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Article(id=1151881494504026560, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1151881493552394994, articleNumber=null, orderNo=null, doi=10.19812/j.cnki.jfsq11-5956/ts.20250106004, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1736092800000, receivedDateStr=2025-01-06, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1752559549212, onlineDateStr=2025-07-15, pubDate=1748102400000, pubDateStr=2025-05-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752559549212, onlineIssueDateStr=2025-07-15, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752559549212, creator=13701087609, updateTime=1752559549212, updator=13701087609, issue=Issue{id=1151881493552394994, tenantId=1146029695717560320, journalId=1149652044408987649, year='2025', volume='16', issue='10', pageStart='1', pageEnd='324', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752559548986, creator=13701087609, updateTime=1756202008453, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1167159075906265916, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1151881493552394994, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1167159075906265917, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1151881493552394994, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=239, endPage=246, ext={EN=ArticleExt(id=1151923896879510228, articleId=1151881494504026560, tenantId=1146029695717560320, journalId=1149652044408987649, language=EN, title=Screening of risk markers in pork storage process based on high resolution mass spectrometry combined with chemometrics, columnId=1151895321388347923, journalTitle=Journal of Food Safety & Quality, columnName=Food Analysis and Detection, runingTitle=null, highlight=null, articleAbstract=

Objective To screen risk markers in the storage process of pork using ultra performance liquid chromatography-quadrupole-orbitrap high resolution mass spectrometry (UPLC-Q-Orbitrap HRMS) combined with chemometrics. Methods After sample pretreatment, UPLC-Q-Orbitrap HRMS was employed to detect metabolites in pork, obtaining high precision chromatographic and mass spectrometric data. Principal component analysis (PCA) was applied for dimensionality reduction to explore the distribution characteristics among samples. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was then used to screen differential metabolites with a variable importance factor (VIP)>1.0. The statistical significance of the differences was further evaluated using T-tests (P<0.05), and the final key metabolites were confirmed by combining fold change analysis (fold change, FC>2 or FC<0.5). Results PCA revealed that storage temperature significantly influenced metabolite distribution in different pork cuts (loin, belly, and ham), with samples under different temperatures showing clear separation in the principal component space (total variance R2=0.861, predictive ability Q2=0.478). The OPLS-DA model further optimized the separation of intergroup differences (R2=0.811, Q2=0.653), making the impact of storage temperature on metabolite abundance and type more evident. The R2 and Q2 values of the real model were significantly higher than those of the permutation model, confirming the reliability of the analysis. Through screening conditions (VIP>1, P<0.05, FC>2 or FC<0.5) and structural verification, N-acetylputrescine and 6-methylquinoline were identified as risk markers. Conclusion UPLC-Q-Orbitrap HRMS combined with chemometrics effectively screens risk markers in pork during storage.

, correspAuthors=Yun-Fan WANG, 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=Juan-Hua YANG, Yun-Fan WANG, Liang-Hong YUAN, Le XU, Fei-Fan YANG, Jun-Hua WU, Yu-Cong LIANG), CN=ArticleExt(id=1151923915309281795, articleId=1151881494504026560, tenantId=1146029695717560320, journalId=1149652044408987649, language=CN, title=基于高分辨质谱法结合化学计量学筛选猪肉储藏过程中风险标志物, columnId=1151895321958773274, journalTitle=食品安全质量检测学报, columnName=食品分析与检测, runingTitle=null, highlight=null, articleAbstract=

目的 基于超高效液相色谱-四极杆-静电场轨道阱高分辨质谱法(ultra performance liquid chromatography-quadrupole-orbitrap high resolution mass spectrometry, UPLC-Q-Orbitrap HRMS)结合化学计量学筛选猪肉储藏过程中风险标志物。方法 样品经前处理后, 采用UPLC-Q-Orbitrap HRMS对猪肉进行代谢物检测, 获取高精度的色谱和质谱数据, 运用主成分分析(principal component analysis, PCA)进行降维以探索样本间的分布特征, 通过正交偏最小二乘法判别分析(orthogonal partial least squares-discriminant analysis, OPLS-DA)筛选出变量重要性因子(variable importance factor, VIP)值大于1.0的差异代谢物。进一步通过T检验(P<0.05)评估差异的统计显著性, 并结合倍数变化分析(fold change, FC>2或FC<0.5)确认最终的关键代谢物。结果 PCA结果显示储藏温度显著影响猪肉不同部位(里脊、五花肉、后腿肉)的代谢物分布, 不同温度下样品间在主成分空间中分离良好(总方差R2=0.861, 预测能力Q2=0.478)。OPLS-DA模型进一步优化了组间差异的分离(R2=0.811, Q2=0.653), 储藏温度对代谢物丰度和类型的影响更加清晰。真实模型的R2和Q2明显高于置换模型, 验证了分析的可靠性。通过筛选条件(VIP>1, P<0.05, FC>2或FC<0.5), 进一步结合结构确证, 确定了N-乙酰腐胺和6-甲基喹啉为风险标志物。结论 UPLC-Q-Orbitrap HRMS结合化学计量学能够有效筛选出猪肉储藏过程中的风险标志物。

, correspAuthors=王云帆, authorNote=null, correspAuthorsNote=
* 王云帆(1984—), 男, 助理研究员, 主要研究方向为食品质量与安全。E-mail:
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杨娟华(1991—), 女, 硕士, 工程师, 主要研究方向为食品质量与安全。E-mail:

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杨娟华(1991—), 女, 硕士, 工程师, 主要研究方向为食品质量与安全。E-mail:

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杨娟华(1991—), 女, 硕士, 工程师, 主要研究方向为食品质量与安全。E-mail:

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注: A. 里脊; B. 五花; C. 后腿。D0: 原始状态; T-20: 冷冻-20 ℃; QC: 质控; T4: 冷藏4 ℃; T40: 高温储藏40 ℃。

, figureFileSmall=LrnrMBT4ZPm8jH502D/Veg==, figureFileBig=NO8LDRNOq298AMyt/UqpfQ==, tableContent=null), ArticleFig(id=1167158702013424387, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1151881494504026560, language=EN, label=Fig.2, caption=Screening of VIP value (A) and site distribution of differential metabolites (B), figureFileSmall=C6ktrHXYgOFe9/I8zE8lxQ==, figureFileBig=JMa4yoSXNnHxlStjuGA8Ew==, tableContent=null), ArticleFig(id=1167158702059561732, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1151881494504026560, language=CN, label=图2, caption=VIP值筛选(A)与差异代谢物的部位分布(B), figureFileSmall=C6ktrHXYgOFe9/I8zE8lxQ==, figureFileBig=JMa4yoSXNnHxlStjuGA8Ew==, tableContent=null), ArticleFig(id=1167158702109893381, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1151881494504026560, language=EN, label=Fig.3, caption=Analysis of the effects of storage time and temperature on metabolite changes in pork, figureFileSmall=U93hJYWZdCEGXJlTjlnw+w==, figureFileBig=uT5tgLS/ujEUEPZYWcxQwQ==, tableContent=null), ArticleFig(id=1167158702172807942, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1151881494504026560, language=CN, label=图3, caption=储存时间和温度对猪肉代谢物变化的影响分析, figureFileSmall=U93hJYWZdCEGXJlTjlnw+w==, figureFileBig=uT5tgLS/ujEUEPZYWcxQwQ==, tableContent=null), ArticleFig(id=1167158702239916807, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1151881494504026560, language=EN, label=Fig.4, caption=Key differential metabolites and their secondary fragment information, figureFileSmall=ge1nvyFkuX+fHNC/92ymRQ==, figureFileBig=ezCVPXpQDFEVRAQIAJArbQ==, tableContent=null), ArticleFig(id=1167158702294442760, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1151881494504026560, language=CN, label=图4, caption=关键差异代谢物及其二级碎片信息

注: A、B分别为N-乙酰腐胺标准品与实际样品的保留时间对比图和二级质谱碎片信息匹配图; C、D分别为6-甲基喹啉的标准品与实际样品的保留时间对比图和二级质谱碎片信息匹配图。

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Gradient elution program of UPLC-Q-Orbitrap

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运行时间/min 流动相A/% 流动相B/%
0.0 95 5
0.5 95 5
10.0 0 100
12.0 0 100
12.5 95 5
15.0 95 5
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UPLC-Q-Orbitrap梯度洗脱程序

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运行时间/min 流动相A/% 流动相B/%
0.0 95 5
0.5 95 5
10.0 0 100
12.0 0 100
12.5 95 5
15.0 95 5
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Differential metabolites

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代谢物 分子式 相对分子质量 电离模式
异戊胺 C5H13N 88.11258 [M+H]+1
DL-异亮氨酸 C6H13NO2 132.10195 [M+H]+1
甲酰乙内脲 C4H6N2O2 115.05063 [M+H]+1
N-乙酰腐胺 C6H14N2O 131.11766 [M+H]+1
苯乙酮 C8H8O 121.06504 [M+H]+1
6-甲基喹啉 C10H9N 144.08043 [M+H]+1
1,3-丙二胺 C5H14N2 103.12331 [M+H]+1
N,N-二甲基丙烯酰胺 C8H15NO 186.11348 [M+FA-H]-1
N-乙酰基-L-苯丙酸乙酯 C13H17NO3 234.11324 [M-H]-1
), ArticleFig(id=1167158702697095950, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1151881494504026560, language=CN, label=表2, caption=

差异代谢物

, figureFileSmall=null, figureFileBig=null, tableContent=
代谢物 分子式 相对分子质量 电离模式
异戊胺 C5H13N 88.11258 [M+H]+1
DL-异亮氨酸 C6H13NO2 132.10195 [M+H]+1
甲酰乙内脲 C4H6N2O2 115.05063 [M+H]+1
N-乙酰腐胺 C6H14N2O 131.11766 [M+H]+1
苯乙酮 C8H8O 121.06504 [M+H]+1
6-甲基喹啉 C10H9N 144.08043 [M+H]+1
1,3-丙二胺 C5H14N2 103.12331 [M+H]+1
N,N-二甲基丙烯酰胺 C8H15NO 186.11348 [M+FA-H]-1
N-乙酰基-L-苯丙酸乙酯 C13H17NO3 234.11324 [M-H]-1
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基于高分辨质谱法结合化学计量学筛选猪肉储藏过程中风险标志物
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杨娟华 1 , 王云帆 1, * , 袁亮宏 2 , 徐乐 2 , 杨飞帆 2 , 吴俊华 1 , 梁宇骢 1
食品安全质量检测学报 | 食品分析与检测 2025,16(10): 239-246
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食品安全质量检测学报 | 食品分析与检测 2025, 16(10): 239-246
基于高分辨质谱法结合化学计量学筛选猪肉储藏过程中风险标志物
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杨娟华1 , 王云帆1, * , 袁亮宏2, 徐乐2, 杨飞帆2, 吴俊华1, 梁宇骢1
作者信息
  • 1.中国检验检疫科学研究院粤港澳大湾区研究院, 特殊食品安全研究中心, 中山 528437
  • 2.广东省袁记食品集团有限公司, 质量管理中心, 佛山 528315

    • 杨娟华(1991—), 女, 硕士, 工程师, 主要研究方向为食品质量与安全。E-mail:

通讯作者:

* 王云帆(1984—), 男, 助理研究员, 主要研究方向为食品质量与安全。E-mail:
Screening of risk markers in pork storage process based on high resolution mass spectrometry combined with chemometrics
Juan-Hua YANG1 , Yun-Fan WANG1, * , Liang-Hong YUAN2, Le XU2, Fei-Fan YANG2, Jun-Hua WU1, Yu-Cong LIANG1
Affiliations
  • 1. Chinese Academy of Inspection and Quarantine Greater Bay Area, Special Food Safety Research Center, Zhongshan 528437, China
  • 2. Guangdong Yuanji Food Group Co., Ltd., Quality Management Center, Foshan 528315, China
出版时间: 2025-05-25 doi: 10.19812/j.cnki.jfsq11-5956/ts.20250106004
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摘要
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目的 基于超高效液相色谱-四极杆-静电场轨道阱高分辨质谱法(ultra performance liquid chromatography-quadrupole-orbitrap high resolution mass spectrometry, UPLC-Q-Orbitrap HRMS)结合化学计量学筛选猪肉储藏过程中风险标志物。方法 样品经前处理后, 采用UPLC-Q-Orbitrap HRMS对猪肉进行代谢物检测, 获取高精度的色谱和质谱数据, 运用主成分分析(principal component analysis, PCA)进行降维以探索样本间的分布特征, 通过正交偏最小二乘法判别分析(orthogonal partial least squares-discriminant analysis, OPLS-DA)筛选出变量重要性因子(variable importance factor, VIP)值大于1.0的差异代谢物。进一步通过T检验(P<0.05)评估差异的统计显著性, 并结合倍数变化分析(fold change, FC>2或FC<0.5)确认最终的关键代谢物。结果 PCA结果显示储藏温度显著影响猪肉不同部位(里脊、五花肉、后腿肉)的代谢物分布, 不同温度下样品间在主成分空间中分离良好(总方差R2=0.861, 预测能力Q2=0.478)。OPLS-DA模型进一步优化了组间差异的分离(R2=0.811, Q2=0.653), 储藏温度对代谢物丰度和类型的影响更加清晰。真实模型的R2和Q2明显高于置换模型, 验证了分析的可靠性。通过筛选条件(VIP>1, P<0.05, FC>2或FC<0.5), 进一步结合结构确证, 确定了N-乙酰腐胺和6-甲基喹啉为风险标志物。结论 UPLC-Q-Orbitrap HRMS结合化学计量学能够有效筛选出猪肉储藏过程中的风险标志物。

超高效液相色谱-四极杆-静电场轨道阱高分辨质谱法  /  化学计量学  /  猪肉  /  标志物

Objective To screen risk markers in the storage process of pork using ultra performance liquid chromatography-quadrupole-orbitrap high resolution mass spectrometry (UPLC-Q-Orbitrap HRMS) combined with chemometrics. Methods After sample pretreatment, UPLC-Q-Orbitrap HRMS was employed to detect metabolites in pork, obtaining high precision chromatographic and mass spectrometric data. Principal component analysis (PCA) was applied for dimensionality reduction to explore the distribution characteristics among samples. Orthogonal partial least squares-discriminant analysis (OPLS-DA) was then used to screen differential metabolites with a variable importance factor (VIP)>1.0. The statistical significance of the differences was further evaluated using T-tests (P<0.05), and the final key metabolites were confirmed by combining fold change analysis (fold change, FC>2 or FC<0.5). Results PCA revealed that storage temperature significantly influenced metabolite distribution in different pork cuts (loin, belly, and ham), with samples under different temperatures showing clear separation in the principal component space (total variance R2=0.861, predictive ability Q2=0.478). The OPLS-DA model further optimized the separation of intergroup differences (R2=0.811, Q2=0.653), making the impact of storage temperature on metabolite abundance and type more evident. The R2 and Q2 values of the real model were significantly higher than those of the permutation model, confirming the reliability of the analysis. Through screening conditions (VIP>1, P<0.05, FC>2 or FC<0.5) and structural verification, N-acetylputrescine and 6-methylquinoline were identified as risk markers. Conclusion UPLC-Q-Orbitrap HRMS combined with chemometrics effectively screens risk markers in pork during storage.

ultra performance liquid chromatography-quadrupole-orbitrap high resolution mass spectrometry  /  chemometrics  /  pork  /  markers
杨娟华, 王云帆, 袁亮宏, 徐乐, 杨飞帆, 吴俊华, 梁宇骢. 基于高分辨质谱法结合化学计量学筛选猪肉储藏过程中风险标志物. 食品安全质量检测学报, 2025 , 16 (10) : 239 -246 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20250106004
Juan-Hua YANG, Yun-Fan WANG, Liang-Hong YUAN, Le XU, Fei-Fan YANG, Jun-Hua WU, Yu-Cong LIANG. Screening of risk markers in pork storage process based on high resolution mass spectrometry combined with chemometrics[J]. Journal of Food Safety & Quality, 2025 , 16 (10) : 239 -246 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20250106004
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0 引言
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猪肉是人们日常生活中重要的动物性食品, 我国是目前猪肉消费量最大的国家, 生猪产量以及猪肉进口量都排在全球前列[1]。劣变在猪肉屠宰后即开始, 生猪屠宰后细胞发生着活跃的生物学过程, 猪肉含有多种蛋白酶和脂肪酶仍可以继续活动, 分解猪肉中的蛋白质和脂质, 产生多肽、游离氨基酸和脂肪酸等, 导致质量和风味的损失[2-3]。此外, 猪肉在运输、贮藏、加工和消费的过程中, 由于保存条件、保存时间等环境因素以及自身因素等导致其新鲜度不断降低, 甚至腐败变质, 产生有毒有害的化学产物, 安全风险增加[4-5]。
目前, 传统的猪肉新鲜度评价多集中于感官检测[6-7]、理化指标测定[8-10]、微生物学指标检验[11-13]等方法, 然而, 这些方法中的大多数具有明显的缺点, 例如存在主观性强, 检测耗时长, 精度不足。亟需探究关键的劣变风险标志物来评价猪肉的新鲜度, 为猪肉产业链在食品安全管理和风险防控领域提供有效的技术支持和决策指导, 因此, 利用代谢组学分析挖掘高效的风险标志物来快速准确评估猪肉新鲜度具有重要的理论意义与经济价值。
超高效液相色谱-四极杆-静电场轨道阱高分辨质谱法(ultra performance liquid chromatography-quadrupole-orbitrap high resolution mass spectrometry, UPLC-Q-Orbitrap HRMS)可以根据不同的检测需求, 灵活选择色谱分离和质谱分析模式, 从而满足多样化的分析要求, 具有样本预处理简单、数据量丰富、选择性高、灵敏度高等优点, 在食品分析领域应用广泛[14-17]。近年来, 针对海量数据挖掘与处理、分析信号的提取和分析方面, 化学计量学作为一种强大的手段, 解决了食品分析中现代仪器数据多维化、复杂化的难题, 已广泛应用于产地溯源、食品掺假鉴别等相关领域[18‒21]。目前, UPLC-Q-Orbitrap HRMS与化学计量学的结合主要应用在风味变化、真伪鉴别、物种鉴别、药物残留筛查等领域, 例如, WINDARSIH等[22]利用超高效液相色谱-质谱法结合化学计量学检测牛肉丸中的掺假物质, HU等[23]通过基于气相色谱-四极杆-飞行时间质谱法和UPLC-Q-Orbitrap质谱法的代谢组学和化学计量学区分3种商业金枪鱼物种, 刘佳等[24]利用超高效液相色谱-四极杆-飞行时间质谱组合化学计量学方法快速筛查肉中109种药物残留, 而在猪肉储藏过程中评价新鲜度的风险标志物筛选方面鲜见报道。本研究以猪肉为研究对象, 采用UPLC-Q-Orbitrap HRMS对猪肉进行分析, 并结合化学计量学筛选出猪肉在储藏过程中潜在的风险标志物, 为猪肉的质量控制和市场上猪肉储藏条件优化提供相关依据。
1 材料与方法
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1.1 材料与试剂
甲醇、乙腈(色谱纯, 美国Thermo Fisher Scientific公司); 甲酸(色谱纯, 北京百灵威科技有限公司); 一次性注射器(2 mL)及滤膜(美国Welch公司)。
1.2 仪器与设备
Q-Exactive超高效液相色谱-四极杆/静电场轨道阱高分辨率质谱仪(美国Thermo Fisher Scientific公司); ACQUITY UPLC BEH C18色谱柱(2.1 mm×100 mm, 1.7 μm, 美国Waters公司); Milli-R04纯水仪(德国Millipore公司); XP105DR分析天平(精度0.01 mg, 梅特勒-托利多上海有限公司); VM-6涡旋振荡器(北京五洲东方科技发展有限公司)。
1.3 实验方法
1.3.1 样品前处理
猪肉购于猪肉交易市场, 脊膘、五花肉和后腿肉样品在-20 ℃(冷冻)、4 ℃(冷藏)和40 ℃(高温储藏)3种条件下存放5 d, 并与初始状态进行对比分析。差异代谢物的动态变化规律研究中脊膘、五花肉和后腿肉样品在4 ℃(低温)、25 ℃(中温)和40 ℃(高温)3种条件下分别存放0、1、2、3、5、7、9、12、15 d进行对比分析。
猪肉样品处理与脂质提取流程: 称取5 g样品经冷冻研磨后使用20 mL甲醇-乙腈-水(4:1:1, V:V:V)提取, 低温超声提取30 min, 离心10 min后取上清液氮吹至干燥, 1 mL乙腈-水(1:1, V:V)溶液复溶, 低温离心5 min (10000 r/min, 4 ℃), 取上清液过0.22 μm有机系针式滤膜, 待测。
1.3.2 色谱条件
流动相: A: 水[0.1%甲酸(V:V)]; B: 甲醇[0.1%甲酸(V:V)]; 柱温: 40 ℃; 进样体积: 5 μL; 流速: 0.4 mL/min; 洗针液: 异丙醇:水(1:9, V:V)。转换阀: 在0.5~12.0 min之间将洗脱液切换至质谱检测器, 0.5 min之前和12.0 min之后洗脱液切换至废液。梯度洗脱程序见表1所示。
1.3.3 质谱条件
采用Q Exactive系列质谱仪Full MS-ddMS2模式进行样本一级、二级谱图的采集。分别采用电喷雾电离(electrospray ionization, ESI)正离子和负离子模式进行检测。采用Full MS-ddms2数据扫描模式, 正/负模式各扫一次, 得到各混合标准工作液的总离子流色谱图。
质量扫描范围m/z: 100~1500, 离子喷雾电压±3500 V, 鞘气45 psi, 辅助加热气15 psi, 辅助加热温度350 ℃, 离子源加热温度350 ℃, 10~20-30 V循环碰撞能, MS1分辨率70000, MS2分辨率17500, 顶点激发1~4 s, 目标离子数为1e6。
1.4 数据处理和差异标志物的鉴定
首先, 使用Xcalibur 3.0软件对采集的质谱数据进行色谱峰提取、峰对齐, 利用mzCloud数据库进行代谢物鉴定, 分别在正离子模式和负离子模式下鉴定出8928个和7165个代谢物。随后将数据导入SIMCA 17.1进行化学计量学分析, 采用主成分分析(principal component analysis, PCA)进行降维以探索样本间的分布特征, 并通过正交偏最小二乘法判别分析(orthogonal partial least squares-discriminant analysis, OPLS-DA)筛选出变量重要性因子(variable importance factor, VIP)值大于1.0的差异代谢物。进一步通过T检验(P<0.05)评估差异的统计显著性, 并结合倍数变化(fold change, FC>2或FC<0.5)分析确认最终的关键代谢物。
2 结果与分析
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2.1 化学计量学分析
PCA作为无监督模型, 对获得的原始数据进行降维处理, 可将无序数据转变为有序信息[25]。OPLS-DA是一种有监督的模型, 解释了导致2组之间显著差异的相关代谢物信息, 消除了与分类不相关的噪音信息[26]。本研究基于PCA和OPLS-DA模型, 揭示了猪肉不同部位(里脊、五花肉、后腿肉)在储藏过程中的代谢特征差异。
基于所有检测到的代谢物进行PCA和OPLS-DA分析, 同时区分不同部位的猪肉(里脊、五花、后腿), 以探讨不同部位在储藏条件下的代谢变化差异, 并通过置换检验验证模型稳定性。图1显示, PCA中各组样本间有一定分离, 但存在部分重叠, 表明其区分能力有限; 而通过OPLS-DA进一步优化后, 各组样本间完全分离, 模型稳定性和预测能力显著增强, 交叉验证表明模型不存在过拟合现象。
异性的分布说明不同部位代谢特征明显, 有助于通过代谢物分析区分储存过程中猪肉的具体部位。
通过对里脊、五花肉和后腿肉3种部位的代谢物分析(图2), 由图2A发现各部位代谢物具有显著的特异性和交集。VIP值分布结果显示, VIP>1的代谢物数量在不同部位之间有所差异, 其中五花肉样品的VIP>1代谢物最多(1237个), 其次为后腿肉(1196个), 里脊肉最少(1106个)。VIP值的分布表明不同部位的代谢物具有显著特异性, 这为后续标志物筛选提供了基础。
图2B韦恩图代表了标志物在3种样品间的交集与特异性: 3种部位(里脊、五花肉、后腿肉)之间有732个共同差异代谢物, 这些是可能的稳定性标志物。五花肉具有247个特异性代谢物, 里脊和后腿肉分别为161个和241个。部位特异性的分布说明不同部位代谢特征明显, 有助于通过代谢物分析区分储存过程中猪肉的具体部位。
基于筛选出的732个标志物重新构建PCA和OPLS-DA模型, 不再区分猪肉部位, 而是将所有部位的样本混合进行分析, 以探讨储藏温度(4、-20、40 ℃)和储藏时间对猪肉代谢物变化的整体影响, 并利用VIP值和FC进一步筛选差异代谢物。通过PCA-X和OPLS-DA模型(图3), 成功区分了不同储藏条件下的样品(D0: 初始样品、T4、T-20、T40)以及QC。PCA显示, 储藏温度显著影响样品的代谢物分布, 不同温度下样品间在主成分空间中分离良好(R2=0.861, Q2=0.478)。OPLS-DA 模型进一步优化了组间差异的分离(R2=0.811, Q2=0.653), 储藏温度对代谢物丰度和类型的影响更加清晰。
置换检验结果表明, 模型具有较强的预测能力, 无过拟合现象。真实模型的R2和Q2明显高于置换模型, 验证了分析的可靠性。这一结果表明, 储藏温度会显著影响猪肉代谢物的变化规律, 不同温度的样品代谢特征可以被有效区分。通过筛选条件(VIP>1, P<0.05, FC>2或FC<0.5), 最终从各储藏条件样品中鉴定出多个显著差异代谢物。VIP值分布表明, 差异代谢物的贡献主要集中于特定温度组, 而不同温度下代谢物的特异性和丰度变化进一步支持储藏条件对代谢物特征的显著影响。
2.2 差异标志物的鉴定与筛选
基于多层次数据分析, 采用OPLS-DA筛选储藏条件下显著差异的代谢物(VIP>1.0), 并结合双侧独立样本T检验(P<0.05)及倍数变化(FC>2或FC<0.5)进一步确认关键代谢物。表2展示了猪肉样品中筛选出的主要差异代谢物及其对应的质谱特征和化学结构, 包括N-乙酰腐胺和6-甲基喹啉。进一步通过标准品比对其结构准确性, 包括保留时间(retention time, RT)对比、母离子和二级质谱碎片(MS/MS)信息匹配。由于基质效应、流动相组成及仪器状态等因素, 液相色谱-质谱联用技术(liquid chromatography-mass spectrometry, LC-MS)分析中同一化合物的RT可能发生轻微漂移, 因此在鉴定过程中不仅依赖于保留时间, 还结合母离子匹配和二级碎片信息比对, 确保结构特征高度一致, 从而提高鉴定的准确性。图4A中N-乙酰腐胺和图4B中6-甲基喹啉的保留时间及二级碎片谱图显示出一致性, 进一步验证了其准确鉴定。这表明, 在UPLC-Q-Orbitrap HRMS 分析中, 这两种差异代谢物的信号清晰、重复性好, 可作为差异标志物。
乙酰腐胺属于腐胺衍生物, 其生成过程与蛋白质降解和氨基酸脱羧有关[27]。在猪肉储藏过程中, 腐胺可通过转氨酶反应生成N-乙酰腐胺, 这种代谢过程与微生物的活性密切相关[28]。N-乙酰腐胺的高丰度表明样品在储藏条件下存在显著的蛋白质降解和微生物活性增加[29]。通过质谱检测, 其电离模式为[M+H]+, 分子量为131.11766, 碎片离子特征明确, 支持其作为腐败标志物的潜力。
6-甲基喹啉是一种具有生物活性的代谢物, 具有抗菌、抗病毒等多种作用[30]。在储藏过程中, 其生成与脂肪氧化和氨基酸降解相关[31-32]。质谱检测结果表明, 6-甲基喹啉的电离模式为[M+H]+, 分子量为144.08043, 具有稳定的二级碎片特征。这表明6-甲基喹啉可能在储藏过程中反映了脂肪氧化和微生物活动的累积效应, 其浓度变化可以作为储藏温度和时间的指标。
图5展示了两个关键代谢标志物——6-甲基喹啉和N-乙酰腐胺在不同储藏温度和时间下的浓度变化规律: (1) 6-甲基喹啉随着储藏天数的增加, 其浓度在不同温度条件下均表现为持续增长, 尤其是在高温(T40)组中增长最为显著。低温组(T4)中浓度变化较小且增长缓慢。(2) N-乙酰腐胺浓度在储藏初期(D0~D3)迅速增加, 随后在高温条件(T40)下呈现持续增长趋势, 而在低温条件(T4)下浓度变化趋于平稳。
2.2.1 储藏温度显著影响标志物的生成
高温条件(T40): 在里脊、五花肉和后腿肉3个部位中, 标志物浓度增长迅速且波动较大, 说明高温储藏加剧了脂质氧化和蛋白质降解反应, 导致6-甲基喹啉和N-乙酰腐胺大量生成。
中温条件(T25): 标志物浓度的增长趋势介于T40和T4之间, 表明中温储藏对代谢过程有一定促进作用, 但未达到高温条件的强度。
低温条件(T4): 标志物浓度变化较小, 说明低温能够有效抑制劣变代谢反应的发生。
2.2.2 部位间代谢物的生成差异
(1)里脊肉(低脂肪)
6-甲基喹啉: 在整个储藏过程中浓度变化较小, 即使在高温下(T40组), 其增长速率也低于五花肉和后腿肉。这可能是由于里脊肉的脂肪含量较低, 脂质氧化的初始底物较少, 导致 6-甲基喹啉的生成受限。
N-乙酰腐胺: 尽管里脊肉蛋白质含量较高, 但其氨基酸降解途径可能受到蛋白酶活性和基质环境的影响, 因此N-乙酰腐胺的积累相较于五花肉有所减缓。
(2)五花肉(高脂肪)
6-甲基喹啉: 在高温(T40)和中温(T25)下的增长最为明显, 表明脂肪氧化反应更为活跃。此外, 五花肉的脂肪含量更高, 可能促进了更多脂肪降解中间产物的形成, 加速6-甲基喹啉的生成。
N-乙酰腐胺: 其浓度增长幅度大于里脊肉, 这可能是由于五花肉在高温下脂肪降解与蛋白质降解相互作用, 使得代谢通路更为活跃。
(3)后腿肉(中等脂肪)
6-甲基喹啉和N-乙酰腐胺的生成趋势均介于里脊和五花肉之间, 符合中等脂肪和蛋白质含量对代谢速率的影响。这表明该部位的脂肪氧化和氨基酸降解途径均较为均衡, 导致代谢物生成速率适中。
2.2.3 储藏时间显著影响标志物的生成
早期(D0~D3): 标志物浓度迅速上升, 尤其是N-乙酰腐胺, 这可能与初期蛋白质降解加速、氨基酸脱羧反应活跃有关。
中后期(D5~D15): 6-甲基喹啉的浓度增长显著, 这表明脂质氧化在储藏后期成为主要代谢路径。
6-甲基喹啉和N-乙酰腐胺在猪肉储藏过程中表现出明显的温度和时间依赖性, 是猪肉品质劣变的敏感指标。低温显著抑制了劣变相关代谢物的生成, 验证了低温储藏对延缓食品腐败的有效性。不同脂肪含量肉的管理策略: 根据不同脂肪含量肉类代谢物生成的差异, 未来可针对脂肪含量较高的肉类采取更严格的储藏条件。
3 结论
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本研究基于UPLC-Q-Orbitrap HRMS结合化学计量学的分析方法, 筛选出了猪肉储藏过程中的两种关键风险标志物6-甲基喹啉和N-乙酰腐胺。对两者的含量随着不同温度下的储存天数的变化规律进行了研究。结果表明, 6-甲基喹啉和N-乙酰腐胺的含量在不同温度下均随着储存天数的增加而不断增加, 低温组的变化趋势相对较小, 高温组中标志物的含量随着时间增加迅速增加, 说明猪肉的劣变与其储存温度相关, 低温储存条件下可有效抑制其劣变。未来的研究中, 将继续通过对生物标志物的准确定量, 用于评估猪肉的劣变程度, 为猪肉储藏优化提供科学依据。
基金
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  • 广东省科技计划项目(2021B0909050002)
文献
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2025年第16卷第10期
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doi: 10.19812/j.cnki.jfsq11-5956/ts.20250106004
  • 接收时间：2025-01-06
  • 首发时间：2025-07-15
  • 出版时间：2025-05-25
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  • 收稿日期：2025-01-06
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广东省科技计划项目(2021B0909050002)
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    1.中国检验检疫科学研究院粤港澳大湾区研究院, 特殊食品安全研究中心, 中山 528437
    2.广东省袁记食品集团有限公司, 质量管理中心, 佛山 528315

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* 王云帆(1984—), 男, 助理研究员, 主要研究方向为食品质量与安全。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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