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Article(id=1217779721274839612, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1217779717386715826, articleNumber=null, orderNo=null, doi=10.19812/j.cnki.jfsq11-5956/ts.20250426002, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1745596800000, receivedDateStr=2025-04-26, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1768270910805, onlineDateStr=2026-01-13, pubDate=1750780800000, pubDateStr=2025-06-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1768270910805, onlineIssueDateStr=2026-01-13, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1768270910805, creator=13701087609, updateTime=1768270910805, updator=13701087609, issue=Issue{id=1217779717386715826, tenantId=1146029695717560320, journalId=1149652044408987649, year='2025', volume='16', issue='12', 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=null, createTime=1768270909877, creator=13701087609, updateTime=1768299620707, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1217900139386163208, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1217779717386715826, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1217900139386163209, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1217779717386715826, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=69, endPage=76, ext={EN=ArticleExt(id=1217779721715241546, articleId=1217779721274839612, tenantId=1146029695717560320, journalId=1149652044408987649, language=EN, title=Application progress of mass spectrometry imaging technology in visual detection of pesticide residues in food, columnId=1217529311867883548, journalTitle=Journal of Food Safety & Quality, columnName=Highlight: Analysis and Monitoring of Toxic and Harmful Substances in Food, runingTitle=null, highlight=null, articleAbstract=

Pesticide application is one of the effective measures to prevent crop diseases and insect pests in agricultural production. However, large-scale use of pesticides also brings food pesticide residues, which brings safety risks to human health. It is of great significance for food safety to clarify the spatial distribution and metabolic transfer pathway of pesticides in food. Traditional detection and analysis methods can only perform qualitative and quantitative analysis of pesticides, unable to directly observe their distribution in food. The emergence of mass spectrometry imaging technology has enabled visual analysis of pesticide residues in food, with advantages such as high sensitivity, high spatial resolution and ease of operation. It has become an important analytical tool for pesticide residue detection. This paper summarized the research progress of mass spectrometry imaging technology for pesticide residue detection in food over the past 5 years, both domestically and internationally. It focused on outlining the principles, characteristics and differences between various types of mass spectrometry imaging techniques. Additionally, it reviewed the application studies of mass spectrometry imaging technology in detecting pesticide residues in food (including different sources of food and different types of pesticides), finally analyzed the deficiencies and challenges of mass spectrometry imaging technology in pesticide residue detection, and proposed the future prospects. This paper aims to provide a reference for the research and innovation development of mass spectrometry imaging technology in pesticide residue detection.

, correspAuthors=Xiao-Bo ZHOU, 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=Xiao-Bo ZHOU), CN=ArticleExt(id=1217779722382135911, articleId=1217779721274839612, tenantId=1146029695717560320, journalId=1149652044408987649, language=CN, title=质谱成像技术在食品农药残留可视化检测中的应用进展, columnId=1217529312056627244, journalTitle=食品安全质量检测学报, columnName=本期重点：食品中有毒有害物质分析与监测, runingTitle=null, highlight=null, articleAbstract=

农药施用是农业生产中预防作物病虫害的有效措施之一, 但是大量且大规模的农药使用也带来了食品农药残留问题, 进而给人体健康带来安全隐患。明确农药在食品中的空间分布和代谢转移途径对于食品安全具有重大意义。传统的检测分析方法仅仅只能对农药进行定性和定量分析, 无法直接观察到农药在食品中的分布情况。质谱成像技术的出现实现了食品中农药残留的可视化分析, 且具有高灵敏度、高空间分辨率和操作简便等优点, 已经成为农药残留检测方面的重要分析工具。本文整理归纳了近5年国内外质谱成像技术用于食品中农药残留检测的研究进展, 重点概述了质谱成像技术的原理、特点及不同技术类型之间的差异性; 并综述了质谱成像技术在食品农药残留检测(包括不同食品来源及不同农药类型)中的应用研究。最后分析了质谱成像技术在农药残留检测中存在的不足和挑战, 并提出未来展望。本文旨在为质谱成像技术在农药残留检测方面的研究与创新发展提供参考。

, correspAuthors=周晓波, authorNote=null, correspAuthorsNote=
*周晓波(1969—), 男, 高级工程师, 主要研究方向为工程技术。E-mail:
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Analytica Chimica Acta, 2020, 1130: 10-19., articleTitle=Monitoring of adsorption and transfer of organochlorines in soybean seeds and sprouts with mass spectrometric imaging, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1217833920549273960, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217779721274839612, xref=null, ext=[AuthorCompanyExt(id=1217833920557662568, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217779721274839612, companyId=1217833920549273960, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Taian Institute for Food and Drug Control, Taian 271000, China), AuthorCompanyExt(id=1217833920561856873, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217779721274839612, companyId=1217833920549273960, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=泰安市食品药品检验检测研究院, 泰安 271000)])], figs=[ArticleFig(id=1217833923040690623, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217779721274839612, language=EN, label=Fig.1, caption=Schematic diagram of MSI[9], figureFileSmall=KB6SVMNOpFM0DyPVyrElYw==, figureFileBig=jYY5Qq/mA6JO3iiV3cUyzw==, tableContent=null), ArticleFig(id=1217833923179102666, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217779721274839612, language=CN, label=图1, caption=MSI技术操作图[9], figureFileSmall=KB6SVMNOpFM0DyPVyrElYw==, figureFileBig=jYY5Qq/mA6JO3iiV3cUyzw==, tableContent=null), ArticleFig(id=1217833923325903319, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217779721274839612, language=EN, label=Table 1, caption=

Comparison of the advantages, disadvantages and parameter differences of the 3 kinds of MSI technology

, figureFileSmall=null, figureFileBig=null, tableContent=
技术类型 离子化方式 空间分辨率 灵敏度 前处理条件 适用样品范围 参考文献
MALDI-MSI 借助基质激光解吸附电离 10~100 μm 高(fmol级) 样品切片并基质涂覆 大分子(蛋白质、多糖) [10-12]
SIMS 离子束轰击后二次离子溅射 1~100 nm 低(需高浓度或表面
富集)		 无需基质, 冷冻干燥
切片		 小分子、小肽、脂质 [13-15]
DESI-MSI 电喷雾电离 50~200 μm 中(pmol级) 无需基质, 含水或活体样本 小分子(<1000 Da) [16-17]
), ArticleFig(id=1217833923485286884, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1217779721274839612, language=CN, label=表1, caption=

3种MSI技术的优缺点和参数差异比较

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技术类型 离子化方式 空间分辨率 灵敏度 前处理条件 适用样品范围 参考文献
MALDI-MSI 借助基质激光解吸附电离 10~100 μm 高(fmol级) 样品切片并基质涂覆 大分子(蛋白质、多糖) [10-12]
SIMS 离子束轰击后二次离子溅射 1~100 nm 低(需高浓度或表面
富集)		 无需基质, 冷冻干燥
切片		 小分子、小肽、脂质 [13-15]
DESI-MSI 电喷雾电离 50~200 μm 中(pmol级) 无需基质, 含水或活体样本 小分子(<1000 Da) [16-17]
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质谱成像技术在食品农药残留可视化检测中的应用进展
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周晓波 *
食品安全质量检测学报 | 本期重点：食品中有毒有害物质分析与监测 2025,16(12): 69-76
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食品安全质量检测学报 | 本期重点：食品中有毒有害物质分析与监测 2025, 16(12): 69-76
质谱成像技术在食品农药残留可视化检测中的应用进展
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周晓波*
作者信息
  • 泰安市食品药品检验检测研究院, 泰安 271000

通讯作者:

*周晓波(1969—), 男, 高级工程师, 主要研究方向为工程技术。E-mail:
Application progress of mass spectrometry imaging technology in visual detection of pesticide residues in food
Xiao-Bo ZHOU*
Affiliations
  • Taian Institute for Food and Drug Control, Taian 271000, China
出版时间: 2025-06-25 doi: 10.19812/j.cnki.jfsq11-5956/ts.20250426002
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农药施用是农业生产中预防作物病虫害的有效措施之一, 但是大量且大规模的农药使用也带来了食品农药残留问题, 进而给人体健康带来安全隐患。明确农药在食品中的空间分布和代谢转移途径对于食品安全具有重大意义。传统的检测分析方法仅仅只能对农药进行定性和定量分析, 无法直接观察到农药在食品中的分布情况。质谱成像技术的出现实现了食品中农药残留的可视化分析, 且具有高灵敏度、高空间分辨率和操作简便等优点, 已经成为农药残留检测方面的重要分析工具。本文整理归纳了近5年国内外质谱成像技术用于食品中农药残留检测的研究进展, 重点概述了质谱成像技术的原理、特点及不同技术类型之间的差异性; 并综述了质谱成像技术在食品农药残留检测(包括不同食品来源及不同农药类型)中的应用研究。最后分析了质谱成像技术在农药残留检测中存在的不足和挑战, 并提出未来展望。本文旨在为质谱成像技术在农药残留检测方面的研究与创新发展提供参考。

质谱成像技术  /  农药残留检测  /  可视化

Pesticide application is one of the effective measures to prevent crop diseases and insect pests in agricultural production. However, large-scale use of pesticides also brings food pesticide residues, which brings safety risks to human health. It is of great significance for food safety to clarify the spatial distribution and metabolic transfer pathway of pesticides in food. Traditional detection and analysis methods can only perform qualitative and quantitative analysis of pesticides, unable to directly observe their distribution in food. The emergence of mass spectrometry imaging technology has enabled visual analysis of pesticide residues in food, with advantages such as high sensitivity, high spatial resolution and ease of operation. It has become an important analytical tool for pesticide residue detection. This paper summarized the research progress of mass spectrometry imaging technology for pesticide residue detection in food over the past 5 years, both domestically and internationally. It focused on outlining the principles, characteristics and differences between various types of mass spectrometry imaging techniques. Additionally, it reviewed the application studies of mass spectrometry imaging technology in detecting pesticide residues in food (including different sources of food and different types of pesticides), finally analyzed the deficiencies and challenges of mass spectrometry imaging technology in pesticide residue detection, and proposed the future prospects. This paper aims to provide a reference for the research and innovation development of mass spectrometry imaging technology in pesticide residue detection.

mass spectrometry imaging technology  /  pesticide residue detection  /  visualization
周晓波. 质谱成像技术在食品农药残留可视化检测中的应用进展. 食品安全质量检测学报, 2025 , 16 (12) : 69 -76 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20250426002
Xiao-Bo ZHOU. Application progress of mass spectrometry imaging technology in visual detection of pesticide residues in food[J]. Journal of Food Safety & Quality, 2025 , 16 (12) : 69 -76 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20250426002
正文
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0 引言
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农药是一类用于农作物病虫害防治的化学物质, 在植物保护领域发挥着不可替代的作用。农药的合理使用可以大大降低全球粮食生产损失, 为应对全球人口激增而引发的粮食危机做出了巨大贡献[1-2]。然而, 长期大量、大规模且不规范的使用农药, 同样带来了严重的食品污染和环境污染问题。人体长期摄入含有农药残留的食品会造成农药在体内的富集, 从而给人体神经或免疫系统带来损害[3], 而充分了解食品中残留农药的空间分布可以有效防止人体摄入这些有害物质。因此, 如何简单、高效且精确地检测食品农药残留情况, 对保障人身安全和社会稳定具有重要意义。
传统的检测方法(如液相色谱法、气相色谱法和气相/液相色谱-串联质谱法等)主要通过文本数据和图表形式表达检测结果, 在一定程度上已经实现对多组分农药的定性、定量的静态分析[4-5]。但此类方法存在样品前处理复杂、检测设备昂贵和无法可视化等局限性, 且无法判断农药或代谢产物的动态迁移或分布情况。质谱成像(mass spectrometry imaging, MSI)技术是一种新颖的分子检测技术, 该技术在兼具定性、定量化分析和高灵敏度检测的同时, 省去了样品复杂的前处理过程, 还可以实现目标分子的快速、精确和可视化分析[6]。相比于传统方法的结果呈现形式, MSI技术独有的可视化所提供的视觉图像往往比数字和图表给人带来更直观的印象[7]。质谱成像技术的兴起弥补了传统检测方法存在的缺陷, 在食品农药残留检测中发挥出重要作用, 对保障食品安全和公民健康具有重大意义。
本文综述了MSI技术的原理、特点、不同的技术类型及其在食品农药残留检测中的研究进展, 并指出了MSI技术在食品农药残留检测中存在的挑战, 旨在为MSI技术在食品安全检测的发展提供参考。
1 质谱成像技术概述
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1.1 质谱成像技术原理及优点
MSI技术一种将质谱分析与空间信息相结合的新技术, 其能够通过分子量或质荷比(m/z)以获取样本切片表面的分子。用质谱仪对样品表面进行逐点电离以获得每个点的信号强度、位置等空间数据, 再利用图像软件获得分子的二维图像以实现分子可视化[8]。MSI技术的具体操作流程如图1所示[9]。将待测样本包埋后冷冻切片处理, 再根据不同成像技术对切片进行前处理、数据采集和质谱分析, 最后通过成像分析软件生成可视化的分子分布热图。该技术无需标记, 且灵敏度高、适用分子量范围广, 在食品安全检测中表现出巨大应用潜力。
1.2 质谱成像技术类型
迄今为止, MSI技术在众多领域得到广泛应用, 其中主要的3种技术包括基质辅助激光解吸电离质谱成像(matrix assisted laser desorption ionization-mass spectrometry imaging, MALDI-MSI)技术、解吸电喷雾电离质谱成像(desorption electrospray ionization-mass spectrometry imaging, DESI-MSI)技术和二次离子质谱成像(secondary ion mass spectrometry imaging, SIMS)技术, 表1归纳总结了这3种MSI技术的电离方式、空间分辨率和灵敏度等参数的差异。
1.2.1 MALDI-MSI技术
MALDI-MSI技术是目前普遍使用到的MSI技术。在样品预处理时, 需先对切片涂覆一层均匀基质, 让基质与切片表面分子形成共结晶体, 最后通过激光照射实现待测分子的离子化并进入质谱仪检测[8]。XIANG等[10]采用MALDI-MSI技术, 将胡萝卜跟组织嵌入明胶溶液做冷冻切片处理, 然后以2,5-二羟基苯甲酸作为基质, 采用喷雾升华的方式对样品切片进行基质涂覆, 采用配备正离子模式激光装置的MALDI飞行时间质谱(355 nm, 1000 Hz, 激光功率20%)对切片进行高空间分辨率的MALDI成像(空间分辨率20 μm)。该方法成功实现了邻苯二甲酸二(2-乙基己基)酯[di(2-ethylhexyl) phthalate, DEHP]在胡萝卜组织中的原位检测。可视化了DEHP在胡萝卜皮层、韧皮部、后生木质部、形成层和原生木质部这些不同组织中的差异性分布, 并且还揭示了胡萝卜中糖和β-胡萝卜素的空间分布。ZHANG等[12]为了探究新烟碱类农药对蜜蜂的负面影响, 使蜜蜂在优化的切片温度(optimum cutting temperature, OCT)包埋后液氮冷冻切片处理, 再均匀喷涂α-氰基-4羟基肉桂酸(α-cyano-4hydroxycinnamic acid, CHCA)基质后, 利用MALDI-MSI技术成功观察到杀虫剂类农药N-硝基氨酸和乙酰胺在蜜蜂体内的分布成像及降解情况, 其图像空间分辨率为30 μm。该研究揭示了N-硝基氨酸和乙酰胺对蜜蜂的毒性作用主要归因于其在蜜蜂组织中的降解情况而非渗透差异。这一研究结果为更好地了解杀虫剂类农药对动物的危害和后续正确使用杀虫剂及合理优化杀虫剂配方提供了指导。
MALDI-MSI技术具有高灵敏度(可检测fmol级痕量分子)、高空间分布率(10~50 μm)和大分子兼容性等特点[18], 弥补了DESI-MSI和SIMS技术只能处理小分子化合物的局限性。在MALDI-MSI技术中, 基质是不可或缺的一部分, 基质的存在显著提高了化合物的电离效率, 有效避免了高强度激光照射使高分子化合物样品碎裂的风险[19]。常见的固体基质主要有1,5-二氨基萘(1,5-diaminonaphthalene, 1,5-DAN)[20]、2-巯基苯并噻唑(2-mercaptobenzothiazole, 2-MBT)[21]和9-氨基吖啶(9-aminoacridine, 9-AA)[22]等。如YUE等[20]采用MALDI-MSI技术对药食同源中药材当归进行原位检测, 选择1,5-DAN作为基质并喷涂在当归根切片表面以获得当归根中有机酸、氨基酸、低聚糖和磷脂等多种代谢物的详细空间分布信息, 同时参与胆碱生物合成的代谢途径也成功地被定位和可视化。但是上述这些固体基质在电离过程中会吸收激光能量从而产生很强的电离干扰, 尤其是在检测低分子量(<500 Da)区域的小分子时, 这种干扰对检测结果的准确性是致命的[10], 因此, 选择合适的基质直接决定了成像结果的准确性。
为了避免传统基质材料给检测结果带来的负面影响, 有研究提出了用纳米材料作为一种新型基质以减弱基质干扰[23]。如KUANG等[24]合成出Ti基MOF纳米片作为MALDI-MSI的基质, 使用气体辅助电动喷涂器将Ti基MOF纳米片溶液均匀沉积在具有何首乌根部切片的氧化铟锡(indium tin oxide, ITO)导电载玻片上。而后利用飞行时间质谱仪在负离子模式下, 以200 μm的空间分辨率采集了何首乌组织的MALDI图像, 成功揭示何首乌主要成分的空间分布特征及蒸制过程中何首乌中肝毒性成分的时空含量变化。Ti基MOF纳米片具有较少的背景干扰、显著的稳定性和高耐盐性, 该研究的成功应用提高了质谱成像在小分子领域的检测能力。类似的, KAWASAKI等[25]利用无机纳米颗粒Pt代替有机基质, 避免了基质衍生峰对低分子量分析物的信号干扰, 可视化了抗氧化剂Irganox 1098和有机染料结晶紫的空间分布及其光降解过程, 从而解决传统 MALDI-MSI技术的局限性。此外, 通过文献整理, 也有相关研究提出了无基质MSI技术, 同样避免了基质对检测结果的干扰。如KUWATA等[26]就提出了无基质激光解吸/电离方法(desorption ionization using through-hole alumina membrane, DIUTHAME)用于小鼠脑冷冻组织切片质谱成像的可行性研究。DIUTHAME芯片中的通孔氧化铝膜是通过湿法阳极氧化制得的, 其厚度为5 μm, 且具有200 nm通孔直径和50%开孔率。样品制备是将DIUTHAME 芯片覆盖在已有小鼠脑冷冻组织切片的ITO涂层载玻片上, 解冻并干燥后再用飞行时间质谱仪进行MSI实验。实验结果表明, 由于没有了基质衍生峰的干扰, DIUTHAME-MSI技术可以获得高质量的脑组织解剖结构图像(空间分辨率为50 μm)。该方法具有样品前处理过程简单、无基质干扰和重现性好的特点, 且DIUTHAME芯片中通孔的毛细作用还避免了分析物的位置信息失真。随后, IKEDA等[27]同样利用由玻璃珠形成的新型铂涂层多孔板进行样品制备, 不但省去了基质涂覆, 还可以实现重复的MSI检测操作。以上这些研究都为MALDI-MSI技术的高精确度检测和高质量成像发展提供了重要依据。
1.2.2 DESI-MSI技术
DESI-MSI技术是将带电溶剂在一定压力(3~5 kV)下通过毛细管形成雾化带电液滴撞击到样品表面, 溶解并解吸表面分子; 解吸分子与带电溶剂液滴结合, 形成携带目标分子的二次带电液滴; 随着液滴在干燥气流中地快速蒸发, 分子则通过电荷残留机制离子化, 最后进入质谱仪待检测[28]。AJITH等[16]使用具有加热入口的改进的商业DESI源, 其具有可以耦合到飞行时间质谱仪的高性能发射器盒。使用80:20 (V:V)甲醇/水和0.1%甲酸作为喷雾溶剂并辅助以0.5 kV的喷雾电位, 在0至1200的m/z范围内以2-D正离子模式扫描, 采集到的图像空间分辨率在100 μm。研究结果表明, DESI-MSI技术实现杀菌剂嘧菌酯从制剂施用到叶子上之后的2、24、56和168 h 4个时间点, 其在整个番茄叶的活性迁移率可视化, 通过对嘧菌酯钾加合物含量的监测, 观察到了嘧菌酯在木质部的移动性和侧层铺展性。WANG等[29]基于DESI-MSI, 利用装备有DESI源的Xevo G2-XS QTOF质谱仪在50-1500 m/z的质量范围内, 采用负离子模式和负正离子模式分别对人参皂苷和脂质成像, 获得了空间分辨率为200 μm的图像。该实验实现了对人参根中多部位的多种人生皂苷和脂质的可视化分析, 并在人参根茎中观察到高丰度的丙二酰和齐墩果酸-人参皂苷。该研究有助于了解生物活性分子在人参根部的积累, 这有利于其质量控制和合理使用。
DESI-MSI技术无需前处理, 简化了操作步骤, 适用范围以小分子(如农药、添加剂和脂质等)为主; 但在空间分辨率和灵敏度方面不及MALDI-MSI。为了解决DESI-MSI技术空间分辨率较低的问题, 大量研究实验成功将DESI-MSI的空间分辨率提升至纳米级, 从而拓宽了DESI-MSI技术在检测方面的精确度。如SANCHEZ等[30]使用纳米喷雾解吸电喷雾电离质谱成像(nanospray-desorption electrospray ionization mass spectrometry imaging, nano-DESI MSI)技术代替传统MSI技术以提高空间分辨率。将定制纳米DESI源(探针由两个150 μm OD×50 μm ID的熔融石英毛细管组成)连接到质谱仪, 在负离子模式下对样品切片表面恒速逐线扫描, 得到了空间分辨率约为10 μm的图像。结果表明, nano-DESI MSI技术成功检测到小鼠体内低丰度的非甾体抗炎药双氯芬酸及其代谢物在小鼠肝脏和肾脏组织的可视化空间分布, 并获得了相对定量数据。此外, BAO等[31]开发了等离子体激发雾化器气体辅助电喷雾电离(plasma-excited nebulizer gas-assisted electrospray ionization, PENG-ESI)技术以改善传统电喷雾电离法对低极性农药检测灵敏度低的问题。其方法是通过将特斯拉线圈形成的放电等离子体引入电喷雾雾化器气体通道, 在保持对极性农药灵敏度的同时, 显著提高了对低极性农药的灵敏度。最优条件下对S-生物丙烯菊酯的检出限达到100 pg/g, 具有良好的线性和精密度。NGUYEN等[32]为了扩大空间代谢组学覆盖范围, 设计了掺锂纳米喷雾解吸电喷雾电离技术(lithium-doped nanospray desorption electrospray ionization, Li-nano-DESI)。通过提高缺乏碱性基团的代谢物和脂质在正离子模式下的电离效率来增加空间代谢组学覆盖率。结果表明, 当锂添加到ESI溶剂中时, 可以将一些小分子化合物(如脂肪酸、中性脂质和磷脂等)的信号强度放大10~1000倍。通过验证实验可以发现, Li-nano-DESI能够全面地可视化前列腺素(prostaglandin, PG)生物合成途径中的代谢物和脂质, 并且可以区分和定位结构高度相似的PG异构体PGD2和PGE2。该技术的提出有利于MSI更全面地探测空间代谢组。
1.2.3 SIMS-MSI技术
SIMS-MSI技术主要是通过高能电子束(如Ar+、Au3+或Bi3+等[7])对样品表面轰击, 溅射出表面分子或原子(即二次离子)被电场加速并引入质量分析器进行分析[33]。SIMS在电离过程中, 由于从样品表面喷射和电离的分子较少, 并且在高能电子束轰击下离子碎裂程度较高[34], 因此在对大分子样品上的应用受到限制, 主要适合检测小分子、脂质及小肽等[35]。PASTERSKI等[36]采用飞行时间二次离子质谱技术(time-of-flight-secondary ion mass spectrometry, TOF-SIMS)检测火星样本中脂质生物标志物的空间分布, 在含量较低的样本中, 通过TOF-SIMS技术依旧检测到集中在特定区域的脂质生物标志物。YU等[37]同样利用TOF-SIMS技术, 在10-8 mbar的高真空环境下, 使用25 keV脉冲铋(Bi3+)初级离子束(脉冲电流0.54 pA, 重复频率10 kHz)对500 μm×500 μm的现场水样和200 μm×200 μm的参比化学品进行扫描。实现了对真实复杂水样中的全氟烷基物质和多氟烷基物质(per-and polyfluoroalkyl substances, PFAS)高分辨率的可视化检测识别。同时, PFAS中的两种典型化合物全氟戊酸(perfluoropentanoic acid, PFPeA)和全氟辛烷磺酸(perfluorooctanesulfonic acid, PFOS)的检出限分别达到28.0 mg/L和5.6 mg/L。
SIMS技术也避免了使用基质对切片样本预处理, 且具有极高的空间分辨率, 可以揭示纳米级别的微观结构。但其灵敏度较差, 对痕量分子检测困难, 往往要求高浓度或分子富集[9]。为了解决灵敏度差的问题, SHISHIDO等[38]提出了脂肪族羧酸(反式乌头酸和柠檬酸)基质对增强磷脂分子检测灵敏度的研究。研究发现将反式乌头酸和柠檬酸分别作为基质, 磷脂的二次离子产率分别比原始磷脂高了10~150倍和400~1000倍。这一研究表明脂肪族羧酸确实是增强SIMS在检测磷脂灵敏度方面的有效基质, 未来可以探索更多的基质以测试其检测的灵敏度。这一结果对改善SIMS的检测灵敏度具有重要意义。
1.2.4 新型MSI技术
随着时代的发展和技术的进步, 越来越多新型的MSI技术应运而生。这些新技术通常具有更高的灵敏度、空间分辨率和检测覆盖范围等。
WANG等[39]开发了一种新型的激光解吸定位质谱成像(laser desorption postionization mass spectrometry imaging, LDPI-MSI)技术以实现对胡萝卜块茎中多菌灵残留的可视化检测。该方法是采用两束不同的激光束(解吸激光束和电离激光束)同时发生解吸和电离, 一方面, LDPI-MSI中使用的电离激光器是真空紫外(vacuum ultraviolet, VUV)激光器, 其利用单光子能量实现目标分子的气化和电离, 形成的碎片峰较少, 实验准确度更高。另一方面, 相比于传统单一激光束的MALDI-MSI, 该技术简化了样品的前处理和操作步骤, 避免了基质干扰效应, 并且对小分子化合物也能实现精确检测, 获得了空间分辨率为70 µm的高质量图像。这项技术的出现可以为进一步验证各种生物制剂的有效性提供帮助。ZHAO等[40]利用气流辅助电离质谱成像(air flow-assisted ionization mass spectrometry imaging, AFAI-MSI)技术快速鉴定了芒果果肉中低分子代谢物的分布情况。使用与质谱仪偶联的AFAI离子源进行测量, 利用空气辅助电喷雾实现电离, 质谱在负离子模式下(70~1000 Da范围)对冻干切片后的芒果果肉扫描分析。该技术无需预处理样品、灵敏度高、代谢物检测覆盖面广, 在植物内源物质的快速鉴定和原位表征方面具有巨大潜力。除此以外, ZHAO等[41]采用水热法合成了复合材料(Fe3O4@hexagonal boron nitride nanosheets, Fe3O4@BN)作为无机基质用于表面辅助解吸/电离质谱(surface-assisted desorption/ionization mass spectrometry, SALDI-MS), 实现对废水和人血清中的各种合成抗氧化剂进行检测。合成的Fe3O4@BN基质具有更高的解吸/电离效率、良好的耐盐性和显著的信噪比改善, 图像的空间分辨率为100 µm。检测结果表明Fe3O4@BN复合材料在作为无机基质应用于实际检测时可以提供优秀的灵敏度和良好的检出限。该方案的提出为提高传统MSI技术的灵敏度和精准性以及在农药残留方面的检测提供了新思路。
以上研究表明, 不同的MSI技术在空间分辨率、灵敏度、适用样品范围及前处理的复杂性上具有明显差异和各自的优势, 新型MSI技术的问世更是提高了检测的适用性和广谱性。对不同应用场景选择合适的MSI技术, 可以大大提高检测效率和准确性, 更好地协助食品农药残留检测工作的顺利开展。
2 质谱成像技术在食品农药残留检测中的应用
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随着MSI技术的日益发展, 该技术已在生物医学[42-43]、环境监测[44]和食品安全评估[45]等诸多领域发挥出重要作用。MSI技术作为分子空间分布可视化的核心分析方法, 近年来在食品农药残留检测领域展现出较大优势。
2.1 不同食品来源中的农药残留检测
农药在植物源性食品(如水果、蔬菜、谷物和种子等)上的传递方式包括通过植物根部对土壤中农药的吸收、叶片或果实表皮喷药时通过表面孔道吸收及种子在萌发生长过程中吸收这几种方式[46-47], 且不同植物种类结构上的差异性也会导致农药在其内部的转移途径不同。此外, 农药在果蔬的不同组织中经不同代谢方式得到的代谢产物也存在差异性。而对动物源性食品(如肉类), 则主要通过环境水体、食用含农药的植物等途径造成肉类中农药残留。
上述问题的存在给人类健康带来安全隐患, 因此需要精准直观的检测技术检测其中的农药残留以确保食品安全。MSI技术实现了食品中残留农药的空间分布和代谢途径的可视化分析, 针对农药在动植物性食品中的差异性分布选择不同的MSI技术, 可以确保检测结果的精确性和可靠性。
蔬菜类食品种类繁多, 可食用部位也各不相同。明确农药在蔬菜各组织部位的分布情况有助于正确判断蔬菜的清洗方式和可食用部位。如JIANG等[41]采用AFAI-MSI技术, 以Thermo四极杆-静电场轨道阱高分辨率质谱仪连接AFAI离子源, 以高纯度氮气辅助电喷雾实现解吸电离, 质谱仪在正负离子模式下(50~750 m/z范围下)对豇豆截面切片进行成像扫描。该实验利用AFAI-MSI技术成功可视化了内吸性和非内吸性农药在豇豆中的转移途径, 从而确定了内吸性和非内吸性农药在豇豆中的差异性传播。随后, JIANG等[48]同样利用AFAI-MSI技术可视化评估了豇豆中两种农药唑虫酰胺和灭蝇胺的分布情况以及在2%碳酸氢钠溶液浸泡30 min和清水冲洗3 min后对二者的去除效果。结果表明, 碳酸氢钠溶液浸泡要比清水冲洗更有效地降低豇豆表面农药残留, 而内部农药浓度仍处于较高水平。但是浸泡会导致豇豆中营养成分的减少。YANG等[11]建立了一种优化后的MALDI-TOF-MSI成像技术。以α-氰基-4-羟基肉桂酸(α-cyano-4-hydroxycinnamic acid, CHCA)作为最佳基质, 将小麦叶片冲洗后直接固定到导电载玻片上, 滴加苯曲酮溶液, 待溶剂完全蒸发后涂覆基质, 随后直接用于MALDI-TOF-MSI分析。同时对施加不同浓度苯曲酮溶液的小麦叶片进行冷冻切片、基质涂覆和MSI分析, 以研究小麦叶片中苯曲酮渗透量的纵向分布。结果表明, 该MSI技术克服传统农药检测中耗时的非现场限制, 实现了对小麦叶片中农药苯曲酮残留的高灵敏度和高精准度的可视化(获得了空间分辨率为30 μm的高质量图像)和定量分析, 最优测试条件下实现了0.6 ng/mm2的检出限。
水果主要可以分为外皮可食用(如苹果、草莓和梨等)和外皮不可食用(如香蕉、石榴和芒果等)两类。准确地判断农药是仅残留于水果表皮还是会渗透进内部果肉, 有助于为后续水果生产加工提供理论支撑。WANG等[49]利用MSI技术对不同成熟度香蕉中福美双、霜霉威、吡虫啉和唑菌胺酯4种农药的迁移模式进行研究, 以探究内吸性和非内吸性农药在香蕉中的迁移。结果表明非内吸性农药只会存在于果皮表面, 内吸性农药在6 h后通过横向渗透和垂直迁移进入会向香蕉内部渗透, 这与JIANG等[48]在豇豆中的农药迁移模式研究相同。此外, 研究还发现随着香蕉成熟度的增加, 农药迁移速度也会加快。LIMA等[50]通过激光烧蚀电喷雾电离质谱成像(laser ablation electrospray ionization mass spectrometry imaging, LAESI-MSI)和组织喷雾电离质谱(tissue spray ionization mass spectrometry imaging, TSI-MSI)两种技术对苹果和番石榴中噻菌灵的分布情况进行分析。简言之, 将农药处理过的水果和未处理过的对照组切片处理, LAESI-MSI分析是利用中红外激光源与Q Exactive混合四极杆-轨道阱质谱仪结合, 激光器发出激光照射样品, 并且在位于模型上方的纳米电喷雾源的辅助下将被照射的材料解吸和电离。TSI-MSI分析是利用装有TSI源的Q Exactive混合四极杆-轨道阱质谱仪, 实现切片材料的解吸和电离。实验结果通过可视化图像(两种方法的图像空间分辨率均为200 μm)证实了对果皮进行清洗和削皮可以降低人体对农药的摄入量。
以上研究均证明了MSI技术在水果农药残留检测方面的强大能力。MSI技术不仅解决了传统检测方法无法区分农药在果蔬中的具体分布情况; 同时, 还可以追踪内吸性农药在果蔬中的迁移途径, 对果蔬清洗加工前后农药残留变化有了更直观准确的判断。通过文献整理发现, 将MSI技术用于动物源性食品中农药残留检测的相关报道相对较少, 后续研究可以考虑肉、蛋、奶类食品中的农药残留检测, 以提高MSI技术在食品检测领域的广泛性。
2.2 食品中不同用途农药的残留检测
针对不同的防治目的, 农药可以分为杀菌剂、杀虫剂、除草剂和生长调节剂等。
生长调节剂可以对植物的生长发育起到促进作用, 但当其进入到作物内部并富集后, 人体长期食用带有生长调节剂的食物可能会引起人体慢性中毒。因此, 对生长调节剂的检测不可忽视。ZHANG等[17]采用DESI-MSI技术对拟南芥中低分子量的植物生长激素实现高通量、可视化分析。相比于传统液相分析方法, 该技术无需标记、成本低、表现直观, 非常适用于低分子量目标物质的痕量检测。类似的, WANG等[51]通过MALDI-MSI技术对东方甜瓜中痕量的植物生长调节剂氯吡脲的时间依赖性渗透和降解位点进行可视化检测。将冷冻后的甜瓜样品切片处理并固定到载玻片上解冻, 以CHCA作为最佳基质, 采用“两步基质施用”(即升华与喷涂步骤相结合)处理切片以提高灵敏度, 使用具有大气压MALDI的混合离子阱飞行时间质谱仪以及二极管泵浦的固态激光器进行质谱成像, 图像分辨率为20 μm。可视化图像结果显示大部分氯吡脲在甜瓜的外果皮和中果皮区域检测到, 并在施用后2 d出现明显下降。这表明基于MALDI-MSI技术的检测方法对于农药降解研究是可靠且实用的。
杀菌剂往往需要渗透到植物体内部才能实现有效杀菌。利用MSI技术可以可视化杀菌剂在植物体内部的具体分布, 以便于后续的针对性去除。SHIMMA等[52]以基质CHCA对冷冻切片后的玉米和大豆种子进行涂覆, 结合MALDI-MSI技术来可视化杀菌剂噻唑菌胺在玉米和大豆种子中的分布, 获得了空间分辨率为50 μm的图像。同时还获得了播种后杀菌剂在种子中的空间分布信息, 以便更好地了解植物内杀菌剂的动态传递途径。IKUTA等[53]利用MALDI-MSI技术观察杀菌剂嘧菌酯从叶子表皮渗透到内部组织的情况, 并确定了其在小麦内部组织的具体积聚位置。
除草剂和杀虫剂同样会通过食物在人体中富集, 威胁人体健康。KUBICKI等[54]借助MALDI-MSI技术可视化分析了玉米叶片中除草剂噻吩卡巴脲甲基(thiencarbazone-methyl, TCM)和安全剂环丙磺酰胺(cyprosulfamide, CSA)在施用到玉米叶片后二者之间的相互作用关系, 并观察4个时间节点下的动态分布和代谢, 以确定安全剂CSA处理对玉米叶片的影响。质谱成像结果证实了当安全剂CSA和除草剂TCM处于同一组织部位时, 安全剂CSA的可靠性是显著的。该研究结论也为进一步证实其他农药与安全剂之间的相互作用关系提供了依据。YANG等[55]利用MSI技术完成了对有机氯农药二氯二苯基三氯乙烷(dichlorodiphenyltrichloroethane, DDT)在大豆种子和豆芽生长过程中的动态迁移、分布、生物积累和生物转化的监测。该实验以半导体纳米颗粒薄膜(Bi2O3)0.07(CoO)0.03(ZnO)0.9作为基质对冷冻切片样本进行预处理, 质谱数据采集和成像是在配备有MALDI电离源的质谱仪上进行的, 最终获得了空间分辨率为80 μm的高质量图像。同时, 该研究也进一步为外源性农药残留和内源性代谢产物的原位研究提供了新的途径。
农药种类繁多, 功能各异, 不同功效的农药在食品中的传播渗透方式和部位也大不相同(如水溶性除草剂易扩散到多汁的水果中, 而脂溶性的则更容易富集到脂肪中; 内吸性杀虫剂通常在食物内部残留, 非内吸性的杀菌剂则主要分布在表皮)。利用MSI技术可视化分析不同类型农药的传播方式和残留部位, 有助于改进农药施用和食品加工方式, 保障消费者的生命安全。我国作为农业生产大国, 食品安全问题关乎我国的国际形象, MSI技术以其独特的优势, 已成为食品中农药残留检测分析的强大工具。该技术不仅可以准确定位农药在食品中的残留分布情况, 也为进一步对农药的迁移转运途径和代谢产物的组学研究奠定基础。
3 MSI技术在食品农药残留检测中的不足与挑战
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MSI技术作为一种很有前途的检测分析技术, 其可视化成像的独特优势为食品中农药残留的准确定位及代谢途径分析提供了巨大帮助。然而, 该技术在食品农药残留检测方面仍存在诸多挑战。
在样本前处理阶段, 实际样本中大量的基质干扰(如色素、有机酸和多糖等)会对电离效果造成影响。因此, 如何最大程度降低基质干扰而又不破坏农药在组织样本中的原始分布是MSI技术发展的一大挑战。此外, MALDI-MSI技术是食品中农药残留检测的主要方法, 而基质的背景干扰和喷涂方式的均一性会对成像结果有显著影响。目前虽已开发出大量的无机材料代替传统有机基质及自动涂覆设备改善喷涂均一性, 但仍存在材料制备复杂和设备昂贵等问题亟待解决。同时, 灵敏度和空间分辨率仍旧是MSI技术改进的重点, 这二者直接决定了组织样本中痕量目标分析物的准确定位及高质量成像。一味的提高空间分辨率会延长检测时间, 从而出现待测物降解的可能性。所以, 开发出兼顾空间分辨率、灵敏度和检测效率的检测技术是MSI技术发展的方向之一。最后, 优化成像软件并建立完善的数据库为后续研究提供参考, 这对MSI技术的快速、可持续性发展同样是有帮助的。
4 结束语
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MSI技术作为一种新兴的分析检测技术, 在食品农药残留和代谢产物的空间分布及转运迁移检测分析中应用广泛, 并有助于进一步剖析其内在机制。较传统的液相色谱-质谱技术, MSI技术可提供直观且高空间分辨率的分子空间分布信息, 避免了复杂的样品前处理和流动相的使用, 使其在农药残留检测中具有不可替代的地位。在未来, MSI技术仍有巨大的提升空间, 如开发新型的基质解决基质在检测过程中的杂峰干扰问题; 在保证灵敏度的同时, 增强空间分辨率, 以实现更快更准确的质谱检测; 将MSI技术应用到动物源性食品的农药残留检测中, 以增强MSI技术的实际应用性。尽管面临挑战, MSI技术仍在不断革新以保障食品安全问题。相信未来MSI技术将会有更大的突破, 并为更多领域提供新的有价值的见解。
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2025年第16卷第12期
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doi: 10.19812/j.cnki.jfsq11-5956/ts.20250426002
  • 接收时间：2025-04-26
  • 首发时间：2026-01-13
  • 出版时间：2025-06-25
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  • 收稿日期：2025-04-26
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    泰安市食品药品检验检测研究院, 泰安 271000

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*周晓波(1969—), 男, 高级工程师, 主要研究方向为工程技术。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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