Article(id=1215670311912133303, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1215670311140381365, articleNumber=null, orderNo=null, doi=10.19812/j.cnki.jfsq11-5956/ts.20250331009, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=research-article, receivedDate=1743350400000, receivedDateStr=2025-03-31, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1767767988422, onlineDateStr=2026-01-07, pubDate=1753372800000, pubDateStr=2025-07-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1767767988422, onlineIssueDateStr=2026-01-07, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1767767988422, creator=13701087609, updateTime=1767767988422, updator=13701087609, issue=Issue{id=1215670311140381365, tenantId=1146029695717560320, journalId=1149652044408987649, year='2025', volume='16', issue='14', pageStart='1', pageEnd='326', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1767767988237, creator=13701087609, updateTime=1767970098618, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1216518023599538606, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1215670311140381365, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1216518023599538607, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1215670311140381365, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=40, endPage=46, ext={EN=ArticleExt(id=1215670313468220096, articleId=1215670311912133303, tenantId=1146029695717560320, journalId=1149652044408987649, language=EN, title=Determination of residues of 9 kinds of pesticides in black tea and green tea by multi-walled carbon nanotubes coupled with gas chromatography-tandem mass spectrometry, columnId=1215670312151208635, journalTitle=Journal of Food Safety & Quality, columnName=Special Topic: Food Safety Risk Monitoring and Assessment in Beijing, runingTitle=null, highlight=null, articleAbstract=

Objective To establish a method for the determination of 9 kinds of pesticide residues in black tea and green tea based on multi-walled carbon nanotubes (MWCNTs) combined with gas chromatography-tandem mass spectrometry (GC-MS/MS). Methods The key parameters were optimized by single factor experiment, including the amount of MWCNTs, ultrasonic extraction time and extraction solvent, and the methodological performance was evaluated, including linear range, detection limit, quantification limit, accuracy and precision. Results The linear relationship of this method was excellent, with correlation coefficients all greater than 0.99. The limits of detection and limits of quantitation were 0.002-0.004 mg/kg and 0.007-0.012 mg/kg, respectively. The recoveries of 9 kinds of pesticides in black tea and green tea were 71.52%-118.80%, and the relative standard deviation were 1.00%-6.92%. Conclusion This study successfully establishes a pesticide residue detection method based on MWCNTs combined with GC-MS/MS, which has the advantages of high sensitivity, high recovery rate and low solvent consumption, and significantly improves the detection efficiency. The actual sample detection shows that this method is suitable for the rapid screening of commercially available tea, and provides reliable technical support for the regulatory authorities.

, correspAuthors=Kai LEI, 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=Shu-Liang JI, Fang WANG, Jian-Guo WANG, Xiao-Yi BI, Yun WU, Kai LEI), CN=ArticleExt(id=1215670318333612928, articleId=1215670311912133303, tenantId=1146029695717560320, journalId=1149652044408987649, language=CN, title=多壁碳纳米管结合气相色谱-串联质谱法测定红茶和绿茶中9种农药残留, columnId=1215670313522746049, journalTitle=食品安全质量检测学报, columnName=专题:北京市食品安全风险监测与评估, runingTitle=null, highlight=null, articleAbstract=

目的 建立基于多壁碳纳米管(multi-walled carbon nanotubes, MWCNTs)结合气相色谱-串联质谱法(gas chromatography-tandem mass spectrometry, GC-MS/MS)测定红茶和绿茶中9种农药残留的方法。方法 通过单因素实验优化关键参数, 包括MWCNTs用量、超声提取时间及提取溶剂, 并评估方法学性能, 涵盖线性范围、检出限、定量限、准确度、精密度。结果 本方法线性关系良好, 相关系数均大于0.99, 检出限和定量限分别为0.002~0.004 mg/kg和0.007~0.012 mg/kg, 红茶和绿茶中9种农药的加标回收率为71.52%~118.80%, 相对标准偏差为1.00%~6.92%。结论 本研究成功建立了一种基于MWCNTs结合GC-MS/MS的农药残留检测方法, 具有高灵敏度、高回收率、低溶剂消耗等优势, 显著提升了检测效率。实际样本检测表明, 该方法适用于市售茶叶的快速筛查, 为监管部门提供可靠的技术支持。

, correspAuthors=雷凯, authorNote=null, correspAuthorsNote=
*雷凯(1980—), 男, 副主任技师, 主要研究方向为食品安全检测、卫生学检验技术。E-mail:
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籍术良(1981—), 男, 副主任技师, 主要研究方向为食品安全检测、卫生学检验技术。E-mail:

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Analytical Chemistry, 2021, 93(14): 5892-5901., articleTitle=Comprehensive evaluation of QuEChERS performance in tea matrices: Recovery and precision analysis, refAbstract=null)], funds=[Fund(id=1215670327330394615, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1215670311912133303, awardId=YHLD2019004, language=CN, fundingSource=通州区高层次人才发展支持计划项目(YHLD2019004), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1215670318560105358, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1215670311912133303, xref=null, ext=[AuthorCompanyExt(id=1215670318568493968, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1215670311912133303, companyId=1215670318560105358, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=Beijing Tongzhou District Center for Disease Control and Prevention, Beijing 101100, China), AuthorCompanyExt(id=1215670318576882577, 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chromatogram of 9 kinds of pesticide standard solutions, figureFileSmall=lyTGRmCn8bwcHLVtU0Wsjg==, figureFileBig=j/K5aeyCLbsIg7YxpG9ZFg==, tableContent=null), ArticleFig(id=1215670324562153791, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1215670311912133303, language=CN, label=图7, caption=9种农药标准溶液的总离子流图, figureFileSmall=lyTGRmCn8bwcHLVtU0Wsjg==, figureFileBig=j/K5aeyCLbsIg7YxpG9ZFg==, tableContent=null), ArticleFig(id=1215670324667011401, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1215670311912133303, language=EN, label=Table 1, caption=

GC-MS/MS analysis parameters of 9 kinds of pesticides

, figureFileSmall=null, figureFileBig=null, tableContent=
化合物 保留时间
/min
定量
离子对(m/z)
碰撞
电压/eV
定性
离子对(m/z)
碰撞
电压/eV
灭线磷 7.102 157.9/97.0 15 157.9/114.0 5
毒死蜱 9.229 196.9/169.0 15 198.9/171.0 15
水胺硫磷 9.387 120.0/92.0 10 135.9/108.0 15
虫螨腈 10.668 327.8/246.8 15 246.8/226.9 20
联苯菊酯 12.039 181.2/166.2 10 181.2/165.2 25
甲氰菊酯 12.176 181.1/152.1 25 207.9/181.0 5
三氟氯氰
菊酯
12.683 181.0/152.0 20 197.0/141.0 10
氟氰戊菊酯 14.005 156.9/107.1 15 198.9/157.0 10
氟氰戊菊酯 14.154 156.9/107.1 15 198.9/157.0 10
顺式氰戊
菊酯
14.873 167.0/125.1 10 181.0/152.1 25
), ArticleFig(id=1215670324830589269, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1215670311912133303, language=CN, label=表1, caption=

9种农药GC-MS/MS分析参数

, figureFileSmall=null, figureFileBig=null, tableContent=
化合物 保留时间
/min
定量
离子对(m/z)
碰撞
电压/eV
定性
离子对(m/z)
碰撞
电压/eV
灭线磷 7.102 157.9/97.0 15 157.9/114.0 5
毒死蜱 9.229 196.9/169.0 15 198.9/171.0 15
水胺硫磷 9.387 120.0/92.0 10 135.9/108.0 15
虫螨腈 10.668 327.8/246.8 15 246.8/226.9 20
联苯菊酯 12.039 181.2/166.2 10 181.2/165.2 25
甲氰菊酯 12.176 181.1/152.1 25 207.9/181.0 5
三氟氯氰
菊酯
12.683 181.0/152.0 20 197.0/141.0 10
氟氰戊菊酯 14.005 156.9/107.1 15 198.9/157.0 10
氟氰戊菊酯 14.154 156.9/107.1 15 198.9/157.0 10
顺式氰戊
菊酯
14.873 167.0/125.1 10 181.0/152.1 25
), ArticleFig(id=1215670324969001316, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1215670311912133303, language=EN, label=Table 2, caption=

Parameters of methodology

, figureFileSmall=null, figureFileBig=null, tableContent=
化合物 红茶 绿茶
相关
系数
LODs
/(mg/kg)
LOQs
/(mg/kg)
相关
系数
LODs
/(mg/kg)
LOQs
/(mg/kg)
灭线磷 0.9949 0.003 0.010 0.9936 0.004 0.012
毒死蜱 0.9961 0.002 0.007 0.9983 0.002 0.007
水胺硫磷 0.9946 0.003 0.009 0.9952 0.004 0.012
虫螨腈 0.9987 0.003 0.010 0.9980 0.002 0.007
联苯菊酯 0.9956 0.004 0.012 0.9952 0.003 0.010
甲氰菊酯 0.9920 0.003 0.009 0.9934 0.002 0.007
三氟氯氰
菊酯
0.9924 0.003 0.010 0.9935 0.003 0.010
氟氰戊菊酯 0.9905 0.004 0.012 0.9911 0.003 0.010
顺式氰戊
菊酯
0.9944 0.004 0.012 0.9963 0.003 0.010
), ArticleFig(id=1215670325090636152, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1215670311912133303, language=CN, label=表2, caption=

方法学参数

, figureFileSmall=null, figureFileBig=null, tableContent=
化合物 红茶 绿茶
相关
系数
LODs
/(mg/kg)
LOQs
/(mg/kg)
相关
系数
LODs
/(mg/kg)
LOQs
/(mg/kg)
灭线磷 0.9949 0.003 0.010 0.9936 0.004 0.012
毒死蜱 0.9961 0.002 0.007 0.9983 0.002 0.007
水胺硫磷 0.9946 0.003 0.009 0.9952 0.004 0.012
虫螨腈 0.9987 0.003 0.010 0.9980 0.002 0.007
联苯菊酯 0.9956 0.004 0.012 0.9952 0.003 0.010
甲氰菊酯 0.9920 0.003 0.009 0.9934 0.002 0.007
三氟氯氰
菊酯
0.9924 0.003 0.010 0.9935 0.003 0.010
氟氰戊菊酯 0.9905 0.004 0.012 0.9911 0.003 0.010
顺式氰戊
菊酯
0.9944 0.004 0.012 0.9963 0.003 0.010
), ArticleFig(id=1215670325258408334, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1215670311912133303, language=EN, label=Table 3, caption=

Comparison results with national standard method

, figureFileSmall=null, figureFileBig=null, tableContent=
项目 优化QuEChERS法 GB 23200.113—2018 QuEChERS法
提取溶剂及
用量
10 mL乙腈 15 mL乙腈-醋酸 10 mL 乙腈
是否溶剂交换
净化剂用量 25 mg MWCNTs、
50 mg PSA、
100 mg MgSO4
200 mg石墨化炭黑(graphitized carbon black, GCB)、
400 mg PSA、
400 mg 十八烷基硅烷键合硅胶C18
1200 mg MgSO4
7.5 mg GCB、
50 mg PSA、
50 mg C18
150 mg MgSO4
LOQs/(mg/kg) 0.007~0.012 0.010~0.050 0.015~0.030
检测时间 显著缩短 较长 较短
精密度(n=6)/% ≤6.92 ≤15.00 ≤12.00
), ArticleFig(id=1215670325421986207, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1215670311912133303, language=CN, label=表3, caption=

与国家标准方法对比结果

, figureFileSmall=null, figureFileBig=null, tableContent=
项目 优化QuEChERS法 GB 23200.113—2018 QuEChERS法
提取溶剂及
用量
10 mL乙腈 15 mL乙腈-醋酸 10 mL 乙腈
是否溶剂交换
净化剂用量 25 mg MWCNTs、
50 mg PSA、
100 mg MgSO4
200 mg石墨化炭黑(graphitized carbon black, GCB)、
400 mg PSA、
400 mg 十八烷基硅烷键合硅胶C18
1200 mg MgSO4
7.5 mg GCB、
50 mg PSA、
50 mg C18
150 mg MgSO4
LOQs/(mg/kg) 0.007~0.012 0.010~0.050 0.015~0.030
检测时间 显著缩短 较长 较短
精密度(n=6)/% ≤6.92 ≤15.00 ≤12.00
), ArticleFig(id=1215670325564592562, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1215670311912133303, language=EN, label=Table 4, caption=

Average recovery rates and RSDs of 9 kinds of target pesticides in black tea at 3 spiked levels (n=6)

, figureFileSmall=null, figureFileBig=null, tableContent=
化合物 0.05 mg/kg 2.50 mg/kg 5.00 mg/kg
回收率
/%
RSDs
/%
回收率
/%
RSDs
/%
回收率
/%
RSDs
/%
灭线磷 112.98 1.25 95.31 6.18 94.61 1.75
毒死蜱 113.82 3.26 96.03 6.84 96.71 3.06
水胺硫磷 116.75 4.14 104.97 4.70 103.30 4.95
虫螨腈 86.18 6.71 94.67 4.84 98.62 4.64
联苯菊酯 75.90 6.01 86.72 6.08 92.73 6.26
甲氰菊酯 118.80 6.80 85.74 6.92 93.52 6.58
三氟氯氰菊酯 102.60 6.87 85.27 4.24 88.50 6.62
氟氰戊菊酯 109.90 2.60 90.74 6.56 97.01 5.29
顺式氰戊菊酯 101.12 3.29 76.03 3.71 71.52 4.27
), ArticleFig(id=1215670326873215430, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1215670311912133303, language=CN, label=表4, caption=

红茶中9种目标农药在3种加标水平下的平均回收率和RSDs

, figureFileSmall=null, figureFileBig=null, tableContent=
化合物 0.05 mg/kg 2.50 mg/kg 5.00 mg/kg
回收率
/%
RSDs
/%
回收率
/%
RSDs
/%
回收率
/%
RSDs
/%
灭线磷 112.98 1.25 95.31 6.18 94.61 1.75
毒死蜱 113.82 3.26 96.03 6.84 96.71 3.06
水胺硫磷 116.75 4.14 104.97 4.70 103.30 4.95
虫螨腈 86.18 6.71 94.67 4.84 98.62 4.64
联苯菊酯 75.90 6.01 86.72 6.08 92.73 6.26
甲氰菊酯 118.80 6.80 85.74 6.92 93.52 6.58
三氟氯氰菊酯 102.60 6.87 85.27 4.24 88.50 6.62
氟氰戊菊酯 109.90 2.60 90.74 6.56 97.01 5.29
顺式氰戊菊酯 101.12 3.29 76.03 3.71 71.52 4.27
), ArticleFig(id=1215670327003238868, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1215670311912133303, language=EN, label=Table 5, caption=

Average recovery rates and RSDs of 9 kinds of target pesticides in green tea at 3 spiked levels

, figureFileSmall=null, figureFileBig=null, tableContent=
化合物 0.05 mg/kg 2.5 mg/kg 5.0 mg/kg
回收率
/%
RSDs
/%
回收率
/%
RSDs
/%
回收率
/%
RSDs
/%
灭线磷 109.12 1.17 81.74 3.44 88.07 3.28
毒死蜱 106.47 4.46 93.56 5.38 100.43 2.45
水胺硫磷 109.87 4.52 92.21 6.71 98.71 2.00
虫螨腈 77.33 3.49 72.04 6.82 90.11 2.53
联苯菊酯 112.80 5.72 86.54 6.35 83.15 2.33
甲氰菊酯 110.10 1.86 81.10 6.76 83.28 1.89
三氟氯氰
菊酯
99.45 1.00 73.17 6.52 73.04 1.92
氟氰戊菊酯 111.20 6.21 77.86 6.68 92.16 1.31
顺式氰戊
菊酯
110.80 4.17 72.22 6.64 71.67 2.12
), ArticleFig(id=1215670327141650914, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1215670311912133303, language=CN, label=表5, caption=

绿茶中9种目标农药在3种加标水平下的平均回收率和RSDs

, figureFileSmall=null, figureFileBig=null, tableContent=
化合物 0.05 mg/kg 2.5 mg/kg 5.0 mg/kg
回收率
/%
RSDs
/%
回收率
/%
RSDs
/%
回收率
/%
RSDs
/%
灭线磷 109.12 1.17 81.74 3.44 88.07 3.28
毒死蜱 106.47 4.46 93.56 5.38 100.43 2.45
水胺硫磷 109.87 4.52 92.21 6.71 98.71 2.00
虫螨腈 77.33 3.49 72.04 6.82 90.11 2.53
联苯菊酯 112.80 5.72 86.54 6.35 83.15 2.33
甲氰菊酯 110.10 1.86 81.10 6.76 83.28 1.89
三氟氯氰
菊酯
99.45 1.00 73.17 6.52 73.04 1.92
氟氰戊菊酯 111.20 6.21 77.86 6.68 92.16 1.31
顺式氰戊
菊酯
110.80 4.17 72.22 6.64 71.67 2.12
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多壁碳纳米管结合气相色谱-串联质谱法测定红茶和绿茶中9种农药残留
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籍术良 , 王芳 , 王建国 , 毕小艺 , 吴云 , 雷凯 *
食品安全质量检测学报 | 专题:北京市食品安全风险监测与评估 2025,16(14): 40-46
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食品安全质量检测学报 | 专题:北京市食品安全风险监测与评估 2025, 16(14): 40-46
多壁碳纳米管结合气相色谱-串联质谱法测定红茶和绿茶中9种农药残留
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籍术良 , 王芳, 王建国, 毕小艺, 吴云, 雷凯*
作者信息
  • 北京市通州区疾病预防控制中心, 北京 101100
  • 籍术良(1981—), 男, 副主任技师, 主要研究方向为食品安全检测、卫生学检验技术。E-mail:

通讯作者:

*雷凯(1980—), 男, 副主任技师, 主要研究方向为食品安全检测、卫生学检验技术。E-mail:
Determination of residues of 9 kinds of pesticides in black tea and green tea by multi-walled carbon nanotubes coupled with gas chromatography-tandem mass spectrometry
Shu-Liang JI , Fang WANG, Jian-Guo WANG, Xiao-Yi BI, Yun WU, Kai LEI*
Affiliations
  • Beijing Tongzhou District Center for Disease Control and Prevention, Beijing 101100, China
出版时间: 2025-07-25 doi: 10.19812/j.cnki.jfsq11-5956/ts.20250331009
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目的 建立基于多壁碳纳米管(multi-walled carbon nanotubes, MWCNTs)结合气相色谱-串联质谱法(gas chromatography-tandem mass spectrometry, GC-MS/MS)测定红茶和绿茶中9种农药残留的方法。方法 通过单因素实验优化关键参数, 包括MWCNTs用量、超声提取时间及提取溶剂, 并评估方法学性能, 涵盖线性范围、检出限、定量限、准确度、精密度。结果 本方法线性关系良好, 相关系数均大于0.99, 检出限和定量限分别为0.002~0.004 mg/kg和0.007~0.012 mg/kg, 红茶和绿茶中9种农药的加标回收率为71.52%~118.80%, 相对标准偏差为1.00%~6.92%。结论 本研究成功建立了一种基于MWCNTs结合GC-MS/MS的农药残留检测方法, 具有高灵敏度、高回收率、低溶剂消耗等优势, 显著提升了检测效率。实际样本检测表明, 该方法适用于市售茶叶的快速筛查, 为监管部门提供可靠的技术支持。

茶叶  /  多壁碳纳米管  /  气相色谱-串联质谱法  /  农药残留

Objective To establish a method for the determination of 9 kinds of pesticide residues in black tea and green tea based on multi-walled carbon nanotubes (MWCNTs) combined with gas chromatography-tandem mass spectrometry (GC-MS/MS). Methods The key parameters were optimized by single factor experiment, including the amount of MWCNTs, ultrasonic extraction time and extraction solvent, and the methodological performance was evaluated, including linear range, detection limit, quantification limit, accuracy and precision. Results The linear relationship of this method was excellent, with correlation coefficients all greater than 0.99. The limits of detection and limits of quantitation were 0.002-0.004 mg/kg and 0.007-0.012 mg/kg, respectively. The recoveries of 9 kinds of pesticides in black tea and green tea were 71.52%-118.80%, and the relative standard deviation were 1.00%-6.92%. Conclusion This study successfully establishes a pesticide residue detection method based on MWCNTs combined with GC-MS/MS, which has the advantages of high sensitivity, high recovery rate and low solvent consumption, and significantly improves the detection efficiency. The actual sample detection shows that this method is suitable for the rapid screening of commercially available tea, and provides reliable technical support for the regulatory authorities.

tea  /  multi-walled carbon nanotubes  /  gas chromatography-tandem mass spectrometry  /  pesticide residues
籍术良, 王芳, 王建国, 毕小艺, 吴云, 雷凯. 多壁碳纳米管结合气相色谱-串联质谱法测定红茶和绿茶中9种农药残留. 食品安全质量检测学报, 2025 , 16 (14) : 40 -46 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20250331009
Shu-Liang JI, Fang WANG, Jian-Guo WANG, Xiao-Yi BI, Yun WU, Kai LEI. Determination of residues of 9 kinds of pesticides in black tea and green tea by multi-walled carbon nanotubes coupled with gas chromatography-tandem mass spectrometry[J]. Journal of Food Safety & Quality, 2025 , 16 (14) : 40 -46 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20250331009
茶叶作为全球消费量最大的农产品之一, 其质量安全受到广泛关注。近年来, 农药残留问题因涉及食品安全和国际贸易壁垒, 成为国内外研究热点[1-5]。研究表明, 茶叶中农药残留主要来源于种植过程中的病虫害防治[6], 而我国现行农药最大残留限量(maximum residue limits, MRLs)标准与欧盟、美国等存在显著差异[7-9], 例如, 欧盟对茶叶毒死蜱的MRLs限值为0.01 mg/kg, 而我国标准为2 mg/kg, 这导致我国出口茶叶频繁遭遇技术性贸易壁垒[10-11]。此外, 农药残留的潜在健康风险已引发公共卫生领域的担忧[12-14]。因此, 开发高效、灵敏的农药残留检测方法对保障茶叶安全和提升国际竞争力具有重要意义[15-17]
目前, 快速、简便、廉价、高效、耐用、安全(QuEChERS)方法[18-19]是农药残留检测的主流前处理技术[20], 但其在复杂基质(如茶叶)中表现出净化效率不足、溶剂消耗量大等问题[21-23]。近年来, 纳米材料因其独特吸附性能被引入样品前处理领域[24-26], 其中多壁碳纳米管(multi-walled carbon nanotubes, MWCNTs)通过π-π堆积作用、吸附作用等机制可显著提升净化效率[27-28]。然而, 现有研究多聚焦于单一材料优化, 缺乏MWCNTs与传统净化剂的协同效应分析, 对茶叶基质研究不足[29-30], 且主要集中于吸附剂改性、基质效应抑制、溶剂减量及自动化联用技术等。
本研究旨在建立茶叶中农药残留的检测方法, 9种成分均为我国茶叶种植中代表性的常用杀虫剂。此外, 这些农药的化学性质适合气相色谱-串联质谱法(gas chromatography-tandem mass spectrometry, GC-MS/MS)分析, 覆盖有机磷、拟除虫菊酯等主要类别, 具有代表性[31], 为茶叶质量控制和安全保障提供了有力支持。
灭线磷、毒死蜱、水胺硫磷、虫螨腈、联苯菊酯、甲氰菊酯、三氟氯氰菊酯、氟氰戊菊酯、顺式氰戊菊酯(质量浓度100 μg/mL, 农业部环境保护科研检测所); 乙腈(色谱纯, 美国Fisher公司); 萃取盐包(北京迪马科技有限公司); 分散固相萃取管1规格[50 mg乙二胺-N-丙基硅烷化硅胶(ethylenediamine-N-propylsilanesilica gel, PSA), 25 mg MWCNTs, 100 mg 无水硫酸镁(MgSO4)]、分散固相萃取管2规格[50 mg PSA, 50 mg MWCNTs, 100 mg MgSO4]、分散固相萃取管3规格[50 mg PSA, 100 mg MWCNTs, 100 mg MgSO4](北京纳鸥科技有限公司)。
茶叶均购自于电商平台、线下实体商店和餐饮场所。
7890A-7000B气相色谱-三重四极杆质谱仪、DB-5MS色谱柱(30 m×0.25 mm, 0.25 μm)(美国Agilent科技公司); 3-18k高速台式冷冻离心机(德国Sigma公司); AL204电子天平(感量0.1 mg, 美国Mettler Toledo公司); FB15067超声波清洗仪(美国Fisherbrand公司)。
茶叶样品粉碎后称取2.0 g于50 mL离心管中, 加入10 mL乙腈提取, 涡旋混匀2 min, 超声提取30 min, 加入萃取盐包, 涡旋混匀3 min, 离心5 min (10000 r/min); 取上清液, 转移至分散固相萃取管中, 吸附杂质, 涡旋混匀2 min, 离心5 min (10000 r/min), 取1 mL过0.22 μm有机滤膜, 上机测定。
色谱条件: 色谱柱DB-5MS色谱柱(30 m×0.25 mm, 0.25 μm), 流量为1.0 mL/min; 色谱柱初始温度65 ℃, 保持1 min, 以30 ℃/min升至170 ℃, 再以15 ℃/min升至300 ℃, 保持7 min。
质谱条件: 离子源温度设定为250 ℃; 质谱接口传输温度为280 ℃; 溶剂延迟时间设置为3.917 min; 四极杆温度为150 ℃; 碰撞气选用氮气(N2); 猝灭气采用氦气(He), 流速为2.25 mL/min。分析参数如表1
本研究中引用的实验图谱由Agilent Mass Hunter提供, 实验重复6次测定, 表格由Mircosoft Excel 2021软件完成。
为筛选适用于茶叶中多类别农药残留的提取溶剂, 本研究对比了丙酮、乙酸乙酯、正己烷及乙腈的提取效率, 并做了平行测定。结果如图1~2: 高极性溶剂(丙酮、乙酸乙酯)在红茶与绿茶中均呈现高回收率(100.6%~147.4%), 显著超出理论范围(70%~120%), 分析其高极性与茶叶中多酚、色素等极性干扰物的共萃取行为有关, 导致基质干扰加剧, 影响定量准确性。非极性溶剂(正己烷)回收率波动大(红茶: 46.5%~123.9%; 绿茶: 41.3%~121.3%), 尤其对极性农药(如水胺硫磷)提取效率低, 与其溶解能力不足相关, 表明其不适用于多类别农药的同步提取。乙腈回收率稳定(红茶: 85.4%~108.9%; 绿茶: 87.9%~108.3%), 且90%目标物回收率在85%~110%范围内, 乙腈作为中等极性溶剂, 可通过疏水作用有效提取有机磷及拟除虫菊酯类农药, 同时减少糖类与色素共萃取, 与后续MWCNTs净化步骤协同降低基质干扰。
乙腈在提取效率与抗干扰能力间达到最佳平衡, 符合QuEChERS方法对溶剂选择的要求, 故选定乙腈为最优提取溶剂。
为平衡提取效率与操作成本, 本研究系统考察了超声时间(10~40 min)对农药回收率的影响。结果表明: 短时间提取(10~20 min): 红茶中回收率范围(54.9%~92.2%), 绿茶中(53.9%~90.5%), 部分农药(如联苯菊酯)回收率低于70%, 表明超声能量不足导致目标物从茶叶中释放不完全。30 min提取: 红茶与绿茶中回收率趋于稳定(红茶: 83.0%~102.4%; 绿茶: 89.1%~108.5%), 90%目标物回收率集中在80%~110%。此阶段溶剂充分渗透至茶叶基质, 目标物扩散达到动态平衡。超时提取(40 min): 回收率无明显提升(红茶: 77.0%~110.6%; 绿茶: 79.0%~108.1%), 但联苯菊酯回收率比提取30 min下降12%, 分析因长时间超声破坏茶叶脂质结构, 释放更多色素及脂肪酸, 加剧基质效应。见图3图4
30 min超声提取可兼顾效率与稳定性, 避免因时间不足导致的提取不完全或时间过长引发的副反应, 故选定为最优条件。
本研究系统考察了MWCNTs用量对茶叶基质净化的影响。在基础净化剂50 mg PSA + 100 mg MgSO₄中分别添加0、25、50、100 mg的MWCNTs, 结果显示: 无MWCNTs时, 红茶与绿茶中农药回收率显著偏低(红茶: 61.3%~79.9%; 绿茶: 60.2%~78.3%), 表明传统净化剂无法有效去除茶叶中色素及多酚类干扰物。添加25 mg MWCNTs: 回收率显著提升(红茶: 89.8%~110.9%; 绿茶: 89.0%~110.1%), 其作用机制源于MWCNTs的π-π堆积与疏水吸附特性, 捕获茶叶中脂类、色素及糖类杂质, 同时保留目标农药。过量MWCNTs (>25 mg): 竞争性吸附增强(尤其对拟除虫菊酯类农药), 导致回收率下降, 且高用量引发纳米管团聚, 降低分散效率。见图5图6
25 mg MWCNTs为最佳用量, 其比表面积与孔径分布适配茶叶基质特性, 既保障净化效率, 又避免目标物损失, 为复杂基质中农药残留分析提供可靠解决方案。
优化后升温程序为: 初始温度65 ℃, 保持1 min, 40 ℃/min升到170 ℃, 再以10 ℃/min升到260 ℃, 50 ℃/min升到300 ℃, 保持1 min; 进样口温度为280 ℃; 进样量: 1 µL; 进样方式: 不分流进样; 柱流量: 1.1 mL/min。该梯度设计基于农药沸点与极性差异, 使9种目标物在15 min内基线分离, 联苯菊酯与甲氰菊酯分离度提升, 峰对称性显著改善, 基质干扰峰减少, 满足高通量检测需求。见图7
基质效应(matrix effect, ME), 是指目标待测物在电离时, 除去目标分析物外, 受到其他物质的影响, 造成定性定量结果偏差, 分为基质增强效应和基质抑制效应。其计算公式为: ME=B/A, 其中, B为空白样基质标准溶液的响应值, A为纯溶剂标准溶液的响应值, 当ME>1.15时, 为基质增强效应, ME<0.85时, 为基质抑制效应, ME在0.85~1.15范围内时, 可以忽略。本研究在茶叶基质中, 通过引入MWCNTs作为净化材料, 9种农药的ME在0.65~1.35之间, 除毒死蜱存在轻微基质抑制效应, 其他农药均存在不同程度的基质增强效应。实验表明, 结合MWCNTs净化和基质匹配曲线定量, 可提升茶叶复杂基质中农药检测的准确性, 满足实际样本筛查需求。
用红茶、绿茶空白基质溶液分别配制混合标准溶液, 配制7个不同质量浓度的混合标准液, 分别为0.005、0.010、0.020、0.200、0.500、1.000、2.000 μg/mL的基质混合标准工作溶液, 取1 mL上机, 外标法定量。
以3倍信噪比测得各化合物的检出限(limit of detection, LOD), 10倍信噪比测得方法定量限(limit of quantitation, LOQ)。结果表明: 9种农药在0.005~2.000 μg/mL范围内线性良好, 红茶和绿茶的方法相关系数均大于0.99, 线性范围覆盖我国茶叶农药MRLs, 可满足实际样品中低浓度残留和高浓度超标场景的检测需求。LODs为0.002~0.004 mg/kg, LOQs为0.007~0.012 mg/kg, 较国家标准方法(GB 23200.113—2018《食品安全国家标准 植物源性食品中208种农药及其代谢物残留量的测定 气相色谱-质谱联用法》)的LOQs (0.01~0.05 mg/kg)降低, 灵敏度显著提升, 结果见表2表3。该结果验证了基质匹配校准策略有效消除了茶叶复杂基质的干扰, 为痕量农药残留的精准定量提供了可靠保障。
应用本方法和GB 23200.113—2018及QuEChERS法分别对部分样品进行测定对比, 见表3, 结果表明: 本方法减少了提取溶剂、净化剂的使用量, 减少了溶剂更换步骤, 缩短了检测时间, 提高了灵敏度。
在3种加标水平下(0.05、2.50、5.00 mg/kg), 覆盖我国茶叶中农药残留限量标准的10~100倍, 模拟实际样品可能的高残留场景, 同时确保方法在低、中、高浓度范围内的适用性。以每个加标水平的样品连续重复6次进样, 计算各目标农药对应的精密度。红茶中9种农药的回收率范围为71.52%~118.80%, 相对标准偏差(relative standard deviations, RSD)为1.25%~6.92%; 绿茶中9种农药的回收率范围为71.67%~112.80%, RSDs为1.00%~6.82%, 这表明方法具有良好的准确性和精密度, 能够满足茶叶中农药残留的快速定量分析需求, 结果见表4表5
应用建立的方法对10份市售茶叶(红茶5件, 绿茶5件)显示, 红茶农药残留检出率为100% (5/5), 以虫螨腈、联苯菊酯、甲氰菊酯为主, MRLs为10、5、5 mg/kg; 绿茶检出率为80% (4/5), 以虫螨腈、联苯菊酯、氯氰菊酯为主, 最大残留量为10、5、10 mg/kg; 灭线磷、水胺硫磷均未检出, 分析这两种农药残留的禁限用情况属于禁止用于茶叶。检测结果表明该方法能够有效应用于实际样品的检测, 为食品安全监管提供了可靠的技术支持。
本研究建立的MWCNTs-QuEChERS结合GC-MS/MS方法在茶叶农药残留检测中展现出显著优势。前处理效率提升, 通过引入25 mg MWCNTs, 有效吸附茶叶中色素及多酚类干扰物, 与传统QuEChERS相比, 方法回收率稳定, 精密度好, RSD<7%, 优于文献报道的8%~12%[32]。检测灵敏度突破, 优化后的色谱升温程序与多反应监测模式联用, 使9种农药在15 min内基线分离, LOD低至0.002 mg/kg, 较国家标准(GB 23200.113—2018)灵敏度提升2倍, 可精准识别痕量残留。
实际应用价值, 检测10份市售茶叶样本, 其中虫螨腈、联苯菊酯、甲氰菊酯、氯氰菊酯为主要风险因子, 为监管部门锁定高风险品类提供了数据支撑。将MWCNTs用于茶叶基质净化, 结合分段升温策略, 实现了多类别农药的高通量筛查, 为复杂食品基质中痕量污染物分析提供了新思路。
  • 通州区高层次人才发展支持计划项目(YHLD2019004)
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doi: 10.19812/j.cnki.jfsq11-5956/ts.20250331009
  • 接收时间:2025-03-31
  • 首发时间:2026-01-07
  • 出版时间:2025-07-25
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  • 收稿日期:2025-03-31
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通州区高层次人才发展支持计划项目(YHLD2019004)
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    北京市通州区疾病预防控制中心, 北京 101100

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*雷凯(1980—), 男, 副主任技师, 主要研究方向为食品安全检测、卫生学检验技术。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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