : 1 -11 . DOI: 10.13386/j.issn1002-0306.2025020063
Haohao YU, Xingfei FU, Xiaofei BI, Zhongxian LI, Wenjiang DONG, Guiping LI, Yanan LI, Faguang HU. Widely Targeted Metabolomics Analysis of Effects of Different Honey Treatment Processing Methods on Non-volatile Compounds in Yunnan Arabica Coffee[J]. Science and Technology of Food Industry, 2026 , 47 (3) : 1 -11 . DOI: 10.13386/j.issn1002-0306.2025020063
咖啡因其独特的风味而备受消费者欢迎,与茶、可可并列为世界三大饮料。在中国,云南省是最大的咖啡产区,其种植面积、产量和产值均占据全国的98%以上,具有独一无二的产业发展优势[1]。2022年,中国咖啡消费量28.80万吨,同比增长14.29%,为全球增速的31.07倍[2]。随着国内咖啡热潮的兴起,消费者对咖啡风味质量的要求也不断增加。因此,为满足日益成熟的消费者需求,通过品种[3−4]、栽培管理[5]、初加工方式[6−7]等因素改善咖啡豆的风味质量成为了咖啡生产上的重大挑战。
咖啡风味质量的关键因素之一是通过不同初加工方法获得用于烘焙的咖啡生豆[8]。据报道,咖啡生豆的成分非常复杂,包含1000多种具有不同化学性质的化合物[9],例如,碳水化合物、脂质、含氮化合物(生物碱、氨基酸)等是咖啡生豆中重要的风味前体物质[10]。这些物质在烘焙过程中通过美拉德反应、焦糖化反应、脱水反应、脱碳反应等化学过程分解形成烘焙豆中重要的风味活性物质,为咖啡带来果酸、苦涩、甜感等特征风味[11]。此外,咖啡生豆中还含有许多生物活性物质,例如绿原酸、咖啡因、葫芦巴碱、萜类化合物等。这些物质不仅与人体健康密切相关,还具有一定的风味贡献[12]。蜜处理加工方法是一种新型初加工技术,能同时表现出水洗咖啡和日晒咖啡的风味特性。在这一工艺中,咖啡经过脱皮后保留果胶,并在发酵过程中被自然干燥[13]。果胶在发酵过程中形成的风味前体物质可能会渗入咖啡生豆中,改变其物质组成和风味特性。Aswathi等[14]研究表明,蜜处理加工方法会增加中粒咖啡中碳水化合物、有机酸和次生代谢物的含量。Zhai等[15]研究表明,蜜处理加工方法能增强小粒咖啡烘焙豆中焦糖、烘焙和黄油的风味。随着咖啡工业的不断发展,蜜处理加工方法根据脱胶量的不同已衍生出白蜜、黄蜜、红蜜、黑蜜等精品微批次处理法。这些处理法通过控制果胶发酵过程,提升产品质量,增加经济价值。然而,这些精品微批次的初加工方法引起的咖啡风味及活性物质的差异是未知的,有待进一步研究。
基于超高效液相-电喷雾-三重四极杆线性离子阱-串级质谱(ultra performance liquid chromatography-electrospray ionization-quadrupole-linear ion trap-tandem mass spectrometry,UPLC-ESI-Q TRAP-MS/MS)的广泛靶向代谢组学具有高通量、高灵敏度、高覆盖度的技术优势,已被广泛用于食品科学领域的化合物定性、定量、鉴定、分类等方面[16]。目前,利用代谢组进行咖啡初加工研究的报道越来越多,例如,不同初加工方法造成的风味差异[9],或使用酵母或真菌进行咖啡豆发酵[17]。然而,对于单一初加工方法的发酵控制却鲜见报道。本研究以5种不同脱胶率的咖啡生豆为研究对象,采用代谢组学方法全面分析不同蜜处理加工方法对咖啡生豆中非挥发性物质的影响,挖掘其特征成分及代谢通路,以期阐明主要差异代谢物的风味特性。研究结果有助于进一步理解咖啡果胶发酵过程中的质量控制,并为进一步提升咖啡风味特征提供理论参考。
供试品种为小粒咖啡(Coffea arabica L.)栽培品种卡蒂姆(Catimor) 2023年3月采集于中国云南省保山市潞江镇新寨村(25°1'35"N,98°49'51"E,海拔1200 m);HPLC级溶剂(甲醇、乙腈、乙醇、甲酸) 美国Merck集团;标准品(HPLC级溶剂) 美国Sigma-Aldrich集团。
AFJG-2000咖啡脱皮机 云南澳福实业有限公司;AM-CF800咖啡脱胶机 武汉艾克美农业科技有限公司;DRC-1X咖啡脱壳机 巴西Pinhalense公司;Forma995型超低温冰箱 美国Thermo Fisher Scientific公司;Scientz-100F冷冻干燥机 宁波新芝冻干设备有限公司;MM400研磨仪 德国Retsch公司;MS105DM电子天平 瑞士METTLER TOLEDO集团;5424R离心机 德国Eppendorf公司;ExionLC™ AD超高效液相色谱、AppliedBiosystems 4500 Q TRAP串联质谱 美国SCIEX公司。
预处理:将收集的咖啡鲜果仔细分类,剔除未成熟、过熟和有缺陷的鲜果,获得颜色一致、大小均匀的鲜果作为待测样品。为确定单次脱胶量,使用脱皮机将咖啡鲜果去皮,将得到的咖啡豆进行称重(认为此时未脱胶),重量记为W1。然后,使用脱胶机将咖啡豆完全机械脱胶后称重(认为此时果胶已完全去除),重量记为W2。单次脱胶量的计算公式如下:
$ \mathrm{单}\mathrm{次}\mathrm{脱}\mathrm{胶}\mathrm{量}=(\mathrm{W}_1-\mathrm{W}_2)\times\mathrm{R} $
式中,R为单次脱胶率(%),通过调节机械脱胶机的松紧度控制在20%~25%之间(目前生产上常用的咖啡脱胶机精度有限,只能达到范围值)。
样品制备:将样品分成5份,每份重10 kg,分别用于5种不同脱胶量的五种处理。根据预实验得到的单次脱胶量,按照脱胶次数的增加来达到不同处理的脱胶量,并通过称重进行验证。脱胶后将咖啡豆静置40 min,然后戴上无菌手套手动搅拌,确保果胶均匀分布在每颗咖啡豆上。随后,将咖啡豆置于晾晒架上自然干燥(无雨水环境),直至水分含量达到11.0±1.0 g/DW后结束干燥过程(一般来说,脱胶率越低的处理,需要更长的干燥时间)。最后,使用脱壳机去除外壳,获得咖啡生豆,以便进一步分析。
根据脱胶量的差异区分五种处理:CH,水洗法完全去除果胶,即水洗处理;WH,脱胶3次,脱胶率约为80%,即白蜜处理;YH,脱胶两次,脱胶率为40%~50%,即黄蜜处理;RH,脱胶1次,脱胶率为20%~25%,即红蜜处理;BH,不脱胶,即黑蜜处理,这也是咖啡生产上传统的蜜处理方法。所有样品设置3个生物学重复,将CH、WH、YH、RH、BH共5组样品进行广泛靶向代谢组检测。
取出样品置于冷冻干燥机中真空冷冻干燥,随后使用研磨仪研磨(30 Hz,90 s)至粉末状。称取50 mg样本粉末,加入1200 μL预冷的70%甲醇水内标溶液(−20 ℃,2-氯苯丙氨酸,1 PPM)后涡旋(每30 min涡旋1次,每次持续30 s,持续6次)和离心(12000 r/min,3 min),吸取上清液后使用微孔滤膜(0.22 μm)过滤样品,保存于进样瓶中,用于UPLC-MS/MS分析。
色谱柱:Agilent SB-C18 1.8 µm,2.1 mm×100 mm;流动相:A相为超纯水(加入0.1%的甲酸),B相为乙腈(加入0.1%的甲酸);洗脱梯度:0.00 min时B相比例为5%,9.00 min内B相比例线性增加到95%并维持1 min,10.00~11.10 min时B相比例降为5%并维持至14 min;流速、柱温和进样量分别设定为0.35 mL/min、40 ℃和2 µL。
电喷雾离子源(ESI)设置为:温度550 ℃;离子喷雾电压(IS)为5500 V(正离子模式)/−4500 V(负离子模式);离子源气体(GSⅠ)为50 psi;气体Ⅱ(GSⅡ)为60 psi;气帘气(CUR)为25 psi,碰撞诱导电离参数设置为高。三重四极杆(QQQ)扫描使用多反应监测(MRM)模式,碰撞气体(氮气)设置为中等,根据优化的去簇电压(DP)和碰撞能(CE)扫描检测各个MRM离子对。
基于迈维代谢自建数据库(metware database)对二级谱信息进行物质定性并去除同位素信号与重复信号。利用三重四极杆质谱(QQQ TRAP-MS/MS)的多反应监测(MRM)模式进行代谢物定量。获得不同处理的代谢物质谱分析数据后,对所有物质色谱峰进行峰面积积分,并对同一代谢物在不同样本中的质谱出峰进行积分校正。
使用主成分分析(principal component analysis,PCA)和层次聚类分析(hierarchical clustering analysis,HCA)评估样本质量与重现性。通过正交偏最小二乘法判别分析(orthogonal partial least squares-discriminant analysis,OPLS-DA)确定两组间数据的分离性,并获得VIP值(variable importance in projection)。将VIP>1,log2|FC|>1和P<0.05作为筛选条件区分差异代谢物(differential metabolites,DMs)。使用STEM(版本1.3.11)进行代谢物趋势分析(trend analysis)。基于KEGG数据库(http://www.kegg.jp)注释差异代谢物,获得显著变化的代谢通路。
使用Excel 2019软件进行数据整理和分析。利用Metware Cloud云平台(https://cloud.metware.cn/)和Prism Graphpad软件(版本8.0.2)进行图表制作。通过Adobe Illustrator 2019软件进行图形布局排版。
通过对五种蜜处理加工方法获得的咖啡生豆进行广泛靶向代谢组分析,共鉴定到1682个代谢物,包括366种氨基酸及其衍生物(21.76%)、254种酚酸(15.10%)、212种其它类(12.60%,包括82种糖和19种维生素等)、184种脂质(10.94%)、159种生物碱(9.45%)、118种黄酮(7.02%)、109种有机酸(6.48%)、106种萜类(6.30%)等(图1a)。其中,许多代谢物是烘焙过程中产生的挥发性化合物的风味前体物质,也是咖啡中具有重要营养价值和保健功能的次生代谢物[18]。
通过主成分分析(PCA)研究五种处理的非挥发性代谢物轮廓差异。如图1b所示,PCA分数图显示了组内3个重复的聚集,并通过PC1(23.33%)和PC2(14.09%)进行区分。根据图中结果,CH、WH、YH三个处理组间分离度不明显,其二维分布均位于PC1左侧。RH与BH的二维分布则位于PC1右侧,与其余三个处理间存在明显的分离。而RH与BH间主要通过PC2分离,其置信区间存在一定的交集。层次聚类分析(HCA)显示(图1c),组内重复性高证实了代谢组数据的可靠性(重现性良好),而不同处理样品间明显分离表明蜜处理加工方法明显改变了代谢物轮廓组成。正交偏最小二乘法判别分析(OPLS-DA)是一种有监督的多元统计方法,适用于分离判别并放大两组代谢数据间的类别差异并最大程度地去除与类别分离无关的信息[19]。通过交叉验证确定最佳的正交组分数(R2X、R2Y、Q2Y),可以确保模型的预测性能。其中,R2X和R2Y分别表示模型中X和Y矩阵的解释率,Q2Y表示模型的预测能力。其中,R2Y和Q2Y越接近1,模型越稳定。结果表明,CH vs. WH(图2a)、CH vs. YH(图2b)、CH vs. RH(图2c)、CH vs. BH(图2d)这4个不同脱胶量的对比组中,R2Y分别为0.996、1、1、1,Q2Y分别为0.730、0.858、0.928、0.961。总的来说,R2Y均超过了0.99,而Q2Y在0.73~0.961之间,具有良好的数据分离度与模型可靠性。此外,通过OPLS-DA模型得出的VIP值可用于筛选差异代谢物。
为明确不同蜜处理加工方法的咖啡生豆中代谢物的显著差异,根据VIP>1,P<0.05和log2|FC|>1的筛选条件鉴定差异代谢物。结果显示,在CH vs. WH组中(图3a)鉴定出43种差异代谢物(23种上调代谢物和20种下调代谢物),包括20种氨基酸及其衍生物、10种酚酸和4种有机酸等(表1)。在CH vs. YH中(图3b),共鉴定出40种差异代谢物(24种上调代谢物和16种下调代谢物),包括22种氨基酸及其衍生物、5种酚酸和3种糖等(表1)。在这两个对比组中,log2|FC|>3的差异代谢物均为氨基酸及其衍生物,分别有8种和6种。在CH vs. RH组中(图3c),共鉴定出78种差异代谢物,包括51种上调代谢物和27种下调代谢物。其中,氨基酸及其衍生物最多(38种),其次为酚酸(8种)、萜类化合物(7种)、生物碱(7种)、有机酸(5种)等(表1)。有13种代谢物的log2|FC|>3,包括8种氨基酸及其衍生物、4种三萜和1种生物碱。在CH vs. BH组中(图3d),鉴定到138种差异代谢物(120种上调代谢物和18种下调代谢物),主要包括氨基酸及其衍生物(58种)、有机酸(17种)、酚酸(14种)、糖(14种)等(表1)。大量有机酸以及糖类物质表现出显著的差异。这些物质已被证明在咖啡风味质量中有重要的贡献,能为咖啡提供酸味、甜味和果味[20]。火山图显示了不同蜜处理方法中非挥发性化合物的总体差异,差异代谢物的数量随脱胶率的降低而逐渐增加。结合PCA分析,结果表明在脱胶率较低时,脱胶的差异会更显著地影响咖啡豆中的非挥发性化合物。因此,在咖啡生产中,进行黄蜜、红蜜或黑蜜处理时有必要更严格地控制果胶发酵,这或许有助于保持精品咖啡产品的一致性。
总的来说,在4个不同脱胶量对比组中的生豆样品中共鉴定到192种差异代谢物,包括76种氨基酸及其衍生物、23种酚酸、20种有机酸、17种糖、12种生物碱、8种黄酮、8种萜类化合物、7种核苷酸及其衍生物、6种脂质、4种木脂素和香豆素和11种其它物质(表1)。韦恩图显示了不同对比组中各种差异代谢物的交叉与分离(图4a)。其中,鉴定到苏氨酰-异亮氨酰-谷氨酰胺、L-亮氨酰-L-天冬氨酰-L-谷氨酰胺、谷氨酸-异亮氨酸-亮氨酸、咖啡酸乙酯、3,4-二甲氧基肉桂酸、洋川芎内酯K共6种代谢物在不同蜜处理与对照间均表现出显著的差异,这或许可以作为区分蜜处理与水洗处理的潜在生物标志物。峰面积图显示(图4b),随着脱胶率的降低,非挥发性化合物主要呈上调趋势,代谢物丰度逐渐增加。其中,氨基酸及其衍生物是最主要的差异代谢物,其次是以糖类、有机酸为主的初生代谢物以及酚酸为主的次生代谢物。
在不同蜜处理加工方法中,氨基酸及其衍生物是观察到的最丰富的差异非挥发性化合物。氨基酸类化合物在咖啡烘焙中扮演了重要的角色,是美拉德反应中产生吡啶、吡咯等芳香化合物的重要前体物质[21]。这些芳香化合物与坚果、巧克力等风味以及丝滑的口感密切相关[22]。本研究鉴定的差异氨基酸及其衍生物主要包括支链氨基酸和芳香氨基酸,这些氨基酸衍生物大多在蜜处理样品中上调表达(图5a)。例如,组氨酸-脯氨酸-丙氨酸、甘氨酸-亮氨酸-苯丙氨酸、谷氨酸-缬氨酸-异亮氨酸、L-组氨酸-L-缬氨酸-L-缬氨酸和L-异亮氨酸-L-脯氨酸-L-异亮氨酸在WH中明显高于其他处理。相比CH和BH组,甘氨酸-甘氨酸-亮氨酸、L-异亮氨酸-L-脯氨酸-L-精氨酸和L-丙氨酰-L-异亮氨酰-L-苯丙氨酸在YH处理中表现出增加的趋势。这是由于在发酵过程中,一些多肽会在发酵时间久的处理中被分解为更小的肽或氨基酸,从而提高蛋白质的利用率,并影响咖啡风味。N-乙酰-L-酪氨酸、N-乙酰-L-谷氨酸、N-乙酰-L-蛋氨酸、N-乙酰基-DL-苯丙氨酸和N-乙酰基-DL-蛋氨酸五种乙酰化氨基酸衍生物在RH和BH中至少增加了3倍。研究表明,N-乙酰氨基酸是普洱茶发酵过程中的关键上调代谢物,并具有潜在的神经保护活性[23]。因此,在本研究中N-乙酰氨基酸或许可以作为区分果胶量差异的关键代谢物,并且可能增加蜜处理咖啡豆的生物活性。此外,有36种氨基酸及其衍生物在BH组中明显高于其余处理,与Priyadi等[24]的研究结果类似。其中,N-(1-脱氧-1-果糖基)丙氨酸、N-(1-脱氧-1-果糖基)苯丙氨酸、N-(1-脱氧-1-木糖基)缬氨酸、N-(1-脱氧-1-果糖基)亮氨酸、N-(1-脱氧-1-果糖基)天冬酰胺可能会增强美拉德反应,通过与糖类物质发生一系列反应并脱水分解产生吡嗪等挥发性化合物[25−26]。据报道,蜜处理加工方法相比传统水洗处理具有更高的谷氨酸含量[27]。一般来说,发酵过程会增加咖啡豆中氨基酸及其衍生物的含量,这可能是由于微生物发酵分解蛋白质引起的。例如,通过Rhizopus oligosporus发酵处理后的咖啡生豆中天冬氨酸、谷氨酸、丙氨酸、脯氨酸、苯丙氨酸的浓度会显著高于未发酵的咖啡豆[28]。
酚酸是咖啡中重要的生物活性化合物,被报道为对人体健康有益的抗氧化剂[29]。在本研究中,在不同脱胶量的蜜处理加工方法间共检测到23种酚酸类化合物(图5b)。与CH组相比,WH组中发现了10种差异表达的酚酸类化合物。其中,对香豆酸乙酯、咖啡酸苯乙酯和4-羟基苯甲酸乙酯的代谢丰度分别上调了6.69、5.05和4.18倍,这些化合物在脱胶率较高的蜜处理方法中(WH、YH)丰度较高。然而,对于大部分检出的酚类差异代谢物来说,均在脱胶率低的处理中(RH、BH)具有较高的代谢丰度。例如,在RH和BH组中检出的咖啡酸乙酯、3,4-二甲氧基肉桂酸、阿魏酸乙酯的代谢丰度均为CH组的2.5~3.5倍。此外,4-O-没食子酰奎宁酸、异阿魏酸、3-羟基肉桂酸、2-羟基肉桂酸、对香豆酸、α-羟基肉桂酸、香草醇苷、丁香苷、3-(4-羟基苯基)丙酸、2-羟基-3-苯基丙酸、2-甲基苯甲酸这11种酚酸类化合物在BH组中的相对丰度高于其它处理。这些结果表明,果胶发酵能促进酚类物质的合成,从而增加咖啡制品的生物活性。在另一项研究中,低脱胶率的咖啡生豆主要受真菌(主要包括酵母菌和丝状真菌)的影响[30]。有研究表明,这或许是在干燥过程中真菌通过分泌β-葡萄糖苷酶促进酚酸的转化与合成[9]。然而,对于咖啡来说,需要更多的研究来评估这些酚酸类化合物对咖啡风味质量的影响。此外,在不同脱胶量的蜜处理方法中均检出了高水平的绿原酸、咖啡酸等咖啡中重要的酚类化合物。然而这些化合物在各处理中均保持稳定,表明果胶的去除与否并不会对这些咖啡中典型的活性化合物产生影响。
通常来说,酸含量会随着发酵过程而增加,这是由乳酸菌通过乳酸发酵导致的[31]。本研究结果表明,不同脱胶率的蜜处理加工方法中有机酸含量具有较大差异(图5c)。这一现象的原因是微生物会分解果胶中的碳水化合物从而释放出大量的有机酸(碳水化合物代谢中重要的中间体化合物)以及糖类[32−33]。因此,低脱胶率的咖啡豆可能会产生更多的碳代谢产物。在检出的所有20种差异表达的有机酸中,CH vs. BH组中鉴定到了17种,16种为上调表达(表1)。其中,草酸、L-乳酸、DL-3-苯基乳酸的差异倍数最高,分别达到了9.68、5.79、4.77倍。这些有机酸化合物可作为风味前体物质随果胶迁移到种子中,赋予咖啡烘焙豆额外的风味[17]。研究表明,乳酸会显著增加咖啡制品的果酸味,例如“乳酸”“柠檬酸”等风味,这有利于提升咖啡的杯测质量,从而获得更高的杯测分值[34];而草酸被认为与“苦涩”的风味呈正相关[12],这可能会给精品咖啡的风味品质带来影响。此外,在各比较组中均鉴定到了γ-氨基丁酸。γ-氨基丁酸是一种植物中天然存在并具有多种生理功能的生物活性化合物,对人体具有多种健康功效,特别是作为中枢神经系统中的主要抑制性神经递质[35]。研究表明,γ-氨基丁酸与逆境胁迫密切相关,咖啡生豆的自然干燥过程可能会导致其显著积累[36]。
如前所述,咖啡果胶中含有大量的碳水化合物。咖啡内源微生物(主要包括乳酸菌等)通过释放有机酸、降低酸度加速果胶降解,这有助于降低黏性并增加单糖,为咖啡增加甜味和果酸味[14]。在本研究中,共有17种糖类物质在不同处理组间差异表达,其变化趋势与有机酸类似,主要在BH组中被检出(14种)。这些糖类物质主要包括D-葡萄糖、山梨糖、D-果糖、甘露糖、D-半乳糖、D-葡萄糖醛酸、D-半乳糖醛酸等,与CH组相比,分别增加了2.19、2.13、2.70、2.68、2.40、2.90以及2.85倍(表1,图5d)。这些重要的糖类物质属于果胶成分的一部分,可以通过酶促反应降解,并对咖啡的香气以及杯测质量产生积极影响[37]。在烘焙过程中,糖类物质的热降解是形成焦糖化产物的主要原因。同样地,还原糖还与氨基酸通过美拉德反应相互作用,形成吡嗪和吡啶等重要的咖啡芳香族化合物,从而影响咖啡饮料的烘焙香气和坚果感官特性[38]。此前的研究已有报道,不同类型的咖啡加工方法会导致咖啡生豆中的糖含量表现出显著差异。例如,相比水洗处理法以及日晒处理法,蜜处理加工法会保留更高的蔗糖以及其余单糖含量[39]。然而,本研究发现,高糖含量似乎仅在常规的蜜处理法(BH组)中表现出来。在不同脱胶量的其余处理中,均与完全脱胶的CH组无明显差异。
将对照(CH)、白蜜(WH)、黄蜜(YH)、红蜜(RH)、黑蜜(BH)共五个不同脱胶率处理的咖啡生豆进行代谢物变化规律分析。选择所有检出的192个差异代谢物进行趋势分析,共获得10个趋势谱。根据代谢物趋势分析的显著性检验(P<0.05),仅获得1个目标趋势谱(profile9),随着脱胶率的降低而不断上调(图6a)。目标趋势谱(profile9)中包含93种代谢物。其中,氨基酸及其衍生物数量最多(39种),其次为糖类(14种)、有机酸(13种)、酚酸(10种)、生物碱(6种)等。
随后,将profile9中包含的93种代谢物进行KEGG通路富集分析。前20条富集的KEGG通路如图6b所示,显著富集(P<0.05)的通路有7条,分别为肌醇磷酸代谢、戊糖和葡萄糖醛酸的相互转化、半乳糖代谢、氨基糖和核苷酸糖代谢、链霉素的生物合成、吲哚生物碱生物合成以及碳水化合物消化吸收。其中,肌醇磷酸代谢、戊糖和葡萄糖醛酸的相互转化、半乳糖代谢以及氨基糖和核苷酸糖代谢均属于碳水化合物代谢(Carbohydrate metabolism)。这些途径中包含了许多显著变化的糖类物质,例如,磷酸二羟基丙酮、D-半乳糖醛酸、D-葡萄糖醛酸、阿东糖醇、甘露糖、肌醇、木糖醇、D-半乳糖、D-葡萄糖。随着脱胶率的降低,这些糖类物质持续上调积累,在咖啡豆的自然发酵过程中参与碳水化合物代谢。未处理的咖啡果胶具有高度的甲酯化,包含约60%的半乳糖醛酸,这些物质必须在发酵过程中被降解[40]。通过自然发酵处理(包括日晒处理、蜜处理)后的咖啡豆,往往具有更强烈的甜感,这与糖类物质积累密切相关。代谢物引起的风味变化与微生物活动联系紧密,例如咖啡果胶中的糖类物质会通过酵母菌发酵产生挥发性化合物,这有利于咖啡的香气属性[34]。这在另一项研究中已被观察到,酵母在自然发酵后期会提升咖啡的醇香、花香和坚果等香气[30]。链霉素的生物合成以及吲哚生物碱生物合成均属于次生代谢物的生物合成(Biosynthesis of other secondary metabolites)。在本研究中,这主要与色胺的生物合成相关,色氨酸通过色氨酸脱羧酶转化为色胺,并参与吲哚生物碱的生物合成。色胺对人体健康具有许多有益作用,包括抗氧化、在人体内转化为血清素以改善睡眠质量以及维护肠道健康等[41]。此外,在前20条富集的通路中,还鉴定到许多与微生物活动相关的途径,例如碳水化合物消化吸收、群体感应、微生物在不同环境中的新陈代谢、减数分裂-酵母等。这表明微生物对不同脱胶率下的咖啡发酵过程中的代谢物积累具有重要作用。
为了在不同脱胶率的蜜处理方法中鉴定关键的非挥发性化合物,本研究针对CH、WH、YH、RH、BH共5个处理组建立了最小二乘判别分析(partial least squares-discriminant analysis,PLS-DA)模型以确定各代谢物的VIP得分。高VIP得分代表各组间非挥发性化合物的差异越显著,并且更有利于用于组别间的分类。如表2所示,本研究选取了目标趋势谱中VIP得分最高的10种代谢物,分别为N-乙酰-L-酪氨酸、N-乙酰基-DL-苯丙氨酸、1-[2-(呋喃-2-基)-2-氧代乙基]哌啶-2-one、L-亮氨酸-L-半胱氨酸-L-精氨酸、D-赤藓糖-4-磷酸、甲硫氨酰-苏氨酰-谷氨酸、L-精氨酰-L-缬氨酰-L-亮氨酸、N-乙酰-L-谷氨酸、N-乙酰-L-亮氨酸和N-(1-脱氧-1-果糖基)天冬酰胺。这些代谢物随着脱胶率的降低而持续上调,并且可作为区分使用不同果胶量制备咖啡豆的蜜处理方法的潜在标志性化合物。在这10种代谢物中,包括了8种氨基酸及其衍生物、1种生物碱和1种糖类。其中,包含了N-乙酰-L-酪氨酸、N-乙酰基-DL-苯丙氨酸、N-乙酰-L-谷氨酸、N-乙酰-L-亮氨酸共4种N-乙酰氨基酸。如前所述,N-乙酰氨基酸是具有潜在的神经保护活性的生物活性化合物,这可能会增加蜜处理加工后咖啡豆的生物活性[42]。根据PLS-DA模型得出的VIP得分,N-乙酰氨基酸也可作为区分不同果胶量制备咖啡豆的蜜处理方法的潜在标志性化合物。
本研究采用基于UPLC-MS/MS的广泛靶向代谢组学技术分析5种不同脱胶率的蜜处理方法的代谢物组成差异,共获得192种差异非挥发性化合物。其中包括了76种氨基酸及其衍生物、23种酚酸、20种有机酸、17种糖、12种生物碱、8种黄酮、8种萜类化合物、7种核苷酸及其衍生物、6种脂质、4种木脂素和香豆素和11种其它物质。在RH以及BH处理中,脱胶率差异会更显著地影响咖啡豆中的非挥发性化合物。在不同对比组中,氨基酸及其衍生物的差异代谢物数量均为最多,且随着脱胶率的降低而逐渐增加,其次是以糖类、有机酸为主的初生代谢物以及以酚酸为主的次生代谢物。热图显示,这些非挥发性化合物在脱胶率较低的蜜处理方法中会保持更高的丰度。通过烘焙过程中的一系列反应,这些化合物可能给咖啡豆带来更加丰富的“醇香”“果酸”“甜感”等风味特征以增加咖啡豆的附加价值。此外,趋势分析鉴定到93种代谢物随着脱胶率降低而持续上调。这些代谢物主要参与了磷酸肌醇代谢、戊糖与葡萄糖酸的相互转化、半乳糖代谢、氨基糖和核苷酸糖代谢、链霉素的生物合成、吲哚生物碱生物合成等与咖啡豆的风味物质合成以及生物活性密切相关的代谢通路。根据最小二乘判别分析模型,确定了以N-乙酰氨基酸为主的10种潜在标志性化合物以区分不同果胶量的蜜处理方法。以此提出,保留不同的果胶量的蜜处理方法会显著影响咖啡生豆的非挥发性代谢物,且在脱胶率低时更加明显。研究结果为进一步理解咖啡果胶对代谢物的影响提供了理论依据,可为今后咖啡豆的发酵控制以及风味改善等方面提供一定的参考。

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2026年第47卷第3期
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doi: 10.13386/j.issn1002-0306.2025020063
  • 接收时间:2025-02-12
  • 首发时间:2026-10-25
  • 出版时间:2026-02-01
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    1云南省农业科学院热带亚热带经济作物研究所,云南保山 678000
    2云南省咖啡重点实验室,云南保山 678000
    3中国热带农业科学院香料饮料研究所,海南万宁 571533

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胡发广(1979−),男,本科,研究员,研究方向:咖啡关键技术研究与应用推广,E-mail:。
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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
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红菇属 Russula 17 8.13
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