Article(id=1153986584929096560, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1153986579971429187, articleNumber=null, orderNo=null, doi=10.19812/j.cnki.jfsq11-5956/ts.20241219008, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1734537600000, receivedDateStr=2024-12-19, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1753061441881, onlineDateStr=2025-07-21, pubDate=1740412800000, pubDateStr=2025-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1753061441881, onlineIssueDateStr=2025-07-21, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1753061441881, creator=13701087609, updateTime=1753061441881, 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=71, endPage=77, ext={EN=ArticleExt(id=1153986585679876984, articleId=1153986584929096560, tenantId=1146029695717560320, journalId=1149652044408987649, language=EN, title=Simultaneous determination of 12 kinds of nicotine pesticides in fruits by ultra performance liquid chromatography-tandem mass spectrometry coupled with cold induced liquid-liquid extraction, 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 establish a detection method for 12 kinds of nicotine pesticides in fruits by ultra performance liquid chromatography-tandem mass spectrometry coupled with cold induced liquid-liquid extraction. Methods Fruit samples were extracted using acetonitrile, and after salting out, shaking, and centrifugation, they were subjected to freeze induced liquid-liquid extraction using acetonitrile water solution for further enrichment and purification. Combined with ultra performance liquid chromatography mass spectrometry, data was collected using a multi reaction monitoring mode, and quantified using isotope internal standard method. Results The linear range of 12 kinds of nicotine compounds was 1-500 ng/mL, and the method showed good linearity (r2>0.999). The limits of detection were 2.15-3.60 μg/kg, and the method limits of quantification were 7.11-13.01 μg/kg. The recovery rates of 12 kinds of nicotine compounds were 73.1%-126.7%, and the relative standard deviations were 3.5%-13.3%. Conclusion This method is easy to operate, has high precision, and good stability, and is suitable for screening and detecting nicotine like compounds in fruit samples.

, correspAuthors=Ding-Guo JIANG, Sai FAN, 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=Qiang LIN, Yun-Kui SHEN, Chao YANG, Mei-Li LI, Jia WANG, Ding-Guo JIANG, Sai FAN), CN=ArticleExt(id=1153986607624474879, articleId=1153986584929096560, tenantId=1146029695717560320, journalId=1149652044408987649, language=CN, title=冷冻诱导液液萃取结合超高效液相色谱-串联质谱法同时检测水果中12种烟碱类农药, 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 建立冷冻诱导液液萃取结合超高效液相色谱-串联质谱法同时检测水果中12种烟碱类农药的检测方法。方法 水果样品使用乙腈提取, 经盐析、振荡、离心后, 使用乙腈-水溶液进行冷冻诱导液液萃取, 进一步富集和净化, 结合超高效液相色谱质谱联用仪, 使用多反应监测模式采集数据, 同位素内标法定量。结果 12种烟碱类化合物在1~500 ng/mL线性范围内方法线性良好(r2>0.999), 检出限为2.15~3.60 μg/kg, 方法定量限为7.11~13.01 μg/kg。12种烟碱类化合物加标回收率为73.1%~126.7%, 相对标准偏差为3.5%~13.3%。结论 该方法操作简便、精密度高、稳定性好, 适用于水果样品中烟碱类化合物的筛查检测工作。

, correspAuthors=蒋定国, 范赛, authorNote=null, correspAuthorsNote=
* 蒋定国(1974—), 男, 博士, 研究员, 主要研究方向为食品安全。E-mail:
范赛(1982—), 男, 博士, 研究员, 主要研究方向为食品安全。E-mail:
, copyrightStatement=null, copyrightOwner=null, extLink=null, articleAbsUrl=null, sourceXml=MDXYs6tDGUipJ5pBZxJaig==, magXml=mP0LHQk7+Acd3H+dTP/Rcw==, pdfUrl=null, pdf=YFeJnLxuuagR+BMobZXMWA==, pdfFileSize=null, pdfExtLink=null, richHtmlUrl=null, mobilePdfUrl=null, reviewReport=null, pdfFirstPage=null, abstractGraph=null, abstractGraphContent=null, abstractVideo=null, citation=null, cebUrl=null, magXmlContent=n9uMlyoocZ8tlI3rVAli3A==, mapNumber=null, authorCompany=null, fund=null, authors=

林强(1986—), 男, 硕士, 副主任医师, 主要研究方向为卫生检验方向。E-mail:

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林强(1986—), 男, 硕士, 副主任医师, 主要研究方向为卫生检验方向。E-mail:

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Analytical Chemistry, 2003, 75: 3019-3030., articleTitle=Strategies for the assessment of matrix effect in quantitative bioanalytical methods based on HPLC-MS/MS, refAbstract=null)], funds=[Fund(id=1177987121462784552, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986584929096560, awardId=BJSP2021101345, language=CN, fundingSource=北京市食品风险监测项目(BJSP2021101345), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1177987118216393192, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986584929096560, xref=null, ext=[AuthorCompanyExt(id=1177987118224781801, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986584929096560, companyId=1177987118216393192, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1. 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注: FCA. 呋虫胺; XDCA. 烯啶虫胺; SCQ. 噻虫嗪; HYCD. 环氧虫啶; SCA. 噻虫胺; PCL. 吡虫啉; LSL. 氯噻啉; FPFNT. 氟吡呋喃酮; FDCAJ. 氟啶虫胺腈; DCM. 啶虫咪; SCL. 噻虫啉; PCD1. 哌虫啶1; PCD2. 哌虫啶2。

, figureFileSmall=yj+2YxoudipSR36s6jYHEw==, figureFileBig=n2IpLXYXMDE5wo8unU2/3Q==, tableContent=null), ArticleFig(id=1177987120934302242, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986584929096560, language=EN, label=Table 1, caption=

Mass spectrum parameters of target compound

, figureFileSmall=null, figureFileBig=null, tableContent=
物质名称 母离子
(m/z)
子离子
(m/z)
碰撞能量
/V
去簇电压
/V
呋虫胺 203.1 139.0* 17 40
157.0 13
烯啶虫胺 271.1 225.1* 16 30
136.0 40
噻虫嗪 292.0 211.1* 18 20
181.1 31
环氧虫啶 323.2 136.0* 50 40
151.1 30
277.0 20
噻虫胺 250.0 169.1* 19 10
132.0 24
吡虫啉 256.1 175.1* 25 20
209.1 22
氯噻啉 262.0 181.0* 20 20
132.1 40
啶虫咪 223.1 136.0* 28 20
56.2 20
噻虫啉 253.0 136.0* 30 20
90.1 50
哌虫啶1 367.2 321.2* 18 20
263.1 22
哌虫啶2 367.2 306.1* 35 20
137.1 31
氟啶虫胺腈 278.0 173.9* 13 20
153.9 40
氟吡呋喃酮 289.0 136.1* 25 20
90.0 60
呋虫胺-D3 206.0 132.0 17 20
噻虫胺-D3 253.0 172.1 17 10
烯啶虫胺-D3 274.0 228.1 17 10
吡虫啉-D4 260.1 213.1 21 20
氯噻啉-D4 266.0 185.0 21 10
啶虫咪-D3 226.0 136.2 30 10
噻虫嗪-D3 295.0 214.2 17 20
噻虫啉-D4 257.1 136.1 30 20
氟啶虫胺腈-D3 281.2 177.1 14 20
), ArticleFig(id=1177987121013994019, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986584929096560, language=CN, label=表1, caption=

目标化合物质谱参数

, figureFileSmall=null, figureFileBig=null, tableContent=
物质名称 母离子
(m/z)
子离子
(m/z)
碰撞能量
/V
去簇电压
/V
呋虫胺 203.1 139.0* 17 40
157.0 13
烯啶虫胺 271.1 225.1* 16 30
136.0 40
噻虫嗪 292.0 211.1* 18 20
181.1 31
环氧虫啶 323.2 136.0* 50 40
151.1 30
277.0 20
噻虫胺 250.0 169.1* 19 10
132.0 24
吡虫啉 256.1 175.1* 25 20
209.1 22
氯噻啉 262.0 181.0* 20 20
132.1 40
啶虫咪 223.1 136.0* 28 20
56.2 20
噻虫啉 253.0 136.0* 30 20
90.1 50
哌虫啶1 367.2 321.2* 18 20
263.1 22
哌虫啶2 367.2 306.1* 35 20
137.1 31
氟啶虫胺腈 278.0 173.9* 13 20
153.9 40
氟吡呋喃酮 289.0 136.1* 25 20
90.0 60
呋虫胺-D3 206.0 132.0 17 20
噻虫胺-D3 253.0 172.1 17 10
烯啶虫胺-D3 274.0 228.1 17 10
吡虫啉-D4 260.1 213.1 21 20
氯噻啉-D4 266.0 185.0 21 10
啶虫咪-D3 226.0 136.2 30 10
噻虫嗪-D3 295.0 214.2 17 20
噻虫啉-D4 257.1 136.1 30 20
氟啶虫胺腈-D3 281.2 177.1 14 20
), ArticleFig(id=1177987121093685796, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986584929096560, language=EN, label=Table 2, caption=

Linear range, correlation coefficients, LODs and LOQs of 12 kinds of nicotine pesticides

, figureFileSmall=null, figureFileBig=null, tableContent=
化合物 线性范围/(μg/L) 相关系数(r) LODs/(μg/kg) LOQs/(μg/kg) 同位素内标
呋虫胺 1~500 0.9961 2.50 8.28 呋虫胺-D3
烯啶虫胺 1~500 0.9992 3.13 10.30 烯啶虫胺-D3
噻虫嗪 1~500 0.9995 2.60 8.84 噻虫嗪-D3
环氧虫啶 1~500 0.9999 3.01 9.93 烯啶虫胺-D3
噻虫胺 1~500 0.9992 2.15 7.10 噻虫胺-D3
吡虫啉 1~500 0.9975 2.20 7.36 吡虫啉-D4
氯噻啉 1~500 0.9991 3.10 10.36 氯噻啉-D4
啶虫咪 1~500 0.9992 2.25 7.43 啶虫脒-D3
噻虫啉 1~500 0.9961 2.30 7.79 噻虫啉-D4
哌虫啶1 1~500 0.9995 2.45 8.09 噻虫啉-D4
哌虫啶2 1~500 0.9985 3.40 11.45 噻虫啉-D4
氟啶虫胺腈 1~500 0.9992 3.60 13.01 氟啶虫胺腈-D3
氟吡呋喃酮 1~500 0.9975 2.54 8.38 氟啶虫胺腈-D3
), ArticleFig(id=1177987121164988965, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986584929096560, language=CN, label=表2, caption=

12种烟碱类化合物的线性范围、相关系数、LODs和LOQs

, figureFileSmall=null, figureFileBig=null, tableContent=
化合物 线性范围/(μg/L) 相关系数(r) LODs/(μg/kg) LOQs/(μg/kg) 同位素内标
呋虫胺 1~500 0.9961 2.50 8.28 呋虫胺-D3
烯啶虫胺 1~500 0.9992 3.13 10.30 烯啶虫胺-D3
噻虫嗪 1~500 0.9995 2.60 8.84 噻虫嗪-D3
环氧虫啶 1~500 0.9999 3.01 9.93 烯啶虫胺-D3
噻虫胺 1~500 0.9992 2.15 7.10 噻虫胺-D3
吡虫啉 1~500 0.9975 2.20 7.36 吡虫啉-D4
氯噻啉 1~500 0.9991 3.10 10.36 氯噻啉-D4
啶虫咪 1~500 0.9992 2.25 7.43 啶虫脒-D3
噻虫啉 1~500 0.9961 2.30 7.79 噻虫啉-D4
哌虫啶1 1~500 0.9995 2.45 8.09 噻虫啉-D4
哌虫啶2 1~500 0.9985 3.40 11.45 噻虫啉-D4
氟啶虫胺腈 1~500 0.9992 3.60 13.01 氟啶虫胺腈-D3
氟吡呋喃酮 1~500 0.9975 2.54 8.38 氟啶虫胺腈-D3
), ArticleFig(id=1177987121232097830, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986584929096560, language=EN, label=Table 3, caption=

Spiked recoveries and RSDs (%, n=6)

, figureFileSmall=null, figureFileBig=null, tableContent=
化合物 15 µg/kg 100 µg/kg 800 µg/kg
平均
回收率
RSDs 平均
回收率
RSDs 平均
回收率
RSDs
呋虫胺 83.2 11.7 93.6 8.9 101.6 3.5
烯啶虫胺 86.7 10.9 90.5 8.5 96.7 7.6
噻虫嗪 84.9 9.9 94.9 7.1 97.9 6.8
环氧虫啶 84.3 9.7 85.6 8.0 90.7 5.4
噻虫胺 85.1 11.0 91.6 7.4 94.2 3.8
吡虫啉 85.7 10.9 92.5 6.4 95.8 6.9
氯噻啉 73.1 8.7 89.0 8.5 93.5 8.1
啶虫咪 88.8 10.2 93.2 8.3 98.8 5.8
噻虫啉 85.7 10.5 98.3 8.1 97.6 7.2
哌虫啶1 80.6 9.7 99.3 5.5 90.2 3.5
哌虫啶2 91.4 11.7 89.3 7.2 96.4 6.7
氟啶虫胺腈 83.3 11.4 97.7 6.3 94.6 5.5
氟吡呋喃酮 126.7 13.3 94.6 8.9 102.6 3.8
), ArticleFig(id=1177987121299206695, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986584929096560, language=CN, label=表3, caption=

加标回收率与RSDs(%, n=6)

, figureFileSmall=null, figureFileBig=null, tableContent=
化合物 15 µg/kg 100 µg/kg 800 µg/kg
平均
回收率
RSDs 平均
回收率
RSDs 平均
回收率
RSDs
呋虫胺 83.2 11.7 93.6 8.9 101.6 3.5
烯啶虫胺 86.7 10.9 90.5 8.5 96.7 7.6
噻虫嗪 84.9 9.9 94.9 7.1 97.9 6.8
环氧虫啶 84.3 9.7 85.6 8.0 90.7 5.4
噻虫胺 85.1 11.0 91.6 7.4 94.2 3.8
吡虫啉 85.7 10.9 92.5 6.4 95.8 6.9
氯噻啉 73.1 8.7 89.0 8.5 93.5 8.1
啶虫咪 88.8 10.2 93.2 8.3 98.8 5.8
噻虫啉 85.7 10.5 98.3 8.1 97.6 7.2
哌虫啶1 80.6 9.7 99.3 5.5 90.2 3.5
哌虫啶2 91.4 11.7 89.3 7.2 96.4 6.7
氟啶虫胺腈 83.3 11.4 97.7 6.3 94.6 5.5
氟吡呋喃酮 126.7 13.3 94.6 8.9 102.6 3.8
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冷冻诱导液液萃取结合超高效液相色谱-串联质谱法同时检测水果中12种烟碱类农药
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林强 1 , 申贇魁 1 , 杨超 1 , 李美丽 1 , 王佳 1 , 蒋定国 2, * , 范赛 3, *
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): 71-77
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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): 71-77
冷冻诱导液液萃取结合超高效液相色谱-串联质谱法同时检测水果中12种烟碱类农药
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林强1 , 申贇魁1, 杨超1, 李美丽1, 王佳1, 蒋定国2, * , 范赛3, *
作者信息
  • 1.北京市延庆区疾病预防控制中心, 北京 102100
  • 2.国家食品安全风险评估中心, 北京 100021
  • 3.北京市疾病预防控制中心, 北京市预防医学研究中心, 北京 100013
  • 林强(1986—), 男, 硕士, 副主任医师, 主要研究方向为卫生检验方向。E-mail:

通讯作者:

* 蒋定国(1974—), 男, 博士, 研究员, 主要研究方向为食品安全。E-mail:
范赛(1982—), 男, 博士, 研究员, 主要研究方向为食品安全。E-mail:
Simultaneous determination of 12 kinds of nicotine pesticides in fruits by ultra performance liquid chromatography-tandem mass spectrometry coupled with cold induced liquid-liquid extraction
Qiang LIN1 , Yun-Kui SHEN1, Chao YANG1, Mei-Li LI1, Jia WANG1, Ding-Guo JIANG2, * , Sai FAN3, *
Affiliations
  • 1. Beijing Yanqing District Center for Disease Control and Prevention, Beijing 102100, China
  • 2. China National Center for Food Safety Risk Assessment, Beijing 100021, China
  • 3. Beijing Center for Disease Prevention and Control, Beijing Center for Preventive Medicine Research, Beijing 100013, China
出版时间: 2025-02-25 doi: 10.19812/j.cnki.jfsq11-5956/ts.20241219008
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目的 建立冷冻诱导液液萃取结合超高效液相色谱-串联质谱法同时检测水果中12种烟碱类农药的检测方法。方法 水果样品使用乙腈提取, 经盐析、振荡、离心后, 使用乙腈-水溶液进行冷冻诱导液液萃取, 进一步富集和净化, 结合超高效液相色谱质谱联用仪, 使用多反应监测模式采集数据, 同位素内标法定量。结果 12种烟碱类化合物在1~500 ng/mL线性范围内方法线性良好(r2>0.999), 检出限为2.15~3.60 μg/kg, 方法定量限为7.11~13.01 μg/kg。12种烟碱类化合物加标回收率为73.1%~126.7%, 相对标准偏差为3.5%~13.3%。结论 该方法操作简便、精密度高、稳定性好, 适用于水果样品中烟碱类化合物的筛查检测工作。

超高效液相色谱-串联质谱法  /  水果  /  烟碱类农药

Objective To establish a detection method for 12 kinds of nicotine pesticides in fruits by ultra performance liquid chromatography-tandem mass spectrometry coupled with cold induced liquid-liquid extraction. Methods Fruit samples were extracted using acetonitrile, and after salting out, shaking, and centrifugation, they were subjected to freeze induced liquid-liquid extraction using acetonitrile water solution for further enrichment and purification. Combined with ultra performance liquid chromatography mass spectrometry, data was collected using a multi reaction monitoring mode, and quantified using isotope internal standard method. Results The linear range of 12 kinds of nicotine compounds was 1-500 ng/mL, and the method showed good linearity (r2>0.999). The limits of detection were 2.15-3.60 μg/kg, and the method limits of quantification were 7.11-13.01 μg/kg. The recovery rates of 12 kinds of nicotine compounds were 73.1%-126.7%, and the relative standard deviations were 3.5%-13.3%. Conclusion This method is easy to operate, has high precision, and good stability, and is suitable for screening and detecting nicotine like compounds in fruit samples.

ultra performance liquid chromatography-tandem mass spectrometry  /  fruits  /  nicotine pesticides
林强, 申贇魁, 杨超, 李美丽, 王佳, 蒋定国, 范赛. 冷冻诱导液液萃取结合超高效液相色谱-串联质谱法同时检测水果中12种烟碱类农药. 食品安全质量检测学报, 2025 , 16 (4) : 71 -77 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20241219008
Qiang LIN, Yun-Kui SHEN, Chao YANG, Mei-Li LI, Jia WANG, Ding-Guo JIANG, Sai FAN. Simultaneous determination of 12 kinds of nicotine pesticides in fruits by ultra performance liquid chromatography-tandem mass spectrometry coupled with cold induced liquid-liquid extraction[J]. Journal of Food Safety & Quality, 2025 , 16 (4) : 71 -77 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20241219008
新烟碱类农药是一种新型的广谱类杀虫剂, 主要通过控制昆虫的神经系统烟碱乙酰胆碱酯酶受体(nAChRs), 麻痹昆虫的中枢神经系统, 致使昆虫死亡[1]。新烟碱类农药在杀死害虫的同时, 会对蜜蜂等非靶标生物造成的负面影响, 研究表明, 亚致死剂量的新烟碱类农药会影响蜜蜂的生殖、生长发育, 从而影响农作物的授粉[2], 同时, 新烟碱类农药对陆生动物也会产生致死作用[3]。2019年, 美国环境保护署撤销了13种含有噻虫嗪和噻虫胺的农药产品登记, 2022年, 欧盟计划撤销噻虫嗪在所有农产品上的最大残留限量[4]。我国作为新烟碱类农药的生成和使用大国, 建立蔬菜、水果中新烟碱类农药的检测方法, 对于开展残留量限值提供数据支持, 具有重要意义。
目前, 新烟碱类农药的检测方法主要有液相色谱-串联质谱法[5-10]、高效液相色谱法[11-15]、气相色谱-质谱法[16-19]、气相色谱法[20-21]、液相色谱法[20-21]、分子印迹技术[22]以及快速检测方法[23-25]等。液相色谱-串联质谱法灵敏度高、抗干扰能力强, 是新烟碱类农药的主要检测方法。相关研究表明[5,7,26], 使用QuEChERS结合液相色谱-串联质谱法检测蔬菜中新烟碱类农药时, 需要使用除水剂和净化材料对样品进行净化处理, 操作相对复杂, 同时样品净化效果不佳, 目标物出现基质抑制或增强效应, 需使用基质匹配曲线进行定量, 在检测多种水果时, 空白基质样品不易得到。冷冻诱导液液萃取技术是在液液萃取技术基础上形成的[27]。在低温条件下, 乙腈在水相中的溶解度逐渐下降, 从而诱导乙腈与水相分离。大量的目标化合物溶解在上层乙腈相, 进一步调节乙腈-水混合溶液中乙腈的比例, 可以实现目标物的富集和净化。冷冻诱导液液萃取无需使用其他化学试剂, 在低温条件下, 样品便可达到富集和净化的效果, 操作相对简便、易行, 适合水果中烟碱类农药的检测工作。
基于以上研究背景, 本研究使用QuEChERS方法对样品进行前处理, 使用冷冻诱导液液萃取对样品进行富集和净化, 无需使用净化试剂耗材, 结合超高效液相色谱-串联质谱法, 采用同位素内标法定量, 无需寻找空白基质样品建立基质匹配曲线。该方法的建立可以为后续开展新烟碱类农药的检测提供支持。
检测200份水果样本来源于2024年食品风险监测采集样品。
甲醇、乙腈、甲酸、甲酸铵(色谱纯, 美国Fisher公司); 硫酸镁、氯化钠、醋酸钠、柠檬酸钠、柠檬酸氢二钠(优级纯, 中国医药集团有限公司); 陶瓷均质子(50 mL管, 美国安捷伦公司)。12种烟碱类农药混合标准物质: 呋虫胺、烯啶虫胺、噻虫嗪、环氧虫啶、噻虫胺、吡虫啉、氯噻啉、啶虫脒、噻虫啉、哌虫啶、氟啶虫胺腈、氟吡呋喃酮(质量浓度100 mg/L, 天津阿尔塔公司); 9种同位素内标: 呋虫胺-D3、噻虫胺-D3、烯啶虫胺-D3、吡虫啉-D4、氯噻啉-D4、啶虫脒-D3、噻虫嗪-D3、噻虫啉-D4、氟啶虫胺腈-D3(质量浓度100 mg/L, 天津阿尔塔公司)。
QTRAP 6500超高效液相色谱仪(美国SCIEX公司); 艾卡MS 3 Basic旋涡振荡器(德国IKA公司); Centrifuge 5810 R高速冷冻离心机(美国艾本德公司); Milli-Q IQ 7005超纯水器(美国Millipore公司); DW-40L262超低温冰箱(中国海尔公司); Thermo Fisher TM Accucore aQ色谱柱(150 mm× 2.1 mm, 2.6 μm)(美国THERMO公司)。
流动相A相: 准确量取979 mL去离子水, 加入1 mL甲酸, 加入0.315 g甲酸铵, 超声溶解。再加入20 mL甲醇, 超声混匀, 过0.22 µm滤膜备用。
流动相B相: 准确量取19 mL去离子水, 加入1 mL甲酸, 加入0.315 g甲酸铵, 超声溶解。在980 mL甲醇中加入上述液体, 超声混匀, 过0.22 µm滤膜备用。
9种同位素内标使用液(1.0 mg/L): 分别准确吸取1.00 mL的呋虫胺-D3、噻虫胺-D3、烯啶虫胺-D3、吡虫啉-D4、氯噻啉-D4、啶虫脒-D3、噻虫嗪-D3、噻虫啉-D4、氟啶虫胺腈-D3同位素内标溶液, 用乙腈定容至10 mL, 得到质量浓度为10 mg/L的同位素内标储备液。吸取1.00 mL内标储备液, 用乙腈定容至10 mL, 得到质量浓度为1.0 mg/L的同位素内标使用液体。
12种烟碱类农药标准混合使用液(1.0 mg/L): 吸取1.0 mL混合标准溶液, 用乙腈稀释, 得到质量浓度为10 mg/L的标准混合溶液。使用乙腈将上述标准混合溶液稀释, 得到质量浓度为1.0 mg/L的12种烟碱标准混合使用液。
混合标准工作溶液: 准确吸取上述标准混合使用液和内标混合使用液, 使用40%乙腈水溶液为溶剂, 得到质量浓度分别为0、1.00、5.00、10.00、50.00、100.00、200.00、500.00 ng/mL的12种烟碱类混合标准系列溶液, 9种同位素内标质量浓度为20.0 ng/mL。
蔬菜和水果的取样量按照相关标准的规定执行。样品取样部位按照GB 2763—2021《食品安全国家标准 食品中农药最大残留限量》的规定执行。对于个体较小的样品, 取样后全部处理; 对于个体较大的基本均匀样品, 可在对称轴或对称面上分割或切成小块后处理; 对于细长、扁平或组分含量在各部分有差异的样品, 可在不同部位切取小片或截成小段后处理; 取后的样品将其切碎, 充分混匀, 用四分法取样或直接放入组织捣碎机中捣碎成匀浆, 放入聚乙烯瓶中。将样品保存置于-18 ℃冷藏保存。
称取10 g试样(精确至0.01 g)于50 mL塑料离心管中, 先后加入0.20 mL同位素内标使用液、10 mL乙腈, 振荡1 min, 然后加入4 g硫酸镁、1 g氯化钠、1 g柠檬酸钠、0.5 g柠檬酸氢二钠及1颗陶瓷均质子, 盖上离心管盖, 剧烈振荡1 min后, 4 ℃、10000 r/min离心5 min。上清液待净化。
吸取1 mL上清液至离心管中, 加入1.5 mL纯水, 涡旋混匀。放置于-40 ℃冰箱中冷冻30 min, 取上层乙腈相200 µL, 放置于内衬管中, 上机测定。
色谱柱: Thermo Fisher TM Accucore aQ (150 mm× 2.1 mm, 2.6 μm)。流动相: A相为水:甲醇=98:2(含5 mmol甲酸铵和0.1%甲酸), B相为甲醇:水=98:2(含5 mmol甲酸铵和0.1%甲酸)。梯度洗脱程序: 0~4.0 min, 20% B; 4.0~5.5 min, 40% B; 5.5~10.5 min, 100% B; 10.5~13.9 min, 100% B; 13.9~15.0 min, 100% A; 15.0~20.0 min, 100% A。流速: 0.4 mL/min。柱温: 35 ℃。进样量: 2 µL。
电喷雾离子源(正离子模式), 喷雾电压: 5.5 kV。离子源温度: 500 ℃。气帘气: 35 psi。喷雾气: 60 psi。辅助加热气: 60 psi。碰撞气: Medium。扫描模式: 多离子反应监测模式(multiple reaction monitoring, MRM)。具体参数见表1
本研究使用OriginPro 2021 (64-bit), 版本号: 9.8.0.200, 对相关数据进行绘图。使用SPSS 19.0中文修改版对数据进行统计分析。
本研究在文献[28,29]的基础上, 对提取试剂进行了优化。分别考察了乙腈、30%乙腈-水、50%乙腈-水、80%乙腈-水作为提取溶剂进行实验分析, 结果见图1。实验结果表明, 当使用纯乙腈作为提取溶剂时, 目标化合物的提取效率最高, 因此选择乙腈作为提取溶剂。
本研究考察了12种烟碱类农药在40%、50%、60%、70%和80%乙腈含量(乙腈-水溶液)下进行冷冻诱导液液萃取时的富集倍数, 结果见图2。检测结果表明, 随着乙腈水溶液中乙腈比例的不断降低, 12种烟碱类农药(除烯啶虫胺外)富集倍数逐渐增加。当乙腈比例降低至30%时, 乙腈和水相分离消失。因此选择40%乙腈水溶液进行冷冻诱导液液萃取, 对样本进行净化和富集。
本研究对比了不同冷冻温度下, 12种烟碱类农药在乙腈水溶液中发生冷冻诱导液液萃取的时间。实验结果表明, 在-40 ℃时, 30 min出现分层现象。在-20 ℃时, 70 min出现分层。但是如果长时间放置于低温冰箱(-20 ℃、-40 ℃)中, 整个试管会出现结冰现象。为提升工作效率, 本研究选用-40 ℃作为冷冻温度对样品进行处理。
为解决烯啶虫胺等中等极性化合物富集在水相层的情况, 本研究使用40%乙腈-水溶液稀释标准物质, 同时按照上述条件进行冷冻诱导液液萃取处理。
分别配制12种烟碱类农药混合标准溶液和9种烟碱类混合同位素内标溶液, 在正离子扫描模式下, 分别将两种混合溶液通过针泵注入质谱, 通过母离子全扫描, 确定定量离子和定性离子, 在MRM模式下, 分别优化质谱的碰撞能量、去簇电压、雾化气、气帘气, 具体数值见表1
本研究分别对比了乙腈、甲醇和5 mmol甲酸铵、0.1%甲酸作为流动相对目标化合物的分离程度, 研究结果表明, 使用A相为水:甲醇=98:2(含5 mmol甲酸铵和0.1%甲酸), B相为甲醇:水=98:2(含5 mmol甲酸铵和0.1%甲酸)为流动相时, 12种目标化合物能够分离, 且色谱峰峰形对称。12种目标化合物色谱图见图3
按照上述处理方法处理水果样品(苹果、葡萄、梨), 上机检测若无目标物, 则为空白基质提取液。使用该空白基质提取液配制质量浓度范围在1.0~100 µg/L的基质混合标准溶液, 与纯溶剂配制的标准曲线同时上机检测, 以目标化合物的峰面积为纵坐标(Y), 质量浓度为横坐标(X, µg/L)绘制标准曲线。根据基质溶液标准曲线的斜率与纯溶剂配制的标准曲线(不含同位素内标)斜率的比值来评价基质效应, 若斜率比值介于0.8~1.2之间, 则认为基质效应不明显, 对检测结果没有影响; 若斜率比值超过上述范围, 则表明存在基质效应, 影响检测结果[30]。实验结果表明, 12种烟碱类农药的ME值在78.5%~132.1%之间, 表明部分目标化合物存在基质效应, 为降低基质效应对检测结果的影响, 本研究采用同位素内标校准工作曲线, 可有效降低基质效应带来的影响。本研究有9种目标物使用对应的9种同位素内标定量, 其余3种目标物通过加标回收率实验, 确定同位素内标进行定量, 具体见表2。按照上述评价基质效应的方法, 计算基质溶液标准曲线的斜率与纯溶剂配制的标准曲线(含同位素内标)的斜率的比值, 实验结果表明, 12种烟碱类农药的基质效应值在85.2%~115.6%之间, 满足实验要求。根据以上基质效应评估的结果, 在进行实际样品定量检测时, 可以用标准曲线(含同位素内标)代替基质匹配标准工作曲线。
按照1.2方法配制12种烟碱类农药化合的同位素内标标准工作曲线。12种烟碱类化合物的线性范围、相关系数见表2。选取空白水果样品, 添加12种烟碱类化合物混合标准溶液, 按照1.3前处理方法处理, 以S/N=3确定各目标化合物的LOD, 以S/N=10确定各目标化合物的LOQ。实验结果表明, 12种烟碱类化合物在1~500 ng/mL线性范围内方法线性良好(r2>0.999), LOD为2.15~3.60 μg/kg, LOQ为7.10~13.01 μg/kg。
选取草莓、香瓜、葡萄3种水果样品添加低、中、高3种浓度的12种烟碱类农药标准物质, 每类样品检测8次平行加标回收实验, 计算加标回收率和测定结果的相对标准偏差(relative standard deviation, RSD), 实验结果表明(具体见表3), 3类样品中12种烟碱类农药的平均回收率为73.1%~126.7%, RSDs为3.5%~13.3%, 表明该检测方法的准确度高, 稳定性好, 适合开展水果样品中12种烟碱类农药的检测。
利用本研究建立的方法对200份水果样本进行检测, 12种烟碱类农药检出率为35.5%, 其中, 噻虫嗪的浓度为<2.60~56.3 μg/kg, 噻虫胺的浓度为<2.15~86.7 μg/kg, 啶虫脒的浓度为<2.25~35.9 μg/kg, 其他化合物检出率较低。
使用食品风险监测标准操作规章中12种烟碱类农药检验方法对上述200份水果样品进行检测。对两种分析方法的检测结果使用配对t检验分析, 双侧检验, 检验水准: α=0.05, 结果表明P>0.05, 不能拒绝两种分析方法检测结果存在差异。表明本研究建立的方法检测结果一致, 适用于开展水果中烟碱类农药检测工作。同时, 该方法的LOD、LOQ均低于品风险监测标准操作规章中烟碱类农药检测方法, 满足水果中烟碱类农药检测需求。
本研究使用冷冻诱导液液萃取技术对样品进行富集和净化, 结合超高效液相色谱-串联质谱法进行检测, 采用同位素内标法准确定量, 建立了水果样品中多种烟碱类化合物的检测方法。与其他检测方法相比, 该方法操作简便、灵敏度高、稳定性好, 适用于水果样品中多种烟碱类农药的快速筛查和检测工作。
  • 北京市食品风险监测项目(BJSP2021101345)
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doi: 10.19812/j.cnki.jfsq11-5956/ts.20241219008
  • 接收时间:2024-12-19
  • 首发时间:2025-07-21
  • 出版时间:2025-02-25
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  • 收稿日期:2024-12-19
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北京市食品风险监测项目(BJSP2021101345)
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    1.北京市延庆区疾病预防控制中心, 北京 102100
    2.国家食品安全风险评估中心, 北京 100021
    3.北京市疾病预防控制中心, 北京市预防医学研究中心, 北京 100013

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* 蒋定国(1974—), 男, 博士, 研究员, 主要研究方向为食品安全。E-mail:
范赛(1982—), 男, 博士, 研究员, 主要研究方向为食品安全。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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