Article(id=1153986582429295270, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1153986579971429187, articleNumber=null, orderNo=null, doi=10.19812/j.cnki.jfsq11-5956/ts.20241211004, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1733846400000, receivedDateStr=2024-12-11, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1753061441285, onlineDateStr=2025-07-21, pubDate=1740412800000, pubDateStr=2025-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1753061441285, onlineIssueDateStr=2025-07-21, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1753061441285, creator=13701087609, updateTime=1753061441285, updator=13701087609, issue=Issue{id=1153986579971429187, tenantId=1146029695717560320, journalId=1149652044408987649, year='2025', volume='16', issue='4', pageStart='1', pageEnd='320', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1753061440699, creator=13701087609, updateTime=1758783495950, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1177986619249406427, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1153986579971429187, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1177986619249406428, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1153986579971429187, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=62, endPage=70, ext={EN=ArticleExt(id=1153986583217824429, articleId=1153986582429295270, tenantId=1146029695717560320, journalId=1149652044408987649, language=EN, title=Detection of the deoxynivalenol in cereals by polydopamine-based lateral flow immunochromatography assay, columnId=1153429495274000613, journalTitle=Journal of Food Safety & Quality, columnName=Special Topic: Research and Detection of Pesticide and Veterinary Drug Residue, runingTitle=null, highlight=null, articleAbstract=

Objective To prepare a polydopamine (PDA)-based lateral flow immunochromatography assay for the highly sensitive detection of deoxynivalenol (DON) in cereals. Methods In this paper, PDA was synthesized by dopamine self-oxidative polymerization, and the PDA labeled monoclonal antibody (PDA-mAb) probe was prepared by a one-step conjugation method. Subsequently, the PDA lateral flow immunochromatography assay was established using DON antigen as the test line (T line) and goat anti-mouse antibody as the control line (C line). Results The results showed that the cut-off value of the PDA lateral flow immunochromatography assay for detecting DON was 6.0 ng/mL, the visual limit of detection was 0.9 ng/mL, half maximal inhibitory concentration was 1.13 ng/mL, which were 0.88 times and 3.08 times higher than those of fluorescent microsphere test strip and colloidal gold test strip, respectively. Meanwhile, the PDA lateral flow immunochromatography assay possessed well stability and specificity. In addition, the proposed lateral flow immunochromatography assay has been successfully used for the detection of DON in corn, millet, and oats samples, with minimum detection limits of 8.40, 6.87, and 9.89 μg/kg, recoveries ranging from 80.10%-122.05%, and relative standard deviations less than 12.01%. Conclusion In summary, the PDA lateral flow immunochromatography assay established in this work is sensitive, accurate, rapid and simple, and provides technical support for the detection of mycotoxins in cereals.

, correspAuthors=Xi-Ya ZHANG, 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=Xiu-Mei TIAN, Xiao-Yang CHEN, Tian-Bao YANG, Chang-Wei LUO, Ye-Xuan MAO, Tong BU, Xi-Ya ZHANG), CN=ArticleExt(id=1153986608782106965, articleId=1153986582429295270, tenantId=1146029695717560320, journalId=1149652044408987649, language=CN, title=聚多巴胺侧流免疫层析法检测谷物中呕吐毒素, columnId=1153429495479521513, journalTitle=食品安全质量检测学报, columnName=本期专题:农兽药残留研究与检测, runingTitle=null, highlight=null, articleAbstract=

目的 制备一种基于聚多巴胺(polydopamine, PDA)纳米载体的免疫层析试纸条, 用于高灵敏检测谷物中的呕吐毒素(deoxynivalenol, DON)。方法 本研究通过多巴胺自氧化聚合制备PDA, 一步结合法制备PDA-单克隆抗体(monoclonal antibody, PDA-mAb)探针, 以呕吐毒素抗原为检测线(T线)、羊抗鼠二抗作为质控线(C线)建立PDA免疫层析试纸条。结果 该试纸条检测DON的消线值为6.0 ng/mL, 视觉检出限为0.9 ng/mL, IC50=1.13 ng/mL, 比荧光微球试纸条和胶体金试纸条分别高0.88倍和3.08倍, 且该试纸条稳定性与特异性良好。此外, 该试纸条已经成功用于玉米、小米和燕麦样品中DON的检测, 最低检出限分别为8.40、6.87、9.89 μg/kg, 回收率为80.10%~122.05%, 相对标准偏差均小于12.01%。结论 本研究建立的PDA免疫层析试纸条具有灵敏、准确、快速和简便等特点, 为谷物中真菌毒素的检测提供技术支撑。

, correspAuthors=张西亚, authorNote=null, correspAuthorsNote=
* 张西亚(1987—), 男, 博士, 副教授, 主要研究方向为食品安全快速检测。E-mail:
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田秀梅(1987—), 女, 硕士, 主要研究方向为食品安全检测。E-mail:

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田秀梅(1987—), 女, 硕士, 主要研究方向为食品安全检测。E-mail:

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Molecules, 2020, 25(1): 50., articleTitle=Development of a direct competitive ELISA kit for detecting deoxynivalenol contamination in wheat, refAbstract=null), Reference(id=1177985578894246860, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986582429295270, doi=null, pmid=null, pmcid=null, year=2023, volume=14, issue=8, pageStart=187, pageEnd=194, url=null, language=null, rfNumber=[28], rfOrder=28, authorNames=张颖, 刘洪美, 李丽, journalName=食品安全质量检测学报, refType=null, unstructuredReference=张颖, 刘洪美, 李丽, 等. 基于金纳米棒金属化的呕吐毒素多色可视化检测方法研究[J]. 食品安全质量检测学报, 2023, 14(8): 187-194., articleTitle=基于金纳米棒金属化的呕吐毒素多色可视化检测方法研究, refAbstract=null), Reference(id=1177985578957161421, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986582429295270, doi=null, pmid=null, pmcid=null, year=2023, volume=14, issue=8, pageStart=187, pageEnd=194, url=null, language=null, rfNumber=[28], rfOrder=29, authorNames=ZHANG Y, LIU HM, LI L, journalName=Journal of Food Safety & Quality, refType=null, unstructuredReference=ZHANG Y, LIU HM, LI L, et al. 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Science of the Total Environment, 2022, 834: 155354., articleTitle=Sensitive, simultaneous and quantitative detection of deoxynivalenol and fumonisin B1 in the water environment using lateral flow immunoassay integrated with smartphone, refAbstract=null), Reference(id=1177985579133322191, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986582429295270, doi=null, pmid=null, pmcid=null, year=2023, volume=10, issue=null, pageStart=1142820, pageEnd=null, url=null, language=null, rfNumber=[30], rfOrder=31, authorNames=WANG J, WANG L, ZHANG H, journalName=Frontiers in Veterinary Science, refType=null, unstructuredReference=WANG J, WANG L, ZHANG H, et al. Development of a time-resolved immunochromatographic strip for rapid and quantitative determination of deoxynivalenol[J]. Frontiers in Veterinary Science, 2023, 10: 1142820., articleTitle=Development of a time-resolved immunochromatographic strip for rapid and quantitative determination of deoxynivalenol, refAbstract=null), Reference(id=1177985579208819664, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986582429295270, doi=null, pmid=null, pmcid=null, year=2024, volume=164, issue=null, pageStart=110585, pageEnd=null, url=null, language=null, rfNumber=[31], rfOrder=32, authorNames=GIRMATSION M, TANG X, ZHANG Q, journalName=Food Control, refType=null, unstructuredReference=GIRMATSION M, TANG X, ZHANG Q, et al. Phycocyanin-based rapid fluorometric immunoassay for the determination of aflatoxin B1, deoxynivalenol, and zearalenone in food and feed matrices[J]. Food Control, 2024, 164: 110585., articleTitle=Phycocyanin-based rapid fluorometric immunoassay for the determination of aflatoxin B1, deoxynivalenol, and zearalenone in food and feed matrices, refAbstract=null), Reference(id=1177985579280122833, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986582429295270, doi=null, pmid=null, pmcid=null, year=2017, volume=65, issue=36, pageStart=8063, pageEnd=8071, url=null, language=null, rfNumber=[32], rfOrder=33, authorNames=ZHANG X, YU X, WEN K, journalName=Journal of Agricultural and Food Chemistry, refType=null, unstructuredReference=ZHANG X, YU X, WEN K, et al. Multiplex lateral flow immunoassays based on amorphous carbon nanoparticles for detecting three fusarium mycotoxins in maize[J]. Journal of Agricultural and Food Chemistry, 2017, 65(36): 8063-8071., articleTitle=Multiplex lateral flow immunoassays based on amorphous carbon nanoparticles for detecting three fusarium mycotoxins in maize, refAbstract=null), Reference(id=1177985579351426002, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986582429295270, doi=null, pmid=null, pmcid=null, year=2018, volume=11, issue=null, pageStart=2569, pageEnd=2578, url=null, language=null, rfNumber=[33], rfOrder=34, authorNames=ZHANG X, YU X, WANG J, journalName=Food Analytical Methods, refType=null, unstructuredReference=ZHANG X, YU X, WANG J, et al. One-step core/multishell quantum dots-based fluoroimmunoassay for screening of deoxynivalenol in maize[J]. Food Analytical Methods, 2018, 11: 2569-2578., articleTitle=One-step core/multishell quantum dots-based fluoroimmunoassay for screening of deoxynivalenol in maize, refAbstract=null)], funds=[Fund(id=1177985575232619438, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986582429295270, awardId=32172298, language=CN, fundingSource=国家自然科学基金项目(32172298), fundOrder=null, country=null), Fund(id=1177985575333282735, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986582429295270, awardId=2023M741056, language=CN, fundingSource=中国博士后科学基金资助项目(2023M741056), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1177985569767441224, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986582429295270, xref=null, ext=[AuthorCompanyExt(id=1177985569780024135, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986582429295270, companyId=1177985569767441224, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1. 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注: A. 透射电镜200 nm; B. 透射电镜50 nm; C. 紫外吸收光谱表征; D. Zeta电位表征。

, figureFileSmall=9/QIMldSFJB67RWFmUEYiA==, figureFileBig=3b+Y7pdXadq6WQbKkmCvXA==, tableContent=null), ArticleFig(id=1177985574322455460, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986582429295270, language=EN, label=Fig.3, caption=Optimization results of PDA-LFIA, figureFileSmall=Hv6kiBVmAtgvqGg3jR4+jA==, figureFileBig=csvT1zekhiCZLPKXwlqosQ==, tableContent=null), ArticleFig(id=1177985574389564325, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986582429295270, language=CN, label=图3, caption=聚多巴胺免疫层析条件优化结果

注: A. PDA浓度优化; B. 抗原浓度优化; C. 抗体添加量优化; D. 探针添加量优化。抑制率(inhibition ratio, IR), %; 条形图上方为不同优化参数对应的0、1.8、6.0 ng/mL的DON试纸条检测结果图。

, figureFileSmall=Hv6kiBVmAtgvqGg3jR4+jA==, figureFileBig=csvT1zekhiCZLPKXwlqosQ==, tableContent=null), ArticleFig(id=1177985574519587750, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986582429295270, language=EN, label=Fig.4, caption=Performance assay results of PDA-LFIA、QDMs-LFIA and AuNPs-LFIA, figureFileSmall=nAfVuXBU5T8Z3NJQ3OsRrw==, figureFileBig=dFEeQXh8ZqxbaF4409IDhg==, tableContent=null), ArticleFig(id=1177985574578308007, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986582429295270, language=CN, label=图4, caption=PDA-LFIA、QDMs-LFIA和AuNPs-LFIA检测性能测定结果

注: A. PDA-LFIA检测DON标准曲线, 图上方为PDA-LFIA检测不同浓度DON的结果图; B. QDMs-LFIA检测DON标准曲线, 图上方为QDMs-LFIA检测不同浓度DON的结果图; C. AuNPs-LFIA检测DON标准曲线, 图上方为AuNPs-LFIA检测不同浓度DON的结果图; D. DON与常见真菌毒素的特异性检测结果, 图上方为PDA-LFIA检测不同真菌毒素的结果图。

, figureFileSmall=nAfVuXBU5T8Z3NJQ3OsRrw==, figureFileBig=dFEeQXh8ZqxbaF4409IDhg==, tableContent=null), ArticleFig(id=1177985574645416872, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986582429295270, language=EN, label=Fig.5, caption=Recovery rate and coefficient of variation of DON in maize, millet and oats samples detected by PDA-LFIA, figureFileSmall=u5jf9HJOt7KfHrs6Peik+Q==, figureFileBig=5srge5az9XJEAhhgpDKpPw==, tableContent=null), ArticleFig(id=1177985574708331433, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986582429295270, language=CN, label=图5, caption=PDA-LFIA检测玉米、小米和燕麦样品中DON的回收率和变异系数, figureFileSmall=u5jf9HJOt7KfHrs6Peik+Q==, figureFileBig=5srge5az9XJEAhhgpDKpPw==, tableContent=null), ArticleFig(id=1177985574796411818, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986582429295270, language=EN, label=Table 1, caption=

Comparison of the performance of different methods for detecting DON

, figureFileSmall=null, figureFileBig=null, tableContent=
方法 线性范围 缓冲溶液
IC50/LOD/(ng/mL)
实际样品
LOD/(μg/kg)
参考文献
适配体传感器 6.25~125 ng/mL 2.5 20 [23]
SERS 50~10000 ng/mL 24.8 / [24]
荧光法 1~500 ng/mL 0.64 / [25]
ELISA 5~135 μg/kg / 5 [26]
ELISA 1~113.24 ng/mL 6.61 62 [27]
金纳米棒比色法 0~1000 ng/mL 264.71 / [28]
AuNPs-LFIA 3.46~43.64 ng/mL 3.46 / [29]
TRFIA 50~10000 μg/kg / 28.16 [30]
AuNPs-LFIA 1~65/1~75/1.5~85 ng/mL / 2.20/6.45/2.90 [31]
AuNPs-LFIA 2.5~80 ng/mL 11.1 / [32]
ACNPs-LFIA 0.625~20 ng/mL 2.4 20 [32]
QDs-LFIA 0.625~20 ng/mL 2.8 / [32]
QDMs-LFIA 0.62~5.62 ng/mL 1.76 / 本研究
PDA-LFIA 0.33~3.75 ng/mL 1.13 8.4 本研究
), ArticleFig(id=1177985574871909291, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986582429295270, language=CN, label=表1, caption=

不同方法检测DON的性能比较

, figureFileSmall=null, figureFileBig=null, tableContent=
方法 线性范围 缓冲溶液
IC50/LOD/(ng/mL)
实际样品
LOD/(μg/kg)
参考文献
适配体传感器 6.25~125 ng/mL 2.5 20 [23]
SERS 50~10000 ng/mL 24.8 / [24]
荧光法 1~500 ng/mL 0.64 / [25]
ELISA 5~135 μg/kg / 5 [26]
ELISA 1~113.24 ng/mL 6.61 62 [27]
金纳米棒比色法 0~1000 ng/mL 264.71 / [28]
AuNPs-LFIA 3.46~43.64 ng/mL 3.46 / [29]
TRFIA 50~10000 μg/kg / 28.16 [30]
AuNPs-LFIA 1~65/1~75/1.5~85 ng/mL / 2.20/6.45/2.90 [31]
AuNPs-LFIA 2.5~80 ng/mL 11.1 / [32]
ACNPs-LFIA 0.625~20 ng/mL 2.4 20 [32]
QDs-LFIA 0.625~20 ng/mL 2.8 / [32]
QDMs-LFIA 0.62~5.62 ng/mL 1.76 / 本研究
PDA-LFIA 0.33~3.75 ng/mL 1.13 8.4 本研究
), ArticleFig(id=1177985574985155500, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986582429295270, language=EN, label=Table 2, caption=

Determination of precision of PDA-LFIA (n=3)

, figureFileSmall=null, figureFileBig=null, tableContent=
添加质量浓度/(ng/mL) 测定质量浓度/(ng/mL) 变异系数/%
批次内 1 0.74±0.05 6.56
2 1.80±0.14 7.57
3 2.67±0.01 0.54
批次间 1 0.71±0.01 1.36
2 1.72±0.07 4.08
3 2.87±0.10 3.56
), ArticleFig(id=1177985575073235885, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986582429295270, language=CN, label=表2, caption=

PDA-LFIA精密度的测定(n=3)

, figureFileSmall=null, figureFileBig=null, tableContent=
添加质量浓度/(ng/mL) 测定质量浓度/(ng/mL) 变异系数/%
批次内 1 0.74±0.05 6.56
2 1.80±0.14 7.57
3 2.67±0.01 0.54
批次间 1 0.71±0.01 1.36
2 1.72±0.07 4.08
3 2.87±0.10 3.56
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聚多巴胺侧流免疫层析法检测谷物中呕吐毒素
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田秀梅 1 , 陈晓阳 2 , 杨天宝 3 , 罗昌伟 2 , 毛烨炫 2 , 补彤 2 , 张西亚 2, *
食品安全质量检测学报 | 本期专题:农兽药残留研究与检测 2025,16(4): 62-70
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食品安全质量检测学报 | 本期专题:农兽药残留研究与检测 2025, 16(4): 62-70
聚多巴胺侧流免疫层析法检测谷物中呕吐毒素
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田秀梅1 , 陈晓阳2, 杨天宝3, 罗昌伟2, 毛烨炫2, 补彤2, 张西亚2, *
作者信息
  • 1.北京美正生物科技有限公司, 北京 102200
  • 2.河南农业大学食品科学技术学院, 郑州 450002
  • 3.新乡医学院三全学院, 新乡 543000
  • 田秀梅(1987—), 女, 硕士, 主要研究方向为食品安全检测。E-mail:

通讯作者:

* 张西亚(1987—), 男, 博士, 副教授, 主要研究方向为食品安全快速检测。E-mail:
Detection of the deoxynivalenol in cereals by polydopamine-based lateral flow immunochromatography assay
Xiu-Mei TIAN1 , Xiao-Yang CHEN2, Tian-Bao YANG3, Chang-Wei LUO2, Ye-Xuan MAO2, Tong BU2, Xi-Ya ZHANG2, *
Affiliations
  • 1. Beijing Meizheng Bio-Tech Co., Ltd., Beijing 102200, China
  • 2. School of Food Science and Technology, Henan Agricultural University, Zhengzhou 450002, China
  • 3. Sanquan College of Xinxiang Medical College, Xinxiang 543000, China
出版时间: 2025-02-25 doi: 10.19812/j.cnki.jfsq11-5956/ts.20241211004
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目的 制备一种基于聚多巴胺(polydopamine, PDA)纳米载体的免疫层析试纸条, 用于高灵敏检测谷物中的呕吐毒素(deoxynivalenol, DON)。方法 本研究通过多巴胺自氧化聚合制备PDA, 一步结合法制备PDA-单克隆抗体(monoclonal antibody, PDA-mAb)探针, 以呕吐毒素抗原为检测线(T线)、羊抗鼠二抗作为质控线(C线)建立PDA免疫层析试纸条。结果 该试纸条检测DON的消线值为6.0 ng/mL, 视觉检出限为0.9 ng/mL, IC50=1.13 ng/mL, 比荧光微球试纸条和胶体金试纸条分别高0.88倍和3.08倍, 且该试纸条稳定性与特异性良好。此外, 该试纸条已经成功用于玉米、小米和燕麦样品中DON的检测, 最低检出限分别为8.40、6.87、9.89 μg/kg, 回收率为80.10%~122.05%, 相对标准偏差均小于12.01%。结论 本研究建立的PDA免疫层析试纸条具有灵敏、准确、快速和简便等特点, 为谷物中真菌毒素的检测提供技术支撑。

聚多巴胺  /  呕吐毒素  /  侧流免疫层析检测

Objective To prepare a polydopamine (PDA)-based lateral flow immunochromatography assay for the highly sensitive detection of deoxynivalenol (DON) in cereals. Methods In this paper, PDA was synthesized by dopamine self-oxidative polymerization, and the PDA labeled monoclonal antibody (PDA-mAb) probe was prepared by a one-step conjugation method. Subsequently, the PDA lateral flow immunochromatography assay was established using DON antigen as the test line (T line) and goat anti-mouse antibody as the control line (C line). Results The results showed that the cut-off value of the PDA lateral flow immunochromatography assay for detecting DON was 6.0 ng/mL, the visual limit of detection was 0.9 ng/mL, half maximal inhibitory concentration was 1.13 ng/mL, which were 0.88 times and 3.08 times higher than those of fluorescent microsphere test strip and colloidal gold test strip, respectively. Meanwhile, the PDA lateral flow immunochromatography assay possessed well stability and specificity. In addition, the proposed lateral flow immunochromatography assay has been successfully used for the detection of DON in corn, millet, and oats samples, with minimum detection limits of 8.40, 6.87, and 9.89 μg/kg, recoveries ranging from 80.10%-122.05%, and relative standard deviations less than 12.01%. Conclusion In summary, the PDA lateral flow immunochromatography assay established in this work is sensitive, accurate, rapid and simple, and provides technical support for the detection of mycotoxins in cereals.

polydopamine  /  deoxynivalenol  /  lateral flow immunochromatography assay  /  detection
田秀梅, 陈晓阳, 杨天宝, 罗昌伟, 毛烨炫, 补彤, 张西亚. 聚多巴胺侧流免疫层析法检测谷物中呕吐毒素. 食品安全质量检测学报, 2025 , 16 (4) : 62 -70 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20241211004
Xiu-Mei TIAN, Xiao-Yang CHEN, Tian-Bao YANG, Chang-Wei LUO, Ye-Xuan MAO, Tong BU, Xi-Ya ZHANG. Detection of the deoxynivalenol in cereals by polydopamine-based lateral flow immunochromatography assay[J]. Journal of Food Safety & Quality, 2025 , 16 (4) : 62 -70 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20241211004
呕吐毒素(deoxynivalenol, DON), 又名脱氧雪腐镰刀菌烯醇, 一种B型单端孢霉烯, 是由禾谷镰刀菌和黄色镰刀菌产生的次级代谢产物, 广泛存在于玉米、小麦及其副产品中, 被国际癌症研究机构列为第3类致癌物[1-2]。DON能够产生肝肾毒性、神经毒性、细胞毒性及免疫毒性等作用, 误食被DON污染的食物后, 会导致厌食、呕吐、腹泻等症状, 严重时会造成死亡[3-4]。为了保障人类粮食安全, 我国GB 2761-2017《食品安全国家标准 食品中真菌毒素限量》规定谷物及其制品中DON的最大残留量为1000 μg/kg。此外, 植物饲料原料和配方饲料中的DON含量分别控制在5 mg/kg和1 mg/kg以内(GB 13078-2017《饲料卫生标准》)。因此, 建立一种操作简单、成本较低、快速、高灵敏的方法检测谷物中的DON对于保障我国粮食安全具有重要意义。
目前, DON常用的检测方法包括仪器分析法和免疫分析方法。仪器分析法主要有高效液相色谱法和液相色谱-质谱法[5-6]等, 虽然仪器检测法具有高灵敏度和可靠的结果, 但其样品处理复杂, 设备昂贵, 耗时且需要专业人员操作等缺点不适合即时检测。免疫测定法主要有酶联免疫分析法[7]和侧流免疫层析法(lateral flow immunochromatography assay, LFIA)[8]。其中, LFIA具有操作简单、检测快速、价格低廉和灵敏度高等特点, 已广泛应用于真菌毒素的快速检测[9-10]。但传统以胶体金(gold nanoparticles, AuNPs)作为标记物的LFIA, 由于AuNPs的摩尔消光系数和粒径小, 导致灵敏度逐渐不能满足检测需求[11]。近年来, 研究学者通过构建多种新型载体, 如荧光材料, 以提高灵敏度。如HOU等[12]利用量子点纳米球标记抗体作为探针构建荧光试纸条检测DON, 结果表明该方法检测DON的半数抑制浓度(half maximal inhibitory concentration, IC50)值为2.97 ng/mL, 比酶联免疫分析法低2倍(6.05 ng/mL)。CHEN等[13]合成UiO-66-NH2@量子点应用于免疫层析检测谷物中DON, 检出限(limit of detection, LOD)为0.25 μg/kg, 低于已报道的胶体金和量子点免疫层析。然而, 部分载体存在合成复杂、稳定性差以及标记抗体步骤烦琐等缺点, 影响LFIA性能。因此设计合成简单、生物相容性好、标记稳定的新型载体对于构建高性能LFIA具有重要作用。
聚多巴胺(polydopamine, PDA)作为贻贝仿生类材料, 由多巴胺在弱碱性环境下自聚而得。PDA表面有大量儿茶酚结构, 对有机和无机物质具有强烈附着力以及很高的结合强度。同时, 作为一种天然黑色素, PDA具有颜色鲜亮、生物相容性好以及成本低廉的特点[14-15]。此外, PDA可以通过共价键与有机物质结合, 其利用迈克尔加成或希夫碱反应实现与生物分子的稳定结合[16]。如LIU等[17]合成了160 nm的PDA高亲和力标记抗体构建试纸条灵敏检测呋喃唑酮。ZHAO等[18]制备聚多巴胺包覆的氧化铱纳米载体作为信号标签灵敏检测沙丁胺醇。因此, 基于上述这些优势, PDA可成为建立LFIA的优良载体, 以提高LFIA检测DON的性能。
本研究以多巴胺自氧化聚合合成PDA, 随后通过一步结合法制备PDA标记的单克隆抗体(monoclonal antibodies, mAb)作为探针, 通过优化实验条件, 构建PDA-LFIA检测DON的方法。同时, 与荧光微球(quantum dot microspheres, QDMs)免疫层析和胶体金免疫层析对比灵敏度。最后将PDA-LFIA应用于玉米、小米和燕麦中DON的检测以验证构建方法的实际应用性, 为谷物中真菌毒素的检测提供技术支撑。
玉米、小米和燕麦样品(郑州当地超市)。
DON-牛血清白蛋白(bovine albumin, BSA)抗体(质量浓度8.2 mg/mL)、DON抗体(质量浓度10 mg/mL)(实验室自制); DON、展青霉素(patulin, PAT)、赭曲霉毒素(ochratoxin A, OTA)、T-2毒素、伏马菌素(fumonisin B1, FB1)、15-乙酰脱氧雪腐镰刀菌烯醇(15-acetyl deoxynivalenol, 15AC-DON)、3-乙酰脱氧雪腐镰刀菌烯醇(3-acetyl deoxynivalenol, 3AC-DON)、雪腐镰刀菌烯醇(nivalenol, NIV)(质量浓度10 μg/mL, 美国Sigma公司); 盐酸多巴胺(纯度≥98%)、氨水、甲醇、乙醇(分析纯)(阿拉丁试剂有限公司); 牛血清蛋白(纯度≥98%, 美国Amresco公司); 硝酸纤维素膜(nitrocellulose membrane, NC)(德国赛多利斯公司); 聚氯乙烯底板(polyvinyl chloride, PVC)、吸水垫(上海金标生物科技有限公司); QDMs(北京纳诺金生物科技有限公司); 羊抗鼠二抗免疫球蛋白G (immunoglobulin G, IgG)(北京厚生正德有限公司)。
Sorvall Contifuge Stratos Centrifuge高速冷冻离心机(上海巴玖实业有限公司); RCT磁力加热搅拌机(德国IKA公司); MD-600金标读数仪、MD-210试纸条切条机(南京微测生物科技有限公司); HGS510划膜喷金标仪(杭州峰航科技有限公司); KQ2200DB台式数控超声波清洗器(昆山市超声仪器有限公司); DHG-9000鼓风干燥箱(上海一恒仪器有限公司); BSA124S电子天平(精度0.1 mg, 上海赛多利斯贸易有限公司)。
通过溶液氧化法合成PDA[19], 具体步骤如下: 在250 mL的烧杯中加入8 mL乙醇、0.8 mL氨水和18 mL超纯水, 搅拌30 min, 然后将2 mL盐酸多巴胺溶液(0.05 g/mL)快速注入混合溶液, 搅拌24 h之后, 8500 r/min离心30 min, 去除上清液, 收集沉淀并重悬, 在4 ℃下保存备用。
通过一步结合法合成PDA-mAb探针[20], 具体步骤如下: 向1 mL PDA溶液(2 mg/mL)中加入12 μL DON抗体(1 mg/mL), 混合均匀, 常温孵育30 min, 随后加入30 μL 10%的BSA溶液, 封闭30 min。最后将溶液以8500 r/min的速度离心10 min, 去除上清液, 将沉淀用复溶液复溶至200 μL, 混合均匀后放置于4 ℃下备用。此外, 分别利用静电吸附法和N-羟基琥珀酰亚胺活化法制备AuNPs-mAb和QDMs-mAb[11]
试纸条由样品垫、NC膜、吸水垫、和PVC底板4个部分组成[21]。将NC膜固定于PVC底板, 0.5 mg/mL DON-BSA作为T线, 1 mg/mL羊抗鼠二抗IgG作为C线, 采用划膜仪以0.8 μL/cm的速度喷洒在NC膜的检测区域和质控区域。置于37 ℃的热鼓风干燥箱中烘干6 h, 将样品垫、吸水垫与PVC底板进行组装, 与NC膜重叠1~2 mm, 切成宽3.20 mm的试纸条, 用塑封袋避光干燥保存。
取6 μL PDA-mAb与200 μL含Tween 20的磷酸盐缓冲溶液(phosphate buffered saline with tween 20, PBST)或DON标准溶液反应, 孵育3 min, 随后将试纸条插入微孔板中, 反应8 min, 利用定量读数仪进行定量分析, 以试纸条T线显色情况(T线强度≥800)和抑制率为优化标准, 并判断检测结果。若只有C线显色, 则为阳性, 说明样品中含有DON; 若T线和C线都显色, 说明样品中不含DON; 若C线不显色, 说明试纸条异常。
为了获得最佳检测条件, 本研究以T线显色情况与抑制率为优化标准, 对PDA浓度、抗原浓度、抗体添加量、探针添加量、反应时间及反应pH进行优化。利用定量读数仪检测C、T线显色强度, 用公式(1)计算抑制率(inhibition rate, IR)。
$\mathrm{IR} / \%=\frac{B_{0}-B_{\mathrm{I}}}{B_{0}} \times 100 \%$
式中: B0为阴性样品T线信号读值; BI为阳性样品T线信号读值。
(1)灵敏度评价
在最佳反应条件下, 用PBST溶液将DON标准品质量浓度分别稀释至为0.3、0.6、0.9、1.8、3.0、6.0 ng/mL, 并用PBST作为空白对照组, 用于试纸条灵敏度检测, 通过视觉及定量读数仪判定结果, 根据定量读数仪检测的信号强度, 通过Origin 2021软件绘制标准标准曲线。
(2)特异性评价
选择100 ng/mL的PAT、OTA、T-2毒素、FB1、15AC-DON、3AC-DON、NIV 7种常见的真菌毒素进行特异性检测, DON的质量浓度为6 ng/mL。用试纸条检测, 确定其交叉反应率, 评价其特异性。
(3)精密度评价
根据试纸条的线性范围选定3个不同浓度的标准品, 分别从同一批试纸条和3批试纸条中随机抽取若干试纸条, 对3个不同浓度标准品做3次平行测定, 根据测定结果的平均值计算其变异系数, 评价同一批次和不同批次试纸条之间的精密度。
(1)提取溶液稀释倍数的优化
本研究在提取实际样本中的DON时, 选用甲醇溶液(甲醇:水=9:1, V:V)作为提取液提取样品中的DON, 但提取液的基质干扰会影响抗原抗体的免疫结合, 从而导致试纸条的灵敏度降低。通过探究提取液的不同稀释倍数对试纸条的影响选出最佳的稀释倍数以消除基质干扰。
分别将上清液稀释2、4、6、8、10倍, 按照1.3.4检测步骤进行实验, 通过定量读数仪测定, 选择抑制率高且显色情况好的为提取液稀释倍数。
(2)添加回收实验
将磨碎的谷物样品通过筛网过滤后, 称取1 g置于10 mL离心管中, 向其中加入DON标准品, 混合均匀, 然后加入5 mL甲醇:水(9:1, V/V), 并将混合物涡旋2 min, 超声20 min; 6000 r/min离心10 min, 将上清液移入10 mL离心管。
测定20份空白样本, 根据测定值和标准差计算该试纸条在实际样本检测时的最低LOD, 计算公式见式(2)。
LOD=X+3SD
式中: X为20份样品的平均值; SD为标准偏差。
采用Origin 2021、Excel 2016和Adobe Photoshop 2020对实验数据和图表进行处理, 每组样品均设置平行重复3次。
本研究将盐酸多巴胺通过溶液氧化法合成PDA纳米载体, 随后向其加入DON-mAb通过醌-胺基相互作用共价结合形成PDA-mAb探针[17](图1A)。随后以PDA-mAb探针构建PDA-LFIA检测玉米中的DON。图1B为试纸条检测样品中DON的原理图, 若玉米样品中不含DON, DON-mAb会与T线上的抗原DON-BSA结合, 多余未被结合的DON-mAb会与C线上的羊抗鼠IgG结合, T线和C线均显色; 若玉米样品中含有DON, DON-mAb会与样品中游离的DON发生特异性结合, 占据抗体中与抗原的结合位点, 层析经过T线时, 由于能与抗原结合的DON-mAb数量减少, 使得T线颜色变浅, 甚至不显色, 最终试纸条上只有C线显色。
本研究通过多巴胺氧化自聚合制备PDA。透射电镜测显示PDA为球状, 粒径为(150.4±21.6) nm(图2A、2B)。随后通过紫外吸收光谱和Zeta电位验证PDA-mAb探针的成功制备。如图2C, PDA没有明显的紫外特征峰[22], 偶联DON单克隆抗体后, 在280 nm处出现抗体的紫外吸收峰。如图2D, 通过纳米粒度电位仪测定PDA和PDA-mAb的电位, 结果显示, PDA电位为-39.37 mV、PDA-mAb为-35.12 mV, 表明PDA与抗体的偶联成功。
本研究制备了PDA-LFIA, 通过对PDA质量浓度、抗原浓度、抗体添加量和PDA-mAb探针添加量进行优化, 以空白试纸条显色情况(T线强度≥800)和1.8 ng/mL和6 ng/mL的DON抑制率为优化标准。图3A为PDA质量浓度优化结果图, PDA质量浓度过低显色较差, 但PDA质量浓度过高, 会使抑制率下降, 且样品垫与NC膜之间会有聚集, 从而影响检测性能。当PDA质量浓度为2.0 mg/mL时, 试纸条显色明显, 且抑制率最高, 分别为50.2%和68.6%。图3B为抗原质量浓度优化结果图, 抑制率随着抗原浓度的增大呈现先增后降的趋势, 当抗原包被质量浓度为0.5 mg/mL时, 试纸条检测抑制率高(49.5%和77.6%)且显色情况好。图3C为抗体添加量的优化结果图, 抗体浓度过低或过高会使检测性能下降, 且过高使成本增加, 当抗体添加量为12 μL, 试纸条的抑制率最高(61.8%和83.6%)且显色效果较好。图3D为PDA-mAb探针添加量优化结果图, 随着PDA-mAb探针添加量的增加, 试纸条显色强度越高, 但抑制率呈现先增加后减少的趋势, 当PDA-mAb添加量为6 μL时, 试纸条的抑制率优异(68.3%和86.5%)且显色效果很好。综上所述, 本研究最终以PDA质量浓度为2.0 mg/mL、抗原包被质量浓度为0.5 mg/mL、抗体添加量为12 μg和PDA-mAb探针添加量为6 μL为最佳检测条件。
在最优检测条件下, 通过配制一系列浓度梯度的DON标准溶液绘制标准曲线, 以确定PDA-LFIA检测DON的灵敏度。如图4A所示, 随着DON标准液质量浓度的增加, 试纸条T线颜色逐渐变浅。消线值为6 ng/mL, 视觉检出限为0.9 ng/mL。此外, 采用定量读数仪MD-600检测T线信号强度, 并用Origin 2021软件分析其与DON浓度的关系。T线强度与DON浓度呈现负相关, 标准曲线见图4A, 相关系数r2=0.999, IC20~IC80=0.33~3.75 ng/mL, IC50=1.13 ng/mL。
另外, 本研究也探究了基于荧光QDMs-LFIA和AuNPs-LFIA检测DON的灵敏度, 如图4B和4C。QDMs-LFIA检测DON的IC50为1.76 ng/mL, 线性范围为0.62~5.62 ng/mL。AuNPs-LFIA检测DON的IC50为3.48 ng/mL, 线性范围为1.08~11.27 ng/mL。结果表明PDA-LFIA检测DON的灵敏度比QDMs-LFIA和AuNPs-LFIA分别高0.88倍和3.08倍。因此, 利用PDA载体能够提升LFIA的检测灵敏度。
此外, 将本研究建立的PDA-LFIA与其他检测方法进行对比, 如表1所示, 本研究构建的试纸条相较于其他检测方法, 如SERS、ELISA、荧光法和比色法等, 具有较高的灵敏度, 且检测迅速。此外, 灵敏度也优于其他载体的试纸条, 如胶体金、胶体碳和量子点等, 因此具有良好的应用潜力。
特异性是评价LFIA的重要参数。本研究选择PAT、OTA、T-2、FB1、15AC-DON、3AC-DON、NIV 7种常见的真菌毒素进行特异性检测, 其质量浓度为100 ng/mL, DON质量浓度为6 ng/mL。如图4D所示, PAT、OTA、T-2、FB1的试纸条T线颜色与空白对照组显色情况相当, 但DON结构类似物15AC-DON、NIV、3AC-DON的T线均有明显抑制, 表明PDA-LFIA检测T-2、PAT、FB1和OTA的特异性良好, 但与其类似物15AC-DON、3AC-DON、NIV存在交叉反应。
通过构建DON结构类似物15AC-DON、NIV、3AC-DON的标准曲线, 计算其各自与DON的交叉反应率。结果显示, 3AC-DON的IC50为3.85 ng/mL, 交叉反应率为29.39%; NIV的IC50为4.37 ng/mL, 交叉反应率为25.93%; 15AC-DON的IC50为343.65 ng/mL, 交叉反应率为0.33%, 交叉反应率均低于30%, 表明PDA-LFIA对DON及其类似物具有良好的特异性。交叉反应率不同主要在于前期以DON的3位引入载体合成免疫原制备抗体[33]。DON与15AC-DON、3AC-DON和NIV的区别在于15位、3位和4位的官能团不同。其中, 15AC-DON中15位的乙酰官能团支链较长, 可能影响了抗体的识别能力。
通过添加1、2、3 ng/mL的DON标准品, 评估试纸条的精密度。结果如表2所示, 批次内变异系数为0.54%~7.57%, 批次间变异系数为1.36%~4.08%, 表明实验具有优异的精密度和重复性。
将构建的PDA-LFIA应用于谷物样品中检测DON。首先通过探究样品提取液的不同稀释倍数, 考察PDA-LFIA的甲醇耐受性。当样品提取液稀释8倍时所获得的标准曲线与在缓冲溶液中建立的标准曲线基本重合, 表明该稀释倍数能够基本消除甲醇对该检测体系的影响。因此选择将提取液稀释8倍进行测定。
通过研究玉米、小米和燕麦样品中DON的回收率来判定试纸条的准确性。通过测定20份玉米、小米和燕麦样品获得LOD值分别为8.40、6.87、9.89 μg/kg。DON添加量为40、80、120 μg/kg, 结果如图5所示, 玉米、小米和燕麦中添加回收率分别为83.77%~115.97%、80.10%~96.52%和103.74%~122.05%, 变异系数分别小于12.01%、8.17%和6.37%。这些结果表明, PDA-LFIA在多种样品基质中具有较好的稳定性和可靠性。
目前, PDA试纸条在实际应用中可能存在以下挑战: (1)环境因素(如湿度、温度)对试纸条性能的影响。例如, 湿度过高可能导致试纸条材料的受潮, 从而降低其反应效率, 而温度波动则可能影响反应物的稳定性及其反应速率。(2)在批量生产过程中一致性, 如试纸条的划线均匀性、反应试剂的稳定性、以及设备精度的控制等因素都可能影响试纸条的性能。
因此, 针对环境影响问题, 在实际应用中对试纸条进行合理的包装和储存, 如放入干燥器或者自封袋保存, 避免长时间暴露在高温或高湿环境中。此外, 通过改进试纸条的材料和制备工艺, 使其对环境变化具有更好的稳定性。例如, 使用吸湿性较低的基材, 或者在试纸条表面涂覆防潮层, 可以有效提高其在复杂环境条件下的适用性。针对批量生产中的技术瓶颈, 通过优化生产设备和工艺参数, 如划线设备的精准度和试剂的稳定性, 来提高生产的一致性。此外, 对于探针采用冻干工艺, 也能减少批次间的性能波动, 从而确保试纸条在实际应用中的可靠性。
本研究通过多巴胺氧化自聚合方法成功合成PDA载体, 该载体合成简单, 具有大的比表面积、良好的生物相容性及颜色鲜亮的特点。利用一步混合法简单标记抗体制备了稳定的PDA-mAb探针, 构建了灵敏的PDA-LFIA。通过优化实验参数, 从而确定最佳实验条件, 成功用于玉米、小米和燕麦中DON的快速检测。结果表明, 该试纸条的IC50为1.13 ng/mL, 灵敏度优于QDMs-LFIA和AuNPs-LFIA。同时, 所构建试纸条具有较好的特异性和精密度。此外, 在玉米、小米和燕麦样品中最低检出限分别为8.40、6.87、9.89 μg/kg, 添加回收率在80.10%~122.05%之间, 变异系数均小于12.01%。综上所述, 本研究构建的PDA-LFIA不仅成本低、操作简单, 而且具有高灵敏度、优异稳定性的特点, 为谷物种真菌毒素的快速检测提供技术支撑。
  • 国家自然科学基金项目(32172298)
  • 中国博士后科学基金资助项目(2023M741056)
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文章信息
doi: 10.19812/j.cnki.jfsq11-5956/ts.20241211004
  • 接收时间:2024-12-11
  • 首发时间:2025-07-21
  • 出版时间:2025-02-25
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  • 收稿日期:2024-12-11
基金
国家自然科学基金项目(32172298)
中国博士后科学基金资助项目(2023M741056)
作者信息
    1.北京美正生物科技有限公司, 北京 102200
    2.河南农业大学食品科学技术学院, 郑州 450002
    3.新乡医学院三全学院, 新乡 543000

通讯作者:

* 张西亚(1987—), 男, 博士, 副教授, 主要研究方向为食品安全快速检测。E-mail:
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2种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

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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