Article(id=1153433637988061952, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1153433633999282214, articleNumber=null, orderNo=null, doi=10.19812/j.cnki.jfsq11-5956/ts.20250120009, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1737302400000, receivedDateStr=2025-01-20, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1752929609056, onlineDateStr=2025-07-19, pubDate=1742832000000, pubDateStr=2025-03-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1752929609056, onlineIssueDateStr=2025-07-19, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1752929609056, creator=13701087609, updateTime=1752929609056, updator=13701087609, issue=Issue{id=1153433633999282214, tenantId=1146029695717560320, journalId=1149652044408987649, year='2025', volume='16', issue='6', pageStart='1', pageEnd='322', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=0, createTime=1752929608105, creator=13701087609, updateTime=1758086445549, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1175062977960096080, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1153433633999282214, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1175062977960096081, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1153433633999282214, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=141, endPage=150, ext={EN=ArticleExt(id=1153433638529127189, articleId=1153433637988061952, tenantId=1146029695717560320, journalId=1149652044408987649, language=EN, title=Determination of 7 kinds of pesticide residues in Cucumis sativus L. and Malus pumila Mill. by QuEChERS-liquid chromatography-tandem mass spectrometry and risk assessment, 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 method for the simultaneous determination and risk assessment of multi-class pesticide residues in Cucumis sativus L. and Malus pumila Mill. using QuEChERS-liquid chromatography-tandem mass spectrometry. Methods Fruit and vegetable samples were homogenized using acetonitrile as extraction solvent, sodium chloride and anhydrous sodium acetate as salting-out agent, and the supernatant was taken and purified by primary secondary amines (PSA), graphitized carbon black (GCB) and anhydrous magnesium sulfate reagent, and 7 kinds of pesticides were analyzed in 11 min using an Agilent Poroshell 120 EC-C18 column with gradient elution. The potential health risks associated with the consumption of Cucumis sativus L. and Malus pumila Mill. were assessed by calculating the estimated daily intake the estimated dietary intake (EDI) and hazard quotient (HQ). Results The 7 kinds of pesticides exhibited satisfactory linearity within the range of 5‒500 μg/L, with correlation coefficients more than 0.99. The limits of detection were 0.5‒1.0 μg/kg and the limits of quantification were 2.0‒4.0 μg/kg. The average recoveries ranged from 71.0% to 118.8% at three different spiked levels, with the relative standard deviations ranging from 3.1% to 12.7%. Concurrently, the HQ values of individual pesticides evaluated from the acceptable daily intake (ADI) were found to be less than 1, and their risks were within acceptable limits. Conclusion The method exhibits high accuracy, precision, and sensitivity, and is suitable for the simultaneous determination of multi-class pesticide residues in fruit and vegetable samples. Furthermore, the risk assessment results of this study demonstrated that the health risk to individuals from pesticide residues in Cucumis sativus L. and Malus pumila Mill. is within acceptable limits.

, correspAuthors=Yan TANG, 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=Yao-Nan LIU, Miao-Xiu GE, Wen-Tian ZHANG, Yue YANG, Yan TANG, Lu-Hong WEN), CN=ArticleExt(id=1153433659022496240, articleId=1153433637988061952, tenantId=1146029695717560320, journalId=1149652044408987649, language=CN, title=QuEChERS-液相色谱-串联质谱法测定黄瓜和苹果中7种农药残留量及其风险评估, 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QuEChERS前处理结合液相色谱-串联质谱法(liquid chromatography-tandem mass spectrometry, LC-MS/MS)同时测定黄瓜和苹果中7种农药残留的方法及其风险评估。方法 对果蔬样品进行均质, 采用乙腈作为提取溶剂, 氯化钠和无水乙酸钠作为盐析试剂, 取其上清液通过N-丙基乙二胺(primary secondary amines, PSA)、石墨化炭黑(graphitized carbon black, GCB)和无水硫酸镁试剂进行净化, 使用Agilent Poroshell 120 EC-C18色谱柱, 以梯度洗脱的方式在11 min内对7种不同类别的农药进行检测分析。通过计算估计每日摄入量(estimate dietary intake, EDI)和危害商(hazard quotient, HQ)来评估食用黄瓜和苹果可能存在的潜在健康风险。结果 7种农药在5~500 μg/L范围内具有良好的线性关系, 相关系数均>0.99。其检出限为0.5~1.0 μg/kg, 定量限为2.0~4.0 μg/kg。在低、中、高3个不同加标水平下的平均回收率为71.0%~118.8%, 相对标准偏差为3.1%~12.7%。同时根据每日允许摄入量(acceptable daily intake, ADI)评估的单个农药的HQ值远远小于1, 其风险在可接受的范围内。结论 该方法具有较高的准确度、精密度和灵敏度, 适用于同时测定果蔬中多类别农药的残留。此外, 本研究中风险评估结果表明黄瓜和苹果中农药残留对人们健康风险在可接受的范围内。

, correspAuthors=唐燕, authorNote=null, correspAuthorsNote=
* 唐燕(1994—), 女, 博士, 助理研究员, 主要研究方向为食品安全检测。E-mail:
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刘耀楠(2000—), 女, 硕士, 主要研究方向为食品安全检测。E-mail:

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Food Control, 2023, 153: 109903., articleTitle=Occurrence and exposure assessment of chlorate and perchlorate in food and drinking water from Fujian, China, refAbstract=null)], funds=[Fund(id=1175086823366869209, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637988061952, awardId=2023ZY01073, language=CN, fundingSource=工信部产业基础再造和制造业高质量发展专项(2023ZY01073), fundOrder=null, country=null), Fund(id=1175086823417200858, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637988061952, awardId=2023S002, language=CN, fundingSource=宁波市公益性科技计划项目(2023S002), fundOrder=null, country=null)], companyList=[AuthorCompany(id=1175086819092873358, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637988061952, xref=null, ext=[AuthorCompanyExt(id=1175086819101261967, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637988061952, companyId=1175086819092873358, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1. 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注: A. Agilent Poroshell 120 EC-C18柱(4.6 mm×50 mm, 2.7 μm); B. Agilent Poroshell 120 EC-C18柱(3.0 mm×100 mm, 2.7 μm); C. Gemini C18柱(2 mm×50 mm, 3 μm)。

, figureFileSmall=OPhYuP8ytCLr1HO168SnNw==, figureFileBig=YlZ2v88ASEBlbUIFR7l4Jg==, tableContent=null), ArticleFig(id=1175086822452510925, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637988061952, language=EN, label=Fig.3, caption=Optimization of extraction and purification conditions, figureFileSmall=7D76WzGIXzcu0Bsj/RTSMg==, figureFileBig=3Xvot7ELu9pEztkJf/QxhQ==, tableContent=null), ArticleFig(id=1175086822519619790, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637988061952, language=CN, label=图3, caption=提取、净化条件优化图

注: a. 不同提取溶剂的选择; b. 样品与提取溶剂料液比的确定; c. 盐析和不同脱水试剂的组合; d. 脱水试剂用量的优化; e. 不同净化试剂的组合: ①~⑤分别为PSA+C18+MgSO4、MWCNTs+MgSO4、GCB+MgSO4、PSA+GCB+MgSO4、PSA+C18+GCB+ MgSO4; f. 净化试剂用量的优化: ①~④分别为25 mg PSA+10 mg GCB+150 mg MgSO4、50 mg PSA+10 mg GCB+150 mg MgSO4、25 mg PSA+150 mg MgSO4、25 mg PSA+5 mg GCB+150 mg MgSO4

, figureFileSmall=7D76WzGIXzcu0Bsj/RTSMg==, figureFileBig=3Xvot7ELu9pEztkJf/QxhQ==, tableContent=null), ArticleFig(id=1175086822565757135, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637988061952, language=EN, label=Table 1, caption=

Parameter information for 7 kinds of targets

, figureFileSmall=null, figureFileBig=null, tableContent=
序号 目标物 保留时间
/min
母离子(m/z) 子离子(m/z) 电离模式
1 乙酰甲胺磷 2.88 206.0 164.9* ESI+
116.9
2 氧乐果 3.07 214.0 182.9* ESI+
124.9
3 啶虫脒 4.17 223.1 125.9* ESI+
187.0
4 噻虫嗪 3.67 292.0 211.0* ESI+
181.0
5 克百威 4.83 222.1 165.0* ESI+
123.0
6 灭多威 3.59 163.1 88.1* ESI+
106.1
7 氟虫腈 5.56 434.9 329.9* ESI-
277.9
), ArticleFig(id=1175086822620283088, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637988061952, language=CN, label=表1, caption=

7种目标物的参数信息

, figureFileSmall=null, figureFileBig=null, tableContent=
序号 目标物 保留时间
/min
母离子(m/z) 子离子(m/z) 电离模式
1 乙酰甲胺磷 2.88 206.0 164.9* ESI+
116.9
2 氧乐果 3.07 214.0 182.9* ESI+
124.9
3 啶虫脒 4.17 223.1 125.9* ESI+
187.0
4 噻虫嗪 3.67 292.0 211.0* ESI+
181.0
5 克百威 4.83 222.1 165.0* ESI+
123.0
6 灭多威 3.59 163.1 88.1* ESI+
106.1
7 氟虫腈 5.56 434.9 329.9* ESI-
277.9
), ArticleFig(id=1175086822683197649, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637988061952, language=EN, label=Table 2, caption=

Exposure and risk assessment parameters

, figureFileSmall=null, figureFileBig=null, tableContent=
农药 ADI
/(mg/kg bw)
BW/kg CR/(g/day)
蔬菜 水果
乙酰甲胺磷 0.0300 60 251.38* 106.97*
氧乐果 0.0003
啶虫脒 0.0700
噻虫嗪 0.0800
克百威 0.0010
灭多威 0.0200
氟虫腈 0.0002
), ArticleFig(id=1175086822754500818, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637988061952, language=CN, label=表2, caption=

用于暴露及风险评估的参数

, figureFileSmall=null, figureFileBig=null, tableContent=
农药 ADI
/(mg/kg bw)
BW/kg CR/(g/day)
蔬菜 水果
乙酰甲胺磷 0.0300 60 251.38* 106.97*
氧乐果 0.0003
啶虫脒 0.0700
噻虫嗪 0.0800
克百威 0.0010
灭多威 0.0200
氟虫腈 0.0002
), ArticleFig(id=1175086822809026771, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637988061952, language=EN, label=Table 3, caption=

Linear equations, limits of detection and limits of quantification of the 7 kinds of pesticides in this method

, figureFileSmall=null, figureFileBig=null, tableContent=
农药 线性方程 相关系数 检出限/(μg/kg) 定量限/(μg/kg)
乙酰甲胺磷 Y=16.32X+456.52 0.9922 1.0 4.0
氧乐果 Y=13.21X+598.00 0.9914 1.0 4.0
啶虫脒 Y=8.6113X+139.98 0.9916 1.0 4.0
噻虫嗪 Y=62.981X+65.44 0.9924 1.0 4.0
克百威 Y=20.811X+936.42 0.9903 0.5 2.0
灭多威 Y=29.225X+352.45 0.9939 1.0 4.0
氟虫腈 Y=142.63X+571.12 0.9911 0.5 2.0
), ArticleFig(id=1175086822888718548, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637988061952, language=CN, label=表3, caption=

本方法中7种农药的线性方程、检出限和定量限

, figureFileSmall=null, figureFileBig=null, tableContent=
农药 线性方程 相关系数 检出限/(μg/kg) 定量限/(μg/kg)
乙酰甲胺磷 Y=16.32X+456.52 0.9922 1.0 4.0
氧乐果 Y=13.21X+598.00 0.9914 1.0 4.0
啶虫脒 Y=8.6113X+139.98 0.9916 1.0 4.0
噻虫嗪 Y=62.981X+65.44 0.9924 1.0 4.0
克百威 Y=20.811X+936.42 0.9903 0.5 2.0
灭多威 Y=29.225X+352.45 0.9939 1.0 4.0
氟虫腈 Y=142.63X+571.12 0.9911 0.5 2.0
), ArticleFig(id=1175086822960021717, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637988061952, language=EN, label=Table 4, caption=

Average recoveries and precision of 7 kinds of pesticides in this method (n=5)

, figureFileSmall=null, figureFileBig=null, tableContent=
农药 加标浓度/(μg/kg) 黄瓜 苹果
回收率/% RSDs/% 回收率/% RSDs/%
乙酰甲胺磷 10 77.7 12.4 88.9 10.2
20 71.0 7.9 97.4 5.8
50 103.9 9.3 110.1 5.4
氧乐果 10 97.2 6.1 99.9 3.7
20 95.1 8.1 104.9 4.1
50 99.6 9.3 108.4 8.0
啶虫脒 10 101.6 9.0 102.5 7.0
20 85.9 9.2 108.1 3.2
50 93.4 7.5 106.7 3.1
噻虫嗪 10 109.5 12.7 111.6 7.2
20 86.0 5.2 117.2 4.2
50 108.0 11.0 113.4 8.1
克百威 10 95.5 3.6 78.4 7.3
20 77.1 4.5 84.9 7.2
50 90.7 11.5 77.2 4.4
灭多威 10 106.3 8.0 109.0 9.6
20 96.1 5.1 111.2 9.8
50 107.7 6.9 106.9 6.3
氟虫腈 10 91.7 11.2 88.8 4.5
20 117.1 6.3 114.1 3.8
50 118.8 7.1 112.4 5.4
), ArticleFig(id=1175086823031324886, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637988061952, language=CN, label=表4, caption=

本方法中7种农药的平均回收率及精密度(n=5)

, figureFileSmall=null, figureFileBig=null, tableContent=
农药 加标浓度/(μg/kg) 黄瓜 苹果
回收率/% RSDs/% 回收率/% RSDs/%
乙酰甲胺磷 10 77.7 12.4 88.9 10.2
20 71.0 7.9 97.4 5.8
50 103.9 9.3 110.1 5.4
氧乐果 10 97.2 6.1 99.9 3.7
20 95.1 8.1 104.9 4.1
50 99.6 9.3 108.4 8.0
啶虫脒 10 101.6 9.0 102.5 7.0
20 85.9 9.2 108.1 3.2
50 93.4 7.5 106.7 3.1
噻虫嗪 10 109.5 12.7 111.6 7.2
20 86.0 5.2 117.2 4.2
50 108.0 11.0 113.4 8.1
克百威 10 95.5 3.6 78.4 7.3
20 77.1 4.5 84.9 7.2
50 90.7 11.5 77.2 4.4
灭多威 10 106.3 8.0 109.0 9.6
20 96.1 5.1 111.2 9.8
50 107.7 6.9 106.9 6.3
氟虫腈 10 91.7 11.2 88.8 4.5
20 117.1 6.3 114.1 3.8
50 118.8 7.1 112.4 5.4
), ArticleFig(id=1175086823119405271, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637988061952, language=EN, label=Table 5, caption=

EDI and HQ of pesticides detected in cucumber and apple samples

, figureFileSmall=null, figureFileBig=null, tableContent=
样品 检出农药 检出率/% 平均检出浓度
/(mg/kg)
P95检出浓度
/(mg/kg)
情景A暴露风险 情景B暴露风险
EDI/(μg/kg bw) HQ EDI/(μg/kg bw) HQ
黄瓜 啶虫脒 45 0.0611 0.1620 0.2559 0.0037 0.6788 0.0097
噻虫嗪 82 0.1404 0.7277* 0.5880 0.0074 3.0487 0.0381
苹果 啶虫脒 64 0.0107 0.0414 0.0191 0.0003 0.0738 0.0011
噻虫嗪 3 0.0043 0.0043 0.0077 0.0001 0.0077 0.0001
), ArticleFig(id=1175086823228457176, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153433637988061952, language=CN, label=表5, caption=

黄瓜和苹果样品中检测到农药的EDI和HQ

, figureFileSmall=null, figureFileBig=null, tableContent=
样品 检出农药 检出率/% 平均检出浓度
/(mg/kg)
P95检出浓度
/(mg/kg)
情景A暴露风险 情景B暴露风险
EDI/(μg/kg bw) HQ EDI/(μg/kg bw) HQ
黄瓜 啶虫脒 45 0.0611 0.1620 0.2559 0.0037 0.6788 0.0097
噻虫嗪 82 0.1404 0.7277* 0.5880 0.0074 3.0487 0.0381
苹果 啶虫脒 64 0.0107 0.0414 0.0191 0.0003 0.0738 0.0011
噻虫嗪 3 0.0043 0.0043 0.0077 0.0001 0.0077 0.0001
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QuEChERS-液相色谱-串联质谱法测定黄瓜和苹果中7种农药残留量及其风险评估
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刘耀楠 1, 2 , 葛苗秀 1, 2 , 章文天 3 , 杨越 1 , 唐燕 1, 3, * , 闻路红 1, 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(6): 141-150
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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(6): 141-150
QuEChERS-液相色谱-串联质谱法测定黄瓜和苹果中7种农药残留量及其风险评估
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刘耀楠1, 2 , 葛苗秀1, 2, 章文天3, 杨越1, 唐燕1, 3, * , 闻路红1, 3
作者信息
  • 1.宁波大学高等技术研究院, 宁波 315211
  • 2.宁波大学食品科学与工程学院, 宁波 315211
  • 3.宁波华仪宁创智能科技有限公司, 宁波 315000
  • 刘耀楠(2000—), 女, 硕士, 主要研究方向为食品安全检测。E-mail:

通讯作者:

* 唐燕(1994—), 女, 博士, 助理研究员, 主要研究方向为食品安全检测。E-mail:
Determination of 7 kinds of pesticide residues in Cucumis sativus L. and Malus pumila Mill. by QuEChERS-liquid chromatography-tandem mass spectrometry and risk assessment
Yao-Nan LIU1, 2 , Miao-Xiu GE1, 2, Wen-Tian ZHANG3, Yue YANG1, Yan TANG1, 3, * , Lu-Hong WEN1, 3
Affiliations
  • 1. The Research Institute of Advanced Technologies, Ningbo University, Ningbo 315211, China
  • 2. College of Food Science and Engineering, Ningbo University, Ningbo 315211, China
  • 3. China Innovation Instrument Co., Ltd., Ningbo 315000, China
出版时间: 2025-03-25 doi: 10.19812/j.cnki.jfsq11-5956/ts.20250120009
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目的 建立QuEChERS前处理结合液相色谱-串联质谱法(liquid chromatography-tandem mass spectrometry, LC-MS/MS)同时测定黄瓜和苹果中7种农药残留的方法及其风险评估。方法 对果蔬样品进行均质, 采用乙腈作为提取溶剂, 氯化钠和无水乙酸钠作为盐析试剂, 取其上清液通过N-丙基乙二胺(primary secondary amines, PSA)、石墨化炭黑(graphitized carbon black, GCB)和无水硫酸镁试剂进行净化, 使用Agilent Poroshell 120 EC-C18色谱柱, 以梯度洗脱的方式在11 min内对7种不同类别的农药进行检测分析。通过计算估计每日摄入量(estimate dietary intake, EDI)和危害商(hazard quotient, HQ)来评估食用黄瓜和苹果可能存在的潜在健康风险。结果 7种农药在5~500 μg/L范围内具有良好的线性关系, 相关系数均>0.99。其检出限为0.5~1.0 μg/kg, 定量限为2.0~4.0 μg/kg。在低、中、高3个不同加标水平下的平均回收率为71.0%~118.8%, 相对标准偏差为3.1%~12.7%。同时根据每日允许摄入量(acceptable daily intake, ADI)评估的单个农药的HQ值远远小于1, 其风险在可接受的范围内。结论 该方法具有较高的准确度、精密度和灵敏度, 适用于同时测定果蔬中多类别农药的残留。此外, 本研究中风险评估结果表明黄瓜和苹果中农药残留对人们健康风险在可接受的范围内。

QuEChERS  /  液相色谱-串联质谱法  /  果蔬  /  农药残留  /  风险评估

Objective To establish a method for the simultaneous determination and risk assessment of multi-class pesticide residues in Cucumis sativus L. and Malus pumila Mill. using QuEChERS-liquid chromatography-tandem mass spectrometry. Methods Fruit and vegetable samples were homogenized using acetonitrile as extraction solvent, sodium chloride and anhydrous sodium acetate as salting-out agent, and the supernatant was taken and purified by primary secondary amines (PSA), graphitized carbon black (GCB) and anhydrous magnesium sulfate reagent, and 7 kinds of pesticides were analyzed in 11 min using an Agilent Poroshell 120 EC-C18 column with gradient elution. The potential health risks associated with the consumption of Cucumis sativus L. and Malus pumila Mill. were assessed by calculating the estimated daily intake the estimated dietary intake (EDI) and hazard quotient (HQ). Results The 7 kinds of pesticides exhibited satisfactory linearity within the range of 5‒500 μg/L, with correlation coefficients more than 0.99. The limits of detection were 0.5‒1.0 μg/kg and the limits of quantification were 2.0‒4.0 μg/kg. The average recoveries ranged from 71.0% to 118.8% at three different spiked levels, with the relative standard deviations ranging from 3.1% to 12.7%. Concurrently, the HQ values of individual pesticides evaluated from the acceptable daily intake (ADI) were found to be less than 1, and their risks were within acceptable limits. Conclusion The method exhibits high accuracy, precision, and sensitivity, and is suitable for the simultaneous determination of multi-class pesticide residues in fruit and vegetable samples. Furthermore, the risk assessment results of this study demonstrated that the health risk to individuals from pesticide residues in Cucumis sativus L. and Malus pumila Mill. is within acceptable limits.

QuEChERS  /  liquid chromatography-tandem mass spectrometry  /  fruits and vegetables  /  pesticide residues  /  risk assessment
刘耀楠, 葛苗秀, 章文天, 杨越, 唐燕, 闻路红. QuEChERS-液相色谱-串联质谱法测定黄瓜和苹果中7种农药残留量及其风险评估. 食品安全质量检测学报, 2025 , 16 (6) : 141 -150 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20250120009
Yao-Nan LIU, Miao-Xiu GE, Wen-Tian ZHANG, Yue YANG, Yan TANG, Lu-Hong WEN. Determination of 7 kinds of pesticide residues in Cucumis sativus L. and Malus pumila Mill. by QuEChERS-liquid chromatography-tandem mass spectrometry and risk assessment[J]. Journal of Food Safety & Quality, 2025 , 16 (6) : 141 -150 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20250120009
随着生活水平的提高, 果蔬的市场消费类也在日益增加。然而果蔬需求量的增加[1], 导致农药的广泛使用以提高果蔬产量[2]。长期过量使用或滥用农药使得果蔬、土壤、水体中有大量的农药残留, 会通过多种直接或间接的方式对动物或者人体带来严重危害[3]。因此, 对果蔬中的农药残留检测以及对人群每日摄入果蔬所含农药的风险进行评估是十分有必要的。
果蔬基质较为复杂, 其中色素、有机酸、糖分等因素是影响待测物准确定量的重要因素[4]。常见的前处理方法包括固相萃取法[5-7]、液液萃取法[8-10]、QuEChERS法[11-13]等, 其中QuEChERS法因其具有简便、快速、高效等优点被广泛应用于果蔬中农药残留检测的优选前处理方法[14]。目前对果蔬中农药残留进行检测的方法主要包括快速检测法和仪器检测法。快速检测法不需要依赖复杂仪器, 能够在较短时间内给出检测结果, 如孙召伟等[15]设计开发了一种一步式快速检测试纸条, 可简单快速筛查果蔬中有机磷和氨基甲酸酯类农药的残留。但快速检测法的准确性通常低于仪器检测法, 易出现假阳性结果[16]。仪器检测法包括气相色谱法[1718]、气相色谱-质谱法[1920]、高效液相色谱法[2122]、液相色谱-质谱法[2324]等, 其具有灵敏度高、分离度好、准确性高等优点。但色谱-质谱法的质谱仪器无法便携移动且仪器维护成本高, 操作烦琐[25]。本研究采用的液相色谱-质谱联用仪偏向小型化, 更加贴近目前对检测仪器进行“便携、小型”的设计要求。
目前, 果蔬中农药残留检测及对人体的健康风险评估已被广泛报道[2627]。KUANG等[28]分析了150个荔枝样本中可能存在的57种农药, 发现在检出的17种农药中, 只有克百威超出其最高残留限量。在中国石家庄的一项研究中, 在110个蔬菜和水果样品中检测了420种农药, 检出频率较高的农药包括了啶虫脒(19.1%)和噻虫嗪(16.4%)[29]。YU等[30]对7种蔬菜的214个样本进行了有机磷农药的检测, 结果显示23.4%的样本含有高于最大残留限量的有机磷农药。此外氟虫腈因其防治某些害虫的效果较好, 暂未找到合适的替代品, 导致氟虫腈非法使用, 在油麦菜、白菜、豇豆等蔬菜中均有氟虫腈检出的报道[31]
针对这些检出频率较高的农药, 本研究通过优化提取、净化条件以及色谱、质谱条件等参数, 建立了一种QuEChERS前处理法结合液相色谱-串联质谱法同时测定黄瓜和苹果中多类别农药的分析方法, 同时对人群每日摄入果蔬中农药含量的风险进行评估, 以期为果蔬中农药残留的有效监管提供可靠的检测技术支撑。
66份黄瓜和苹果样品均于2024年购自浙江省宁波市当地的超市和零售店。所有样品均切成小块, 用榨汁机均质, 冷冻保存。
乙腈(色谱级, 美国TEDIA公司); 甲酸(formic acid, FA)(纯度≥96%, 上海阿拉丁试剂有限公司); 氯化钠(NaCl)、无水乙酸钠、无水硫酸镁(MgSO4)(分析级, 国药集团化学试剂有限公司); N-丙基乙二胺(primary secondary amines, PSA)、石墨化炭黑(graphitized carbon black, GCB) (120~400目, 批号T4780030, 上海安谱实验科技股份有限公司); 7种农药(乙酰甲胺磷、氧乐果、啶虫脒、噻虫嗪、克百威、灭多威和氟虫腈)标准品[质量浓度1000 μg/mL, 坛墨科技有限公司(中国江苏)]; 超纯水由美国Millipore公司的Miili-Q系统生产。
LC-CRAIV110液相色谱-串联小型化质谱仪(宁波华仪宁创智能科技有限公司)、Agilent Poroshell 120 EC-C18色谱柱(4.6 mm×50 mm, 2.7 μm)、0.22 μm聚四氟乙烯膜过滤器(美国Agilent公司); HY-4B恒速振荡器(绍兴市苏泊仪器有限公司); ST-16R高速冷冻离心机(美国Thermo Fisher Scientific公司); SQP Secura125-1CN电子天平(感量1.0 mg, 北京赛多利斯科学仪器有限公司)。
混合标准中间溶液: 分别吸取7种农药(质量浓度1000 μg/mL)标准品溶液用乙腈配制成质量浓度为10.0 μg/mL的标准混合溶液, 置于‒20 ℃冰箱内保存。
整个前处理过程如图1所示。称取5.0 g均质样品于50 mL塑料离心管中, 加入10 mL乙腈混匀, 放置恒速振荡器内剧烈振荡3 min, 加入1.5 g NaCl和4.5 g无水乙酸钠, 混匀后以8000 r/min的转速离心10 min。吸取1 mL上清液至含有净化材料(25 mg PSA、5 mg GCB和150 mg MgSO4)的15 mL离心管中, 涡旋混匀后8000 r/min离心5 min。使用1 mL注射器吸取离心后的上清液过0.22 μm聚四氟乙烯滤膜, 上机测定。
色谱条件: 色谱柱为Agilent Poroshell 120 EC C18柱(4.6 mm×50 mm, 2.7 μm)。流动相A为超纯水; 流动相B为甲醇溶液。柱温为30 ℃, 样品室温度为10 ℃; 进样量为5 μL; 流速为0.6 mL/min。液相梯度洗脱程序: 0~1.0 min, 95% A; 1.0~5.0 min, 95%~2% A; 5.0~10.0 min, 2% A; 10.0~10.5 min, 2%~95% A; 10.5~11.0 min, 95% A。
离子源参数条件: 电喷雾离子源电压为4.5 kV, 鞘气流量为5 L/min, 辅助气流量为4 L/min, 离子源温度为500 ℃, 进样口温度为200 ℃。各待测目标物的离子对、扫描离子模式以及保留时间等参数见表1
为评估与食用蔬菜和水果引起的非传染性疾病相关的潜在健康风险, 通过计算估计每日摄入量(estimate dietary intake, EDI)来评估危害商(hazard quotient, HQ)[32]。EDI和HQ可通过以下公式(1)~(2)计算:
$\mathrm{EDI}=\frac{C\times \text{CR}\times \text{AF}}{\text{BW}}$
$\mathrm{HQ}=\frac{\mathrm{EDI}}{\mathrm{ADI}}$
式中: EDI为成人膳食中待测物的估计每日摄入量, μg/kg bw; C为水果或蔬菜中待测物的浓度, μg/kg; CR为成人每日蔬菜或水果的摄入量, g/d; AF为吸收因子(假定为100%); BW表示体重(60 kg); ADI为每日允许摄入量, 该数据来源于GB 2763—2021《食品安全国家标准 食品中农药最大残留限量》。HQ值大于1(风险水平)表示通过蔬菜和水果的膳食暴露剂量可能会对健康造成不良影响。用于暴露及风险评估的参数见表2
通过Mass Master 0.3.0软件(宁波华仪宁创智能科技有限公司)分析数据, 采用Origin 2021进行制图, 每组实验平行重复5次。
为优化多类别农药色谱峰的分离以及出峰峰型的改善, 分别采用Agilent Poroshell 120 EC-C18柱(4.6 mm×50 mm, 2.7 μm)、Agilent Poroshell 120 EC-C18柱(3.0 mm×100 mm, 2.7 μm)和Gemini C18柱(2 mm×50 mm, 3 μm) 3种色谱柱。结果(图2)发现在Agilent Poroshell 120 EC-C18柱(4.6 mm× 50 mm, 2.7 μm)上的各待测物的分离峰型最为良好, 且具有较高的响应强度。
配制质量浓度为200 ng/mL的单标准溶液依次注入质谱系统, 通过调整隔离电压、隔离宽度、碎裂电压和碎裂时间等参数找寻目标农药正确的母离子和子离子对, 并进行优化以获得各目标农药定性和定量离子对的最大灵敏度。优化后的7种农药相关参数详见1.3.3节。
考虑到果蔬类样品基质较为复杂, 并且色素含量高, 而乙腈是农药残留常用的有机提取溶剂, 较难提取非极性的色素类杂质[33], 因此采用乙腈、0.5%甲酸乙腈和1%甲酸-乙腈为提取溶剂对样品进行前处理, 发现纯乙腈作为提取溶剂时, 回收率在40%~80%范围内的待测物的数量最多(见图3a); 同时考虑到样品和提取溶剂的料液比对待测物回收率的影响[34], 将样品与提取溶剂的料液比调整为1:1和1:2两个比例进行比较, 结果表明待测物在1:2比例下的回收率均在50%之上(见图3b)。综上, 确定乙腈为提取溶剂, 样品与提取溶剂的料液比为1:2。
前处理过程中, 盐析试剂NaCl具有可调节溶液极性且有效分层水样和有机溶剂的特点; 脱水试剂可有效去除样品中的水分, 避免对后续净化和检测步骤的干扰[35]。本研究对盐析试剂进行组合优化同时对其用量进行了考察, 结果表明在1.5 g NaCl和4.5 g无水乙酸钠的组合下, 待测目标物的回收率在55%~90%范围内(见图3c~d)。
QuEChERS方法是利用吸附剂填料与基质之间的杂质相互作用, 通过分散固相萃取去除杂质[36]。该方法净化中常用的吸附剂包括PSA、GCB、多壁碳纳米管(multi-walled carbon nanotubes, MWCNTs)和十八烷基键合硅胶固定相(octadecylsilane, C18)[37]。PSA具有较强的离子交换能力, 能够有效去除有机酸类、糖类、金属离子等物质, 但对色素的吸附能力不够理想[38]。GCB对大部分的色素具有高效吸附的能力, 对降低基质干扰具有明显作用[39]。MWCNTs具有高比表面积、多层中空结构, 可通过π-π和疏水相互作用吸附色素, 对基质中的干扰成分进行针对性清除[40]。C18是一种反相吸附材料, 能有效去除脂类、胆固醇和亲脂化合物[41]
考虑到果蔬样品基质的复杂性, 本研究将以上净化材料进行组合优化以及用量优化。由图3e可知, C18相对于PSA的农药回收率低, 且GCB相对于MWCNTs对果蔬基质中色素的吸附更加高效。最终结果比较表明在PSA和GCB的组合下, 各农药的回收率均在75%以上。同时对吸附剂的用量优化可知(图3f), 过量添加PSA、不加或者GCB过量的情况下均会影响到农药的回收率, 这可能是因为PSA过量会与一些农药中的-OH或-NH官能团形成氢键,造成对这些农药的吸附, GCB含量过高时易吸附同样具有平面结构的其他物质[4243]。因此, 本研究采用25 mg PSA、5 mg GCB和150 mg MgSO4的组合作为净化条件, 在该条件下, 各种农药的回收率均良好, 在80%~120%范围之内。
在最优条件下, 对所建立方法的线性范围、检出限和定量限进行了评估。结果如表3所示, 待测目标物的线性范围在5~500 μg/L之内, 线性关系良好, 相关系数均大于0.99。向各空白样品添加不同浓度的标准物质, 并以信噪比的3倍和10倍确定检出限和定量限, 为0.5~1.0 μg/kg和2.0~4.0 μg/kg。
同时, 以黄瓜和苹果为样品基质, 对其进行添加回收实验, 评估该方法的准确度和精密度。该方法的准确性良好, 在黄瓜和苹果中目标农药的平均回收率为71.0%~118.8%, 相对标准偏差(relative standard deviation, RSD)为3.1%~12.7%, 表明该方法具备良好的精密度, 结果如表4所示。本研究建立的多类别农药检测方法具有与GB/T 20769—2008方法相当的灵敏度、良好的准确性和精密度, 并且前处理的步骤简单, 耗费的试剂与有机溶剂相对较少, 适合大规模推广和使用。并且所使用的检测仪器偏向小型化, 维修成本相对较低, 更加贴合目前对检测仪器进行“便携、小型”的设计要求。
为了研究该方法的实际应用性, 应用验证的方法分析了33个黄瓜样品和33个苹果样品。表5列出了黄瓜和苹果样品中农药的检出率和检出浓度的数据。结果显示黄瓜和苹果样本中均检出新烟碱类农药(neonicotinoid insecticides, NIs)(啶虫脒和噻虫嗪), 其检出率分别为45%~82%和3%~64%, 平均浓度分别为0.0611~0.1404 mg/kg和0.0043~0.0107 mg/kg。在2份黄瓜样品中检出噻虫嗪浓度超出GB 2763—2021规定的限量标准(0.5 mg/kg), 最大浓度可达1.2815 mg/kg。2024年的一项研究报告称[44], 浙江省的NIs检出率为42.1%~82.9%, 表明蔬菜和水果普遍受到NIs污染。这可能是因为啶虫脒和噻虫嗪的化学结构性质稳定, 不易分解, 有利于它们在环境中长期存留和积累, 导致它们的检出率升高[45]。此外, 本研究关于实际样品的检测未检出有机磷、氨基甲酸酯类农药和氟虫腈农药, 这与梁秀美等[46]、胡宁恩等[47]的结果一致。果蔬中除杀菌剂外最常检出的农药是烟碱类农药, 其检出率可达52.1%。
本研究对两种不同情景(A和B)下的农药摄入可能对人类健康造成的潜在风险进行了评估。在情景A中, 使用的是平均浓度和平均日蔬果消费量; 在情景B中, 使用的是第95百分位数(P95)的暴露浓度和平均日蔬果消费量[48]。分别计算了估计每日摄入量(EDI, μg/kg bw)和危害商(hazard quotient, HQ)。结果见表5所示, 在A情景下, 成人通过黄瓜和苹果摄入的啶虫脒和噻虫嗪的EDI值在0.0077~0.5880 μg/kg bw范围内。同样, 在B情景下, 摄入的啶虫脒和噻虫嗪的EDI值在0.0077~3.0487 μg/kg bw范围内。显而易见, 黄瓜和苹果中计算农药的EDI值明显低于每日允许摄入量ADI值。同时根据每日允许摄入量估算的单个农药的HQ值远远小于1, 表明本研究中黄瓜和苹果农药残留对人们健康风险在可接受的范围内。虽然本研究的研究没有发现所调查的蔬果样本中的农药会增加不良健康影响的风险, 但有检出样品中噻虫嗪超标, 存在潜在风险, 仍需要对果蔬中烟碱类农药进行长期监测, 以保障居民的果蔬摄入安全。
本研究建立了一种QuEChERS结合液相色谱-串联质谱对果蔬中所含多类别农药的残留进行检测, 7种目标物在5~500 μg/L范围内表现出良好的线性, 相关系数均大于0.99, 在低、中、高3个浓度加标水平下的回收率范围为71.0%~118.8%, 相对标准偏差为3.1%~12.7%。表明该方法的前处理简单、灵敏度高、结果准确, 能够满足同时对多类别农药进行检测的需求。此外, 通过对人群日常摄入果蔬所接触到的农药含量的风险进行了评估, 结果显示其风险均在可接受范围内。
  • 工信部产业基础再造和制造业高质量发展专项(2023ZY01073)
  • 宁波市公益性科技计划项目(2023S002)
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2025年第16卷第6期
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doi: 10.19812/j.cnki.jfsq11-5956/ts.20250120009
  • 接收时间:2025-01-20
  • 首发时间:2025-07-19
  • 出版时间:2025-03-25
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  • 收稿日期:2025-01-20
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工信部产业基础再造和制造业高质量发展专项(2023ZY01073)
宁波市公益性科技计划项目(2023S002)
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    1.宁波大学高等技术研究院, 宁波 315211
    2.宁波大学食品科学与工程学院, 宁波 315211
    3.宁波华仪宁创智能科技有限公司, 宁波 315000

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* 唐燕(1994—), 女, 博士, 助理研究员, 主要研究方向为食品安全检测。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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