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Article(id=1153433691918426940, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1153433686872679135, articleNumber=null, orderNo=null, doi=10.19812/j.cnki.jfsq11-5956/ts.20240904005, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1725379200000, receivedDateStr=2024-09-04, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1752929621914, onlineDateStr=2025-07-19, pubDate=1744646400000, pubDateStr=2025-04-15, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752929621914, onlineIssueDateStr=2025-07-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752929621914, creator=13701087609, updateTime=1752929621914, updator=13701087609, issue=Issue{id=1153433686872679135, tenantId=1146029695717560320, journalId=1149652044408987649, year='2025', volume='16', issue='7', pageStart='1', pageEnd='322', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752929620712, creator=13701087609, updateTime=1757656380159, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1173259152974561742, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1153433686872679135, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1173259152978756047, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1153433686872679135, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=1, endPage=11, ext={EN=ArticleExt(id=1153433692312691522, articleId=1153433691918426940, tenantId=1146029695717560320, journalId=1149652044408987649, language=EN, title=New application of metabolomics in non-targeted screening of residual veterinary drugs in fish, columnId=1153433689657692523, journalTitle=Journal of Food Safety & Quality, columnName=Highlight: Technology Centre of Dalian Customs, runingTitle=null, highlight=null, articleAbstract=

Objective To establish a new non-targeted screening approach for multi-class veterinary drugs in fish based on metabolomics analysis. Methods High performance liquid chromatography-tandem mass spectrometer in full scan mode was adopted for the determination of fish to obtain the metabolomics data, which were uploaded onto Workflow4Metabolomics platform. The data underwent chromatographic peak treatment to obtain a data matrix containing 867 variables. The data matrix was further analyzed by principal component analysis, cluster analysis, orthogonal partial least squares discriminant analysis, pairwise t-test and fold change of concentration to screen eligible variables. These variables were confirmed as the characteristic ones of marker compounds to represent residual veterinary drugs via comparison within a compound database. Results The 88 in 102 kinds of veterinary drugs were screened by proposed metabolomics approach under the help of high performance liquid chromatography- tandem mass spectrometer (HPLC-MS/MS) positive mode, with the limits of detection ranging from 0.3 to 2.6 µg/kg, achieving a screening rate of 86%. Conclusion Metabolomics analytical strategy can effectively realize the non-targeted screening of veterinary drugs in fish, and provides a new idea for non-targeted screening of contaminants.

, correspAuthors=Wei-Feng XUE, 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=Wei-Feng XUE, Ai-Fu YANG, Shen-Shun LUAN, Jian WU), CN=ArticleExt(id=1153433723581227954, articleId=1153433691918426940, tenantId=1146029695717560320, journalId=1149652044408987649, language=CN, title=代谢组学在鱼肉中兽药残留非靶向筛查的新应用, columnId=1153433689825464685, journalTitle=食品安全质量检测学报, columnName=本期重点：大连海关, runingTitle=null, highlight=null, articleAbstract=

目的 建立基于代谢组学分析的鱼肉中多类别兽药残留非靶向筛查的新方法。方法 在高效液相色谱-串联质谱仪全扫描模式下检测鱼肉样品, 从而获得代谢组学数据。将该数据上传至Workflow4Metabolomics平台, 经过色谱峰处理后得到包含867个变量的数据矩阵, 再通过主成分分析、聚类分析、正交偏最小二乘判别分析、成对t检验和浓度倍数变化分析, 挑选出符合条件的变量。通过数据库比对, 最终确定能够代表残留兽药的“标志化合物”的特征变量。结果 借助高效液相色谱-串联质谱仪(high performance liquid chromatography-tandem mass spectrometer, HPLC-MS/MS)的正离子模式, 采用代谢组学分析策略, 从102种添加兽药中筛查出其中88种(检出限为0.3~2.6 µg/kg), 筛查率达86%。结论 代谢组学分析策略能够高效地实现鱼肉中残留兽药的非靶向筛查, 为污染物非靶向筛查提供了一种新的思路。

, correspAuthors=薛伟锋, authorNote=null, correspAuthorsNote=
* 薛伟锋(1983—), 男, 博士, 主要研究方向为食品污染物非靶向筛查。E-mail:
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Archives of Environmental Contamination and Toxicology, 2019, 77: 88-97., articleTitle=Occurrence, seasonal variation and risk assessment of antibiotics in the surface water of North China, refAbstract=null), Reference(id=1173278610241766268, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433691918426940, doi=null, pmid=null, pmcid=null, year=2017, volume=51, issue=null, pageStart=2392, pageEnd=2400, url=null, language=null, rfNumber=[39], rfOrder=42, authorNames=LIU SS, ZHAO HX, LEHMLER HJ, journalName=Environmental Science and Technology, refType=null, unstructuredReference=LIU SS, ZHAO HX, LEHMLER HJ, et al. Antibiotic pollution in marine food webs in Laizhou Bay, North China: Trophodynamics and human exposure implication[J]. 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Basic informations of 102 kinds of veterinary drugs

, figureFileSmall=null, figureFileBig=null, tableContent=
种类 化合物 CAS号 提取分子量 保留时间/min 种类 化合物 CAS号 提取分子量 保留时间/min
激动剂 索他洛尔 959-24-0 273.1267 1.36 非甾体抗炎药 酮基布洛芬 22071-15-4 255.1016 6.03
特布他林 23031-25-6 226.1438 1.46 萘丁美酮 42924-53-8 229.1223 6.20
沙丁胺醇 18559-94-9 240.1594 1.78 沙奎那韦 42461-84-7 297.0845 6.30
阿替洛尔 29122-68-7 267.1703 2.05 卡洛芬 53716-49-7 274.0629 6.37
西布特罗 54239-39-3 234.1601 2.47 吲哚美辛 53-86-1 358.0841 6.40
氯丙那林 3811-25-4 214.0993 3.88 盘尼西林 阿莫西林 26787-78-0 366.1121 3.02
西马特罗 54239-37-1 220.1444 4.03 氨苄西林 69-53-4 350.1174 3.70
克仑特罗 37148-27-9 277.0869 4.12 萘氟西林 147-52-4 415.1349 6.66
妥布特罗 41570-61-0 228.115 4.55 喹诺酮类 马波沙星 11550-35-1 363.1463 3.28
溴布特罗 41937-02-4 364.9859 4.60 氧氟沙星 82419-36-1 362.1511 3.53
马布台诺 54240-36-7 311.1132 4.73 培氟沙星 70458-92-3 334.1561 3.54
普萘洛尔 5051-22-9 260.1645 5.27 诺氟沙星 70458-96-7 320.1405 3.59
倍他洛尔 63659-18-7 308.222 5.36 环丙沙星 85721-33-1 332.1405 3.73
喷布洛尔 36507-48-9 292.2271 5.85 达诺沙星 112398-08-0 358.1561 3.87
苯并咪唑类 噻菌灵 148-79-8 202.0461 3.61 恩诺沙星 93106-60-6 360.1718 3.87
阿苯哒唑亚砜 54029-12-8 282.0932 4.17 奥比沙星 113617-63-3 396.1529 4.02
奥芬哒唑 53716-50-0 316.0756 4.81 双氟沙星 98106-17-3 400.1467 4.10
奥苯哒唑 20559-55-1 250.1191 4.85 沙拉沙星 98105-99-8 386.1311 4.22
阿苯哒唑砜 75184-71-3 298.0883 4.92 恶喹酸 14698-29-4 262.0710 5.06
阿苯哒唑 54965-21-8 266.0885 5.60 萘啶酸 389-08-2 233.0921 5.52
甲苯哒唑 31431-39-7 296.1057 5.61 氟甲喹 42835-25-6 262.0874 5.61
氟苯咪唑 31430-15-6 314.0963 5.84 类固醇 泼尼松 1953/3/2 359.1853 5.62
芬苯哒唑 43210-67-9 300.0808 6.30 泼尼松龙 50-24-8 361.2009 5.73
非班太尔 58306-30-2 447.1334 7.19 氟米松 2135-17-3 411.1978 5.86
三氯苯哒唑 68786-66-3 358.9578 7.36 倍他米松 378-44-9 393.2072 5.90
头孢菌素 头孢吡啉 21593-23-7 424.0631 2.77 倍氯米松 4419-39-0 409.1776 5.95
头孢匹罗 84957-29-9 515.1166 2.80 氟米龙 426-13-1 377.2123 6.01
头孢他啶 78439-06-2 547.1064 2.91 氟氢缩松 1524-88-5 437.2334 6.07
头孢克洛 53994-73-3 368.0466 3.35 地夫可特 14484-47-0 442.2224 6.11
头孢氨苄 15686-71-2 348.1012 3.75 曲安奈德 76-25-5 435.2177 6.12
头孢克肟 79350-37-1 454.0486 3.91 莫美他松 105102-22-5 427.1437 6.17
头孢唑啉 25953-19-9 455.0373 3.99 布地奈德 51372-29-3 431.2428 6.34
头孢拉定 38821-53-3 350.1169 4.12 哈西奈德 3093-35-4 455.1995 6.40
头孢哌酮 62893-19-0 646.1497 4.45 泼尼卡酯 73771-04-7 489.2483 6.43
头孢噻呋 80370-57-6 524.0363 5.08 安西奈德 51022-69-6 503.2440 6.49
二氨基嘧啶 巴喹普林 102280-35-3 309.1850 2.20 磺胺类 磺胺脒 57-67-0 215.0597 1.80
甲氧苄啶 738-70-5 291.1454 3.80 磺胺二甲嘧啶 57-68-1 279.0915 2.08
大环内酯类 林可霉素 154-21-2 407.2210 3.63 磺胺甲氧哒嗪 80-35-3 281.0703 3.60
螺旋霉素 8025-81-8 843.5213 4.25 磺胺嘧啶 68-35-9 251.0597 3.71
替米考星 108050-54-0 869.5733 4.65 磺胺噻唑 72-14-0 256.0209 3.83
竹桃霉素 3922-90-5 688.4266 5.01 磺胺甲基嘧啶 127-79-7 265.0754 4.13
克林霉素 18323-44-9 425.1871 5.11 磺胺二甲异唑 127-69-5 268.0750 4.25
红霉素 59319-72-1 734.4685 5.17 磺胺苯吡唑 526-08-9 315.0910 4.81
吉他霉素 1392-21-8 786.4634 5.24 磺胺地索辛 122-11-2 311.0808 5.02
泰乐菌素 74610-55-2 916.5264 5.28 磺胺甲恶唑 723-46-6 254.0594 5.10
罗红霉素 80214-83-1 837.5321 5.79 磺胺喹噁啉 59-40-5 301.0754 5.12
交沙霉素 16846-24-5 828.4747 6.01 磺胺氯吡嗪 102-65-8 285.0211 5.50
非甾体抗炎药 替诺昔康 59804-37-4 338.0264 4.98 四环素类 土霉素 79-57-2 461.1559 3.89
吡罗昔康 36322-90-4 332.0699 5.65 四环素 60-54-8 463.1711 4.09
美洛昔康 71125-38-7 352.0420 5.96 金霉素 57-62-5 479.1216 4.60
托麦汀 26171-23-3 258.1125 6.01 强力霉素 564-25-0 445.1605 4.72
), ArticleFig(id=1173278604646564624, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433691918426940, language=CN, label=表1, caption=

102种兽药基本信息

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种类 化合物 CAS号 提取分子量 保留时间/min 种类 化合物 CAS号 提取分子量 保留时间/min
激动剂 索他洛尔 959-24-0 273.1267 1.36 非甾体抗炎药 酮基布洛芬 22071-15-4 255.1016 6.03
特布他林 23031-25-6 226.1438 1.46 萘丁美酮 42924-53-8 229.1223 6.20
沙丁胺醇 18559-94-9 240.1594 1.78 沙奎那韦 42461-84-7 297.0845 6.30
阿替洛尔 29122-68-7 267.1703 2.05 卡洛芬 53716-49-7 274.0629 6.37
西布特罗 54239-39-3 234.1601 2.47 吲哚美辛 53-86-1 358.0841 6.40
氯丙那林 3811-25-4 214.0993 3.88 盘尼西林 阿莫西林 26787-78-0 366.1121 3.02
西马特罗 54239-37-1 220.1444 4.03 氨苄西林 69-53-4 350.1174 3.70
克仑特罗 37148-27-9 277.0869 4.12 萘氟西林 147-52-4 415.1349 6.66
妥布特罗 41570-61-0 228.115 4.55 喹诺酮类 马波沙星 11550-35-1 363.1463 3.28
溴布特罗 41937-02-4 364.9859 4.60 氧氟沙星 82419-36-1 362.1511 3.53
马布台诺 54240-36-7 311.1132 4.73 培氟沙星 70458-92-3 334.1561 3.54
普萘洛尔 5051-22-9 260.1645 5.27 诺氟沙星 70458-96-7 320.1405 3.59
倍他洛尔 63659-18-7 308.222 5.36 环丙沙星 85721-33-1 332.1405 3.73
喷布洛尔 36507-48-9 292.2271 5.85 达诺沙星 112398-08-0 358.1561 3.87
苯并咪唑类 噻菌灵 148-79-8 202.0461 3.61 恩诺沙星 93106-60-6 360.1718 3.87
阿苯哒唑亚砜 54029-12-8 282.0932 4.17 奥比沙星 113617-63-3 396.1529 4.02
奥芬哒唑 53716-50-0 316.0756 4.81 双氟沙星 98106-17-3 400.1467 4.10
奥苯哒唑 20559-55-1 250.1191 4.85 沙拉沙星 98105-99-8 386.1311 4.22
阿苯哒唑砜 75184-71-3 298.0883 4.92 恶喹酸 14698-29-4 262.0710 5.06
阿苯哒唑 54965-21-8 266.0885 5.60 萘啶酸 389-08-2 233.0921 5.52
甲苯哒唑 31431-39-7 296.1057 5.61 氟甲喹 42835-25-6 262.0874 5.61
氟苯咪唑 31430-15-6 314.0963 5.84 类固醇 泼尼松 1953/3/2 359.1853 5.62
芬苯哒唑 43210-67-9 300.0808 6.30 泼尼松龙 50-24-8 361.2009 5.73
非班太尔 58306-30-2 447.1334 7.19 氟米松 2135-17-3 411.1978 5.86
三氯苯哒唑 68786-66-3 358.9578 7.36 倍他米松 378-44-9 393.2072 5.90
头孢菌素 头孢吡啉 21593-23-7 424.0631 2.77 倍氯米松 4419-39-0 409.1776 5.95
头孢匹罗 84957-29-9 515.1166 2.80 氟米龙 426-13-1 377.2123 6.01
头孢他啶 78439-06-2 547.1064 2.91 氟氢缩松 1524-88-5 437.2334 6.07
头孢克洛 53994-73-3 368.0466 3.35 地夫可特 14484-47-0 442.2224 6.11
头孢氨苄 15686-71-2 348.1012 3.75 曲安奈德 76-25-5 435.2177 6.12
头孢克肟 79350-37-1 454.0486 3.91 莫美他松 105102-22-5 427.1437 6.17
头孢唑啉 25953-19-9 455.0373 3.99 布地奈德 51372-29-3 431.2428 6.34
头孢拉定 38821-53-3 350.1169 4.12 哈西奈德 3093-35-4 455.1995 6.40
头孢哌酮 62893-19-0 646.1497 4.45 泼尼卡酯 73771-04-7 489.2483 6.43
头孢噻呋 80370-57-6 524.0363 5.08 安西奈德 51022-69-6 503.2440 6.49
二氨基嘧啶 巴喹普林 102280-35-3 309.1850 2.20 磺胺类 磺胺脒 57-67-0 215.0597 1.80
甲氧苄啶 738-70-5 291.1454 3.80 磺胺二甲嘧啶 57-68-1 279.0915 2.08
大环内酯类 林可霉素 154-21-2 407.2210 3.63 磺胺甲氧哒嗪 80-35-3 281.0703 3.60
螺旋霉素 8025-81-8 843.5213 4.25 磺胺嘧啶 68-35-9 251.0597 3.71
替米考星 108050-54-0 869.5733 4.65 磺胺噻唑 72-14-0 256.0209 3.83
竹桃霉素 3922-90-5 688.4266 5.01 磺胺甲基嘧啶 127-79-7 265.0754 4.13
克林霉素 18323-44-9 425.1871 5.11 磺胺二甲异唑 127-69-5 268.0750 4.25
红霉素 59319-72-1 734.4685 5.17 磺胺苯吡唑 526-08-9 315.0910 4.81
吉他霉素 1392-21-8 786.4634 5.24 磺胺地索辛 122-11-2 311.0808 5.02
泰乐菌素 74610-55-2 916.5264 5.28 磺胺甲恶唑 723-46-6 254.0594 5.10
罗红霉素 80214-83-1 837.5321 5.79 磺胺喹噁啉 59-40-5 301.0754 5.12
交沙霉素 16846-24-5 828.4747 6.01 磺胺氯吡嗪 102-65-8 285.0211 5.50
非甾体抗炎药 替诺昔康 59804-37-4 338.0264 4.98 四环素类 土霉素 79-57-2 461.1559 3.89
吡罗昔康 36322-90-4 332.0699 5.65 四环素 60-54-8 463.1711 4.09
美洛昔康 71125-38-7 352.0420 5.96 金霉素 57-62-5 479.1216 4.60
托麦汀 26171-23-3 258.1125 6.01 强力霉素 564-25-0 445.1605 4.72
), ArticleFig(id=1173278604772393747, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433691918426940, language=EN, label=Table 3, caption=

The 88 kinds of marker compounds screened in fish sample groups

, figureFileSmall=null, figureFileBig=null, tableContent=
种类 标志
化合物		 变量 VIPa 分子量
误差/ppmb		 检出限
/(µg/kg)		 种类 标志
化合物		 变量 VIPa 分子量
误差/ppmb		 检出限/(µg/kg)
激动剂 索他洛尔 M273T81 1.185/1.160 -4.613 2.6 非甾体抗炎药 替诺昔康 M338T299 1.410/3.509 1.479 1.7
特布他林 M226T87 1.273/1.196 -4.024 0.6 吡罗昔康 M332T339 1.423/1.112 -1.596 0.9
沙丁胺醇 M240T107 1.247/1.869 3.456 2.2 美洛昔康 M352T358 1.393/1.485 -0.426 2.1
阿替洛尔 M267T124 1.215/1.151 4.903 0.5 酮基布洛芬 M255T362 1.437/1.019 3.842 1.1
西布特罗 M234T148 1.253/1.213 3.246 1.3 沙奎那韦 M297T378 1.436/1.110 3.703 0.9
氯丙那林 M214T233 1.351/3.345 -0.514 0.5 吲哚美辛 M358T384 1.392/3.312 4.357 0.7
西马特罗 M220T242 1.275/1.737 -0.954 1.7 喹诺酮类 马波沙星 M363T197 2.137/1.356 -2.891 1.6
克仑特罗 M277T247 1.168/1.032 3.573 0.7 氧氟沙星 M362T211 2.091/5.475 3.728 1.8
妥布特罗 M228T273 1.264/1.253 1.578 1.8 培氟沙星 M334T212 2.050/5.386 3.980 1.0
溴布特罗 M364T278 1.139/1.037 -4.493 0.6 诺氟沙星 M320T215 2.120/2.255 1.905 0.9
马布台诺 M311T287 1.141/1.020 -4.436 0.7 环丙沙星 M332T223 2.293/1.670 2.138 0.6
倍他洛尔 M308T323 1.157/1.096 -1.428 2.5 达诺沙星 M358T232 2.284/3.094 0.893 1.3
苯并咪
唑类		 阿苯哒唑亚砜 M282T252 8.638/5.493 3.970 0.7 恩诺沙星 M360T234 2.189/1.360 0.583 0.5
奥芬哒唑 M316T291 4.413/3.011 1.297 1.6 奥比沙星 M396T243 2.076/2.018 0.151 1.4
奥苯哒唑 M250T292 4.357/5.184 -3.438 1.9 双氟沙星 M400T243 2.214/3.692 -3.599 1.7
阿苯哒唑砜 M298T295 9.932/6.480 -0.134 1.6 沙拉沙星 M386T253 2.017/1.235 3.574 0.7
阿苯哒唑 M266T339 13.421/9.092 -3.946 0.6 类固醇 氟米松 M411T353 1.088/1.317 0.268 1.6
甲苯哒唑 M296T333 4.497/3.073 -2.668 1.3 泼尼松龙 M361T343 1.097/2.716 -1.218 2.4
氟苯咪唑 M314T350 4.640/7.811 -1.146 0.8 倍他米松 M393T352 1.134/1.599 4.349 0.5
芬苯哒唑 M300T376 4.869/2.145 1.033 1.0 倍氯米松 M409T355 1.106/1.114 4.888 1.1
非班太尔 M447T433 6.768/4.439 -1.476 0.6 氟氢缩松 M437T364 1.017/1.214 -0.915 0.7
头孢
菌素		 头孢吡啉 M424T165 3.597/1.195 1.391 1.4 地夫可特 M442T367 1.014/1.050 -0.430 0.5
头孢匹罗 M515T171 3.600/6.013 -0.214 1.0 曲安奈德 M435T367 1.023/1.016 1.103 0.7
头孢他啶 M547T176 3.385/1.985 -2.815 0.4 莫美他松 M427T372 1.076/1.080 4.425 1.0
头孢
菌素		 头孢克洛 M368T203 4.183/1.440 -2.717 2.0 类固醇 布地奈德 M431T381 1.038/1.240 -0.301 0.8
头孢氨苄 M348T225 3.591/2.385 3.735 1.2 哈西奈德 M455T384 1.012/1.288 2.658 0.6
头孢克肟 M454T234 3.151/3.096 -4.427 0.7 泼尼卡酯 M489T387 1.010/1.977 -1.513 0.7
头孢唑啉 M455T235 3.543/2.305 4.219 0.9 安西奈德 M503T387 1.003/1.033 -0.278 1.6
头孢拉定 M350T248 3.918/1.412 0.885 1.7 磺胺类 磺胺脒 M215T107 2.003/1.990 -4.417 0.7
头孢哌酮 M646T268 3.209/9.092 -4.132 0.5 磺胺二甲
嘧啶		 M279T126 1.687/1.653 2.221 0.8
头孢噻呋 M524T300 4.225/5.817 -0.954 0.4 磺胺甲氧
哒嗪		 M281T216 1.641/1.113 3.309 0.8
二氨基
嘧啶		 巴喹普林 M309T133 2.918/2.026 2.652 2.0 磺胺嘧啶 M251T223 1.908/1.680 -4.222 0.6
甲氧苄啶 M291T228 2.936/2.227 1.443 0.9 磺胺噻唑 M256T229 1.821/1.269 0.547 0.9
大环内
酯类		 林可霉素 M407T218 2.628/1.170 2.701 1.8 磺胺甲基
嘧啶		 M265T245 1.695/2.590 -3.358 1.9
螺旋霉素 M843T253 2.769/2.550 0.605 1.7 磺胺二甲
异唑		 M268T254 1.654/2.843 -3.432 0.8
替米考星 M869T278 2.725/3.316 1.679 0.3 磺胺苯吡唑 M315T289 1.574/1.054 4.824 1.8
竹桃霉素 M688T302 2.782/4.749 -2.034 2.1 磺胺地索辛 M311T302 1.584/1.951 1.222 1.6
克林霉素 M425T304 2.570/1.108 0.870 1.9 磺胺甲恶唑 M254T306 1.842/2.228 0.945 0.6
吉他霉素 M786T312 2.545/1.695 1.831 0.8 磺胺喹噁啉 M301T307 1.602/3.688 -4.152 0.6
泰乐菌素 M916T316 2.632/3.761 -0.840 0.4 磺胺氯吡嗪 M285T328 2.012/1.217 0.070 0.4
交沙霉素 M828T362 2.900/1.473 -2.547 0.7 四环素类 土霉素 M461T235 1.553/2.047 1.908 1.7
盘尼西林 阿莫西林 M366T182 2.510/7.067 3.687 1.7 四环素 M463T245 1.438/2.775 3.131 1.6
氨苄西林 M350T222 2.364/1.852 3.256 0.4 金霉素 M479T277 1.561/1.407 2.108 0.6
萘氟西林 M415T401 2.321/1.835 1.469 2.4 强力霉素 M445T284 1.559/3.745 4.043 0.7
), ArticleFig(id=1173278604868862742, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433691918426940, language=CN, label=表3, caption=

鱼肉样品组筛查出的88种标志化合物

, figureFileSmall=null, figureFileBig=null, tableContent=
种类 标志
化合物		 变量 VIPa 分子量
误差/ppmb		 检出限
/(µg/kg)		 种类 标志
化合物		 变量 VIPa 分子量
误差/ppmb		 检出限/(µg/kg)
激动剂 索他洛尔 M273T81 1.185/1.160 -4.613 2.6 非甾体抗炎药 替诺昔康 M338T299 1.410/3.509 1.479 1.7
特布他林 M226T87 1.273/1.196 -4.024 0.6 吡罗昔康 M332T339 1.423/1.112 -1.596 0.9
沙丁胺醇 M240T107 1.247/1.869 3.456 2.2 美洛昔康 M352T358 1.393/1.485 -0.426 2.1
阿替洛尔 M267T124 1.215/1.151 4.903 0.5 酮基布洛芬 M255T362 1.437/1.019 3.842 1.1
西布特罗 M234T148 1.253/1.213 3.246 1.3 沙奎那韦 M297T378 1.436/1.110 3.703 0.9
氯丙那林 M214T233 1.351/3.345 -0.514 0.5 吲哚美辛 M358T384 1.392/3.312 4.357 0.7
西马特罗 M220T242 1.275/1.737 -0.954 1.7 喹诺酮类 马波沙星 M363T197 2.137/1.356 -2.891 1.6
克仑特罗 M277T247 1.168/1.032 3.573 0.7 氧氟沙星 M362T211 2.091/5.475 3.728 1.8
妥布特罗 M228T273 1.264/1.253 1.578 1.8 培氟沙星 M334T212 2.050/5.386 3.980 1.0
溴布特罗 M364T278 1.139/1.037 -4.493 0.6 诺氟沙星 M320T215 2.120/2.255 1.905 0.9
马布台诺 M311T287 1.141/1.020 -4.436 0.7 环丙沙星 M332T223 2.293/1.670 2.138 0.6
倍他洛尔 M308T323 1.157/1.096 -1.428 2.5 达诺沙星 M358T232 2.284/3.094 0.893 1.3
苯并咪
唑类		 阿苯哒唑亚砜 M282T252 8.638/5.493 3.970 0.7 恩诺沙星 M360T234 2.189/1.360 0.583 0.5
奥芬哒唑 M316T291 4.413/3.011 1.297 1.6 奥比沙星 M396T243 2.076/2.018 0.151 1.4
奥苯哒唑 M250T292 4.357/5.184 -3.438 1.9 双氟沙星 M400T243 2.214/3.692 -3.599 1.7
阿苯哒唑砜 M298T295 9.932/6.480 -0.134 1.6 沙拉沙星 M386T253 2.017/1.235 3.574 0.7
阿苯哒唑 M266T339 13.421/9.092 -3.946 0.6 类固醇 氟米松 M411T353 1.088/1.317 0.268 1.6
甲苯哒唑 M296T333 4.497/3.073 -2.668 1.3 泼尼松龙 M361T343 1.097/2.716 -1.218 2.4
氟苯咪唑 M314T350 4.640/7.811 -1.146 0.8 倍他米松 M393T352 1.134/1.599 4.349 0.5
芬苯哒唑 M300T376 4.869/2.145 1.033 1.0 倍氯米松 M409T355 1.106/1.114 4.888 1.1
非班太尔 M447T433 6.768/4.439 -1.476 0.6 氟氢缩松 M437T364 1.017/1.214 -0.915 0.7
头孢
菌素		 头孢吡啉 M424T165 3.597/1.195 1.391 1.4 地夫可特 M442T367 1.014/1.050 -0.430 0.5
头孢匹罗 M515T171 3.600/6.013 -0.214 1.0 曲安奈德 M435T367 1.023/1.016 1.103 0.7
头孢他啶 M547T176 3.385/1.985 -2.815 0.4 莫美他松 M427T372 1.076/1.080 4.425 1.0
头孢
菌素		 头孢克洛 M368T203 4.183/1.440 -2.717 2.0 类固醇 布地奈德 M431T381 1.038/1.240 -0.301 0.8
头孢氨苄 M348T225 3.591/2.385 3.735 1.2 哈西奈德 M455T384 1.012/1.288 2.658 0.6
头孢克肟 M454T234 3.151/3.096 -4.427 0.7 泼尼卡酯 M489T387 1.010/1.977 -1.513 0.7
头孢唑啉 M455T235 3.543/2.305 4.219 0.9 安西奈德 M503T387 1.003/1.033 -0.278 1.6
头孢拉定 M350T248 3.918/1.412 0.885 1.7 磺胺类 磺胺脒 M215T107 2.003/1.990 -4.417 0.7
头孢哌酮 M646T268 3.209/9.092 -4.132 0.5 磺胺二甲
嘧啶		 M279T126 1.687/1.653 2.221 0.8
头孢噻呋 M524T300 4.225/5.817 -0.954 0.4 磺胺甲氧
哒嗪		 M281T216 1.641/1.113 3.309 0.8
二氨基
嘧啶		 巴喹普林 M309T133 2.918/2.026 2.652 2.0 磺胺嘧啶 M251T223 1.908/1.680 -4.222 0.6
甲氧苄啶 M291T228 2.936/2.227 1.443 0.9 磺胺噻唑 M256T229 1.821/1.269 0.547 0.9
大环内
酯类		 林可霉素 M407T218 2.628/1.170 2.701 1.8 磺胺甲基
嘧啶		 M265T245 1.695/2.590 -3.358 1.9
螺旋霉素 M843T253 2.769/2.550 0.605 1.7 磺胺二甲
异唑		 M268T254 1.654/2.843 -3.432 0.8
替米考星 M869T278 2.725/3.316 1.679 0.3 磺胺苯吡唑 M315T289 1.574/1.054 4.824 1.8
竹桃霉素 M688T302 2.782/4.749 -2.034 2.1 磺胺地索辛 M311T302 1.584/1.951 1.222 1.6
克林霉素 M425T304 2.570/1.108 0.870 1.9 磺胺甲恶唑 M254T306 1.842/2.228 0.945 0.6
吉他霉素 M786T312 2.545/1.695 1.831 0.8 磺胺喹噁啉 M301T307 1.602/3.688 -4.152 0.6
泰乐菌素 M916T316 2.632/3.761 -0.840 0.4 磺胺氯吡嗪 M285T328 2.012/1.217 0.070 0.4
交沙霉素 M828T362 2.900/1.473 -2.547 0.7 四环素类 土霉素 M461T235 1.553/2.047 1.908 1.7
盘尼西林 阿莫西林 M366T182 2.510/7.067 3.687 1.7 四环素 M463T245 1.438/2.775 3.131 1.6
氨苄西林 M350T222 2.364/1.852 3.256 0.4 金霉素 M479T277 1.561/1.407 2.108 0.6
萘氟西林 M415T401 2.321/1.835 1.469 2.4 强力霉素 M445T284 1.559/3.745 4.043 0.7
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代谢组学在鱼肉中兽药残留非靶向筛查的新应用
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薛伟锋 1, * , 杨爱馥 1 , 栾慎顺 1 , 吴健 2
食品安全质量检测学报 | 本期重点：大连海关 2025,16(7): 1-11
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食品安全质量检测学报 | 本期重点：大连海关 2025, 16(7): 1-11
代谢组学在鱼肉中兽药残留非靶向筛查的新应用
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薛伟锋1, * , 杨爱馥1, 栾慎顺1, 吴健2
作者信息
  • 1.大连海关技术中心, 大连 116600
  • 2.中国检验认证集团黑龙江有限公司, 哈尔滨 150000

通讯作者:

* 薛伟锋(1983—), 男, 博士, 主要研究方向为食品污染物非靶向筛查。E-mail:
New application of metabolomics in non-targeted screening of residual veterinary drugs in fish
Wei-Feng XUE1, * , Ai-Fu YANG1, Shen-Shun LUAN1, Jian WU2
Affiliations
  • 1. Technology Centre of Dalian Customs District, Dalian 116600, China
  • 2. China Certification & Inspection Group Heilongjiang Co., Ltd., Harbin 150000, China
出版时间: 2025-04-15 doi: 10.19812/j.cnki.jfsq11-5956/ts.20240904005
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摘要
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目的 建立基于代谢组学分析的鱼肉中多类别兽药残留非靶向筛查的新方法。方法 在高效液相色谱-串联质谱仪全扫描模式下检测鱼肉样品, 从而获得代谢组学数据。将该数据上传至Workflow4Metabolomics平台, 经过色谱峰处理后得到包含867个变量的数据矩阵, 再通过主成分分析、聚类分析、正交偏最小二乘判别分析、成对t检验和浓度倍数变化分析, 挑选出符合条件的变量。通过数据库比对, 最终确定能够代表残留兽药的“标志化合物”的特征变量。结果 借助高效液相色谱-串联质谱仪(high performance liquid chromatography-tandem mass spectrometer, HPLC-MS/MS)的正离子模式, 采用代谢组学分析策略, 从102种添加兽药中筛查出其中88种(检出限为0.3~2.6 µg/kg), 筛查率达86%。结论 代谢组学分析策略能够高效地实现鱼肉中残留兽药的非靶向筛查, 为污染物非靶向筛查提供了一种新的思路。

鱼肉  /  兽药  /  代谢组学  /  非靶向筛查  /  标志化合物

Objective To establish a new non-targeted screening approach for multi-class veterinary drugs in fish based on metabolomics analysis. Methods High performance liquid chromatography-tandem mass spectrometer in full scan mode was adopted for the determination of fish to obtain the metabolomics data, which were uploaded onto Workflow4Metabolomics platform. The data underwent chromatographic peak treatment to obtain a data matrix containing 867 variables. The data matrix was further analyzed by principal component analysis, cluster analysis, orthogonal partial least squares discriminant analysis, pairwise t-test and fold change of concentration to screen eligible variables. These variables were confirmed as the characteristic ones of marker compounds to represent residual veterinary drugs via comparison within a compound database. Results The 88 in 102 kinds of veterinary drugs were screened by proposed metabolomics approach under the help of high performance liquid chromatography- tandem mass spectrometer (HPLC-MS/MS) positive mode, with the limits of detection ranging from 0.3 to 2.6 µg/kg, achieving a screening rate of 86%. Conclusion Metabolomics analytical strategy can effectively realize the non-targeted screening of veterinary drugs in fish, and provides a new idea for non-targeted screening of contaminants.

fish  /  veterinary drugs  /  metabolomics  /  non-targeted screening  /  marker compounds
薛伟锋, 杨爱馥, 栾慎顺, 吴健. 代谢组学在鱼肉中兽药残留非靶向筛查的新应用. 食品安全质量检测学报, 2025 , 16 (7) : 1 -11 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20240904005
Wei-Feng XUE, Ai-Fu YANG, Shen-Shun LUAN, Jian WU. New application of metabolomics in non-targeted screening of residual veterinary drugs in fish[J]. Journal of Food Safety & Quality, 2025 , 16 (7) : 1 -11 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20240904005
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0 引言
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食品安全是重大的民生和政治问题, “十三五”规划建议更是将食品安全问题上升至国家战略高度。虽然我国的食品安全形势总体向好, 但仍有少数问题(如三聚氰胺奶粉事件)因性质和影响极其恶劣, 导致消费者至今“谈虎色变”。监督不严谨、检验体系不完善是间接造成问题出现的重要原因。突破现有技术瓶颈、开发具有先进性和普适性的食品安全检测技术是解决现有的、正在发生或将来可能发生的食品安全问题的有效途径。
动物源食品兽药非法添加是关注度极高的全球性食品安全问题, 已引起了全世界的广泛关注[1-4]。随着现代化学工业的快速发展, 越来越多的新型兽药可能进入并残留于动物源食品中, 最终通过食物链作用影响人类生命健康[5-7]。鉴于目前动物源食品中兽药筛查方法多为先建立一个含有多种兽药残留的数据库, 再基于该数据库寻找食品中残留兽药, 该方法对数据库内兽药的非靶向筛查具有良好效果, 但却存在无法识别数据库外兽药的明显缺陷, 导致食品安全存在巨大隐患[3-7]。三聚氰胺奶粉和氟虫腈鸡蛋等影响恶劣的食品安全事件就是不法企业或个人人为避开常规抽检设定的指标体系, 非法添加化学品, 利用该化学品不在抽检指标范围内, 即便该化学品浓度再高, 所检出的产品也是合格产品漏洞从事的非法活动, 最终导致悲剧发生。由此可见, 通过不断建立新的数据库达到识别动物源食品中未知和预料外兽药残留是一种被动的、需要消耗大量人力和物力成本的筛查方法, 已经无法满足数量日益增长的兽药残留检测需求, 因此有必要开发一套有效的非靶向筛查方法。
利用代谢组学在分析具有样品量小、干扰物多、噪声高等复杂特征的海量数据所具有的优势, 将其应用于食品中污染物非靶向筛查已经取得了良好的效果[8-12]。该方法基于高分辨气相或液相色谱-串联质谱数据, 经过多变量和单变量分析后, 将寻找残留污染物过程转化为代谢组学中寻找对应的变量过程, 获得了指示残留污染物的特征变量(即“标志化合物”), 以及该变量的质荷比、同位素分布和分子解离形态等信息。这些信息可以用来预测化合物的分子式, 进而在现有的商品化化学物质数据库(如MassBank、ChemSpider、SciFinder、PubChem和Metlin等)或自建数据库中进行检索, 初步推断其可能身份, 再通过该化合物标准品进一步确认其身份, 完成污染物鉴别, 从而达到非靶向筛查目的[10-12]。该方法克服了传统方法无法识别数据库之外残留兽药的缺点, 具有广阔的发展前景和应用价值, 为相关部门全面监督动物源食品安全, 打击兽药非法添加行为, 提供了强有力的技术保障。
随着生活水平的提高, 食用水产品需求量快速增加。据报道[13], 2022年我国水产品进口量增至454万t, 同比增长20.2%。大连口岸作为全国最大的冷链物流基地和第二大冷鲜货物进口口岸, 确保进口水产品安全是大连海关重要职责。尽管海关总署决定自2023年8月24日起全面暂停进口原产地为日本的水产品[14], 但是其他国家(如俄罗斯和美国)的鱼类产品依然可以从大连口岸进港。另外, 本研究还购买了大连市面上的食用鱼类, 用于扩大研究对象范围。本研究从鱼肉中突出的兽药残留问题入手, 以动物源食品中大多数兽药最大残留限量的安全阈值(10 µg/kg)建立分析方法[15], 通过高分辨液相色谱-串联质谱法结合代谢组学分析策略进行兽药残留非靶向筛查分析, 预期建立一种全面、准确、快速筛查鱼类食品中兽药残留方法, 达到保障消费者食用放心鱼类的目的。
1 材料与方法
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1.1 材料与试剂
用于建立筛查方法的鱼肉样品(阴性)为俄罗斯进口鳕鱼; 用于验证筛查方法的鱼肉样品分别为美国进口鳕鱼和大连本地农贸市场采购的鲤鱼。
超纯水(milliq超纯水系统)、甲醇、乙腈、正己烷(色谱纯, 德国Merck公司); 甲酸(色谱纯, 上海安谱实验科技股份有限公司); C18吸附剂(国药集团化学试剂有限公司); 滤膜(0.22 µm, 美国Agilent公司); 恩诺沙星-d5内标溶液(质量浓度100 μg/mL, 甲醇相, 美国First Standard公司)。
102种兽药分别购自美国Sigma公司、美国First Standard公司、德国Dr. Ehrenstorfer公司和加拿大TRC公司, 纯度均大于98%。所有兽药的详细信息如表1所示。
1.2 仪器
8010ES绞肉机(美国Blender公司); JJ200Y天平(感量0.01 g, 常熟市双杰测试仪器厂); MS-3涡旋混合器(德国IKA公司); 3-18KS高速冷冻离心机(美国Sigma公司); QYN100-2氮吹仪(上海巴玖实业有限公司); Q Exactive Plus四极杆/静电场轨道阱高效液相色谱-串联质谱仪(以下简称为高效液相色谱-串联质谱仪)、Accucore RP-MS色谱柱(100 mm×2.1 mm, 2.6 µm)(美国赛默飞世尔科技公司)。
1.3 溶液配制
分别配制包含102种兽药的甲醇溶液(100 µg/mL), 各取1 mL进行混合, 再用甲醇稀释成1 µg/mL溶液。将恩诺沙星-d5的甲醇溶液(100 μg/mL)用甲醇稀释后, 获得100 ng/mL混合溶液。
1.4 样品制备和前处理过程
将鱼肉样品切成小块, 用绞肉机将其打成肉糜; 分别取2.0、5.0和10.0 g肉糜, 以及对应的20、50和100 µL含有102种兽药的混合溶液(1 µg/mL), 加入到50 mL聚丙烯离心管中。每个离心管中继续加入0.5 mL恩诺沙星-d5甲醇溶液(100 ng/mL)用来校准样品前处理过程的回收率; 将10 mL乙腈:水(4:1, V:V)注入离心管, 涡旋1 min。随后, 样品6000 r/min离心10 min; 将全部上清液移入装有500 mg的C18的新离心管中, 继续加入10 mL乙腈饱和正己烷溶液。涡旋1 min后, 样品6000 r/min离心10 min, 下层溶液转移到新的离心管中; 溶液氮吹至近干, 用1 mL甲醇:水(1:1, V:V)溶解残留物, 经0.22 µm滤膜过滤, 制备理论质量浓度分别为20、50和100 ng/mL的102种兽药混合样品溶液。每个质量浓度实验需要重复9次, 从而获得9个平行样品, 用于后续代谢组学分析。
1.5 样品分组和命名
将3个质量浓度组样品分别命名为20 ng/mL-1~20 ng/mL-9、50 ng/mL-1~50 ng/mL-9和100 ng/mL-1~100 ng/mL-9。上述每个样品各抽取30 µL混合获得质控(QC)样品[10,16-17]。该QC样品在每个浓度组样品进样前和进样后分别进样3次, 累计12次, 命名为QC-1、QC-2...QC-12, 用于评价高效液相色谱-串联质谱仪(high performance liquid chromatography-tandem mass spectrometer, HPLC-MS/MS)稳定性。
1.6 分析方法
在可加热电喷雾离子源(electrospray ionization, ESI)正离子模式下, 使用HPLC-MS/MS获取M+H型加合物, 完成所有兽药和内标物的代谢组学分析。每次进样10 µL, 经Accucore RP-MS色谱柱(100 mm×2.1 mm, 2.6 µm)进行组分分离。流动相为0.1%甲酸水溶液(洗脱液A)和0.1%甲酸乙腈溶液(洗脱液B), 流速0.25 mL/min。梯度程序如下: 0 min 5% B; 0.5~5.0 min增加到60% B; 5.0~6.0 min继续增加到100% B; 6.0~11.0 min保持100% B; 11.0~11.5 min下降至5% B; 11.5~15.0 min保持5% B。柱温40 ℃。仪器参数设置如下: 毛细管和加热温度均为320 ℃; 喷雾电压和透镜电压分别为3200 V和50 V; 鞘气和辅助气均为氮气, 流量分别为40 arb和10 arb; 扫描模式为全扫描/数据依赖二级扫描; 一级质谱全扫描分辨率、自动增益控制目标、最大驻留时间和m/z扫描范围分别为70000、1×106、100 ms和100~1000; 二级质谱分辨率、自动增益控制目标和最大驻留时间分别为17500、2×105和50 ms。
采用仪器自带的Trace Finder 3.3软件进行兽药残留分析。对于目标物母离子, 响应强度阈值、信噪比和质量误差分别为10000、5和5 ppm; 对于二级碎片离子, 最小匹配离子数、响应强度阈值和质量误差分别为1、10000和5 ppm。绘制恩诺沙星-d5标准曲线, 以其母离子峰面积为准定量分析, 用于计算回收率。
1.7 代谢组学数据处理
将.RAW格式的HPLC-MS/MS输出文件通过MSConvertGUI软件转换为.mzXML格式文件[18], 然后上传到Workflow4Metabolomics (W4M)平台(https://workflow4metabolomics.usegalaxy.fr/)进行代谢组学分析[19]。色谱峰经过峰检测、峰对齐和保留时间校准, 以及峰强度归一化、中心化、尺度化和数据转换等一系列处理过程后, 形成以变量名(如M111T222, M和T分别代表目标化合物的提取分子量和保留时间)和样品名(如100 ng/mL-1)分别为横、纵坐标的数据矩阵[19-20]。多变量分析中的主成分分析(principal component analysis, PCA)[21-23]和正交偏最小二乘判别分析(orthogonal partial least squares discriminant analysis, OPLS-DA)[24-25], 均在SIMCA 14.1软件[26]中进行, 而聚类分析(cluster analysis, CA)则在Heml 1.0.3.7软件中进行。OPLS-DA模型是否过拟合[26-27]通过置换检验进行评估。变量投影重要性(variable importance in projection, VIP)是筛查符合条件变量作为候选标志化合物的重要参数, 一般要求该值大于1[26-28]。在完成上述工作后, 符合条件的变量被筛查出来。将20 ng/mL和100 ng/mL质量浓度组中符合条件的变量(理论上应分别具有显著低和显著高的峰强度)进行比较, 筛选出重叠变量, 进行成对t检验(在SPSS Statistics V17.0软件中完成)[29-31]和浓度倍数变化分析等单变量分析, 用于进一步确认变量有效性。通过比较102种兽药的保留时间和精确分子量(误差绝对值小于5 ppm), 如表1所示, 最终确定代表标志化合物的特征变量。
2 结果与分析
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2.1 数据预处理
W4M平台上3个浓度组样品的总离子流图如图1所示。鱼肉基质相对比较复杂, 在前处理过程中可能造成不同样品中每种污染物的回收率存在明显差异, 导致总离子流图中峰强度与浓度水平之间相关性不明显, 如图1所示。为了减少回收率对峰强度影响, 恩诺沙星-d5 (母离子m/z 365.21094; 碎片离子m/z 348.19690、322.21803和246.11012; 保留时间3.87 min)作为回收率内标, 一同加入样品中进行前处理实验。使用空白鱼肉提取液制备恩诺沙星-d5标准曲线(5、10、25、50和100 ng/mL), 计算该内标物在样品中的回收率。恩诺沙星-d5在20、50和100 ng/mL组的回收率平均值分别为92.1%、90.5%和90.1%, 相对标准偏差分别为6.1%、5.9%和4.5%。每个样品中内标化合物回收率乘以一个校准系数后获得其100%回收率。内标化合物峰强度和所有其他变量峰强度也乘以该校准系数获得校准后数值。在QC组和3个浓度组中删除峰强度相对标准偏差大于30%的变量后, 建立了867×39的数据矩阵进行下一步分析[32]。
2.2 多变量分析
2.2.1 PCA结果
如图2所示, 所有鱼肉样品的置信度都在95%范围内。QC组12个样品在原点处聚集, 表明本研究获取的数据质量高, 可以用于进一步分析[33]。同一质量浓度组的9个平行样品聚类明显, 其中20 ng/L质量浓度组样品聚集紧密程度明显高于50 ng/L和100 ng/L质量浓度组样品, 表明20 ng/L质量浓度组具有相对更好的组内相似性。3个质量浓度组在第一主成分轴上分离明显(图2), 表明3个质量浓度组之间存在差异显著的变量, 为在各浓度组之间寻找标志化合物奠定了基础。
2.2.2 聚类分析结果
聚类分析可以直接描述样本的相似程度, 即相似度最高的样本优先聚集到一起, 逐级集合, 直到所有样本都完成聚集。从图3中可以看出, 同一浓度组的样品与其他浓度组样品明显分离, 显示出良好的组内相似性。组间分离明显可归因于变量峰强度的差异。聚类分析结果也表明确实存在差异显著的变量。
2.2.3 OPLS-DA结果
OPLS-DA是一个二分类模型, 分类1和分类2位于OPLS-DA得分图中Y轴两侧。如图4所示, 分类1和分类2在第一主成分轴上明显分离, 说明两分类之间存在差异显著变量。R2Y和Q2是分别评价OPLS-DA模型沿Y轴可解释能力和可预测能力的两个关键参数[34-35]。当两个参数值均接近1时, 表明OPLS-DA模型具有良好的可靠性和可预测能力。在图4中, R2Y和Q2值都大于0.99, 表明本研究建立的OPLS-DA模型具有良好的稳健性。
S-plot图中每个点都代表一个变量(图5), 其中最有可能代表标志化合物的变量应位于该“S型”图两端附近, 这些变量在区分OPLS-DA模型中分类1和分类2的贡献更大, 置信度更高。根据上述原则, 应在图5(a)的“S型”图右端寻找低浓度组(20 ng/mL)的标志化合物, 而在图5(b)的“S型”图左端寻找高浓度组(100 ng/mL)的标志化合物。OPLS-DA模型是否过拟合则通过200次迭代置换检验所得指标Q2Y的显著性检验结果(P)进行判断[31]。如果P小于0.05, 对应置换检验图中Q2回归线的截距小于0.05, 表明OPLS-DA模型没有过拟合。如图6所示, 本研究所有OPLS-DA模型均满足上述条件, 因此所有OPLS-DA模型均都不存在过拟合。
计算变量的VIP, 可以用来评估变量的重要性。通常以VIP>1作为选择能够代表标志化合物的特征变量的关键阈值。根据上述原则, 本研究从20 ng/mL质量浓度组vs 50 ng/mL质量浓度组(以下简写为20vs50)分析中筛查出136个变量, 而从100 ng/mL质量浓度组vs 50 ng/mL质量浓度组(以下简写为100vs50)分析中筛查有效变量165个, 共计92个变量在两份VIP表单中发生重叠。
2.3 单变量分析
在代谢组学单变量分析中, 成对t检验[29-31]和浓度倍数变化[10,36]是最常用的两种分析方法, 前者以任意两个浓度组间变量峰强度是否存在显著性差异为焦点, 后者则关注任意两个浓度组间变量峰强度比值变化。通常选择同时满足成对t检验显著性水平(P)小于0.05和浓度倍数变化(fold change, FC)大于2的变量作为特征变量。通过计算VIP表单重叠的92个变量在20 ng/mL和50 ng/mL、50 ng/mL和100 ng/mL, 以及20 ng/mL和100 ng/mL质量浓度组间的P(即P20vs50、P50vs100和P20vs100), 结果发现均远低于0.05, 说明92个变量确实存在明显的组间差异。另外, 92个变量在50 ng/mL和20 ng/mL, 以及100 ng/mL和20 ng/mL组的浓度FC(分别为FC50vs20和FC100vs20)均大于2(表2), 进一步证实了筛查所得92个变量符合特征变量资格。
如表3所示, 代谢组学方法筛查出88个有效特征变量能够代表标志化合物(共计102个), 筛查率约为86%(即88×100%/102), 表明本研究建立的代谢组学分析方法在筛查具有标志化合物特征的残留兽药方面表现良好。噻菌灵、三氯苯哒唑、红霉素、罗红霉素、氟甲喹、萘啶酸、恶喹酸、萘丁美酮、托麦汀、卡洛芬、普萘洛尔、喷布洛尔、泼尼松和氟米龙等14种兽药虽然有匹配变量, 但在20vs50和100vs50分析中至少有一组VIP小于1, 故而未在VIP筛查表中。对于92个变量中未能代表标志化合物的4个变量(分别为M324T242、M615T582、M450T237和M449T383), 其代谢组学信息更为复杂, 需要深入分析判断其可能来源。
本研究采用同样的代谢组学方法对空白鱼肉样品进行了分析, 结果未发现102种兽药相对应的变量, 减少了鱼肉基质中固有的102种兽药对寻找标志化合物的干扰。
2.4 检出限
根据美国环境保护署提出的方法(Method 8061A)计算得到88种兽药的检出限。具体步骤如下: 2.0 g空白鱼肉样品经过介绍的前处理过程后得到1 mL提取液; 将88种兽药的混合溶液(1 µg/mL, 20 µL)加入到该提取液中, 此时溶液质量浓度为20 ng/mL; 重复上述过程, 得到7个平行样品; 进行代谢组学分析, 获取88种兽药的变量峰强度; 对于每种兽药, 20 ng/mL质量浓度水平对应于7个平行峰强度平均值, 因此, 每种兽药的质量浓度(ng/mL)可通过公式—各自的峰强度×20/平均峰强度计算获得。如表3所示, 88种农兽药的检出限结果在0.3~2.6 µg/kg。
2.5 方法实用性验证
应用本方法对5份美国进口鳕鱼和15份市场购买到的鲤鱼样品进行方法实用性验证, 结果发现1份鲤鱼样品中含有环丙沙星, 含量为18.2 μg/kg, 另外1份鲤鱼样品中则含有四环素, 含量为14.3 μg/kg, 其余18份样品均未检出兽药。有研究指出, 环境中抗生素因迁移作用可能蓄积在沉积物中, 而沉积物为鲤鱼提供了丰富的食物, 从而造成鲤鱼组织中具有较高的兽药残留水平[37-38]。据报道[39], 鲤鱼体内蓄积的兽药浓度较其他水位生活的鱼类高, 因此鲤鱼受到的兽药污染风险也相对较高, 这也是本研究选择鲤鱼作为验证对象的主要原因。
3 结论
收起
本研究以食用鱼类为研究对象, 采用代谢组学分析, 建立了一种兽药非靶向筛查方法。该方法可以有效识别102种兽药中的88种, 具有较高的筛查精度。该方法受鱼体内兽药残留水平影响显著, 对于浓度高于10 µg/kg的兽药具有良好的筛查效果, 但在低于该浓度时, 方法实用性尚不明朗, 给鱼类食品埋下安全隐患, 尤其是预料外的高风险兽药(限量小于10 µg/kg)被非法添加到鱼类产品中。将非靶向和靶向筛查有效结合可能是目前解决鱼类食品中兽药残留污染引发食品安全问题的有效途径。
通过代谢组学分析策略找到代表添加污染物所有可能的特征变量, 将其带入商品化数据库或自建数据库进行检索, 获取其可能身份, 再通过污染物标准品进一步确认其身份。本研究为了更快速地验证可能代表102种添加兽药的特征变量(即标志化合物)身份, 直接构建了102种兽药标准品的小型数据库进行分析, 从而准确识别特征变量身份, 这与直接以102种兽药标准品作为目标物, 并且只关注这些兽药的靶向分析方法是截然不同的。随着现代化学工业的快速发展, 越来越多的未知污染物可能进入食品中, 对这些污染物缺乏有效的认知, 势必会严重降低该污染物信息在商品化数据库中的更新速度, 从而影响代谢组学非靶向筛查结果, 这也是本研究所提出方法的局限性和不足。在今后的研究工作中, 如何有效解决该问题可能面临着更大的挑战。
基金
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  • 辽宁省自然科学基金面上项目(2023-MS-349)
  • 海关总署科技项目(2023HK124)
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doi: 10.19812/j.cnki.jfsq11-5956/ts.20240904005
  • 接收时间：2024-09-04
  • 首发时间：2025-07-19
  • 出版时间：2025-04-15
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  • 收稿日期：2024-09-04
基金
辽宁省自然科学基金面上项目(2023-MS-349)
海关总署科技项目(2023HK124)
作者信息
    1.大连海关技术中心, 大连 116600
    2.中国检验认证集团黑龙江有限公司, 哈尔滨 150000

通讯作者:

* 薛伟锋(1983—), 男, 博士, 主要研究方向为食品污染物非靶向筛查。E-mail:
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2种不同金属材料的力学参数
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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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