Article(id=1153986581166805829, tenantId=1146029695717560320, journalId=1149652044408987649, issueId=1153986579971429187, articleNumber=null, orderNo=null, doi=10.19812/j.cnki.jfsq11-5956/ts.20241218001, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1734451200000, receivedDateStr=2024-12-18, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1753061440984, onlineDateStr=2025-07-21, pubDate=1740412800000, pubDateStr=2025-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1753061440984, onlineIssueDateStr=2025-07-21, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1753061440984, creator=13701087609, updateTime=1753061440984, 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=53, endPage=61, ext={EN=ArticleExt(id=1153986581644956486, articleId=1153986581166805829, tenantId=1146029695717560320, journalId=1149652044408987649, language=EN, title=Research progress on detection technology of pesticide residues in edible agricultural products, 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=
Edible agricultural products are primary products derived from agriculture, forestry, livestock husbandry and fishery, which are important components of people's daily diet. The widespread use of pesticides in recent years has effectively promoted agricultural development, but the problem of pesticide residue pollution caused by illegal use of banned or restricted pesticides and unscientific pesticide application has attracted considerable attention to the safety of edible agricultural products. As an agricultural powerhouse, China has a fairly comprehensive range of pesticides, and new pesticides are constantly emerging. Therefore, effective regulation of pesticide residues in edible agricultural products, as well as exploration of more convenient, quick, efficient, and sensitive detection methods, has become an important research topic for the inspection and testing industry. This paper briefly summarized pesticide residues in edible agricultural products, focused on the relevant technical methods of pesticide residues detection in edible agricultural products, and looked forward to new detection technologies and methods, in order to provide references for improving the detection of pesticide residues in edible agricultural products, and ensure food safety and people's health.
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食用农产品是来源于农业、林业、畜牧业和渔业等领域, 提供人们食用的初级产品, 是人们日常饮食的重要组成部分。近年来农药的广泛运用有力地推动了农业发展, 然而由于违规使用禁用或限用农药以及不科学施药所导致的农药残留污染问题, 使得食用农产品安全性备受关注。我国作为农业大国, 拥有较为齐全的农药种类, 并且新型农药不断涌现, 因此, 对食用农产品中农药残留进行有效监管, 以及探索应用更加简便、快捷、高效、灵敏的检测方法已成为检验检测行业共同面临的重要课题。本文对食用农产品中农药残留进行了简单概括, 重点梳理了食用农产品中农药残留检测的相关技术方法, 并展望了新型检测技术与方法, 以期为提升食用农产品中农药残留检测提供参考, 保障食品安全和人民健康。
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45(15): 316-328., articleTitle=Advances in the application of biosensors for the detection of neonicotinoid pesticide residues, refAbstract=null)], funds=null, companyList=[AuthorCompany(id=1177985452763132071, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986581166805829, xref=null, ext=[AuthorCompanyExt(id=1177985452771520680, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986581166805829, companyId=1177985452763132071, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1. Zibo Zhoucun Inspection and Testing Center, Zibo 255300, China), AuthorCompanyExt(id=1177985452788297898, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986581166805829, companyId=1177985452763132071, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=1.淄博市周村区检验检测中心, 淄博 255300)]), AuthorCompany(id=1177985452851212460, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986581166805829, xref=null, ext=[AuthorCompanyExt(id=1177985452855406765, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986581166805829, companyId=1177985452851212460, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2. Chemistry & Chemical and Environmental Engineering College, Weifang University, Weifang 261061, China), AuthorCompanyExt(id=1177985452867989678, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986581166805829, companyId=1177985452851212460, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=2.潍坊学院化学化工与环境工程学院, 潍坊 261061)]), AuthorCompany(id=1177985452939292847, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986581166805829, xref=null, ext=[AuthorCompanyExt(id=1177985452943487152, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986581166805829, companyId=1177985452939292847, language=EN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3. Technical Center of Safety of Industrial Products of Tianjin Customs, Tianjin 300457, China), AuthorCompanyExt(id=1177985452956070065, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986581166805829, companyId=1177985452939292847, language=CN, country=null, province=null, city=null, postcode=null, companyName=null, departmentName=null, remark=3.天津海关工业产品安全技术中心, 天津 300457)])], figs=[ArticleFig(id=1177985454788980968, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986581166805829, language=EN, label=Table 1, caption=
Characteristics and application of common detection techniques for pesticide residues in edible agricultural products
, figureFileSmall=null, figureFileBig=null, tableContent=
| 分析检测技术 | 优缺点 | 主要应用 场合 | 实际应用 |
检测 样品 | 所用方法 | 检测项目 | 检测结果 | 参考 文献 |
| 传统检测技术 | 色谱法 | 优点: 定量分析准确, 精确度高, 灵敏度好, 分离效能高; 缺点: 前处理复杂、对人员专 业技术要求高、设备成本高、检测周期长, 检测农药种类有限, 在定性方面易受干扰 | 实验室 检测 | 植物油 | 气相色谱法 | 有机磷类农药 (11种) | 检出限0.002~0.003 mg/kg 定量限0.007~0.010 mg/kg 相对标准偏差1.6%~8.7% 回收率75.6%~116.2% | [14] |
| 叶类蔬菜 | 高效液相色 谱法 | 氨基甲酸酯类 农药(甲萘威) | 相对标准偏差0.447% 回收率82.1%~92.1% | [16] |
| 气相色谱-质谱技术 | 优点: 灵敏度高, 检出限低、检测结果准确可靠, 精确度高、重复性好, 可同时进行多残留农药检测; 缺点: 样品要求严格, 分析样品范围受限, 设备造价昂贵, 无法应用于现场检测 | 蔬菜 | 气相色谱-串联 质谱法 | 157种农药 | 检出限0.032~6.198 μg/kg 定量限0.107~19.496 μg/kg 相对标准偏差0.4%~10.5% 平均回收率74.5%~117.3% | [20] |
| 橄榄油 | 气相色谱-四级 杆-飞行时间 质谱法 | 180种农药 | 检出限0.002~0.020 mg/kg 回收率70%~120%的农药占 全部被测农药的76.3% | [21] |
| 西洋参 | 气相色谱-三重 四级杆串联 质谱法 | 227种农药 | 定量限0.01~0.05 μg/kg 相对标准偏差0.16%~11.62% 回收率61.4%~125.3% | [22] |
| 液相色谱-质谱技术 | 优点: 检测范围广, 灵敏度高, 检出限低, 分离能力好, 定性定量分析准确, 分析时间短, 可同时进行多残留农药检测; 缺点: 设备造价昂贵, 现场检测 受限 | 热带水果 | 液相色谱- 串联质谱法 | 110种农药及其 代谢物 | 定量限0.005~0.050 mg/kg 相对标准偏差0.30%~15.00% 回收率61%~120% | [23] |
水果蔬菜 食用菌 | 超高效液相色谱- 串联质谱法 | 377种农药 | 定量限0.5~5.0 µg/kg 低质量分数(0.5~5.0 µg/kg)加 标水平下 回收率为73.9%~112.7% 中质量分数(10 µg/kg)加标 水平下 回收率为70.7%~114.5% 高质量分数(50 µg/kg)加标 水平下 回收率为86.4%~107.2% | [24] |
| 青菜 | 超高效液相色谱- 四级杆飞行时间 质谱法 | 214种农药 | 检出限0.004~5.443 µg/kg 定量限0.206~18.125 µg/kg 相对标准偏差0.4%~14.3% 回收率41.9%~128.5% | [25] |
| 水产品 | 高效液相色谱- 串联 四级杆/静电场 轨道阱高分辨质 谱法 | 62种农药 | 定量限2~10 µg/kg 1倍定量限时相对标准偏差2.1%~16.5% 回收率61.4%~112.4%; 2倍定量限时相对标准偏差2.6%~13.4% 回收率63.7%~119.6%; 10倍定量限时相对标准偏差2.7%~15.2% 回收率66.2%~113.5% | [26] |
| 快速检测技术 | 荧光光谱 技术 | 优点: 特异性强, 灵敏度高, 无损检测; 缺点: 易出现光谱重叠及样品干扰, 应用范围不够广泛、高端设备成本高, 易受环境影响 | 现场快速检测或实验室检测 | - | 三维荧光光 谱法 | 混合有机农药(多杀菌素-高效氟氯氰菊酯、多杀菌素-宁南霉素) | 平均回收率接近100% | [30] |
拉曼光谱 技术 | 优点: 样品前处理简单, 操作方便, 响应迅速, 无损检测, 不受水分子干扰, 检测范围广; 缺点: 灵敏度和特异性不高, 重现性较差, 需建立光谱数据库 | 苹果 | 表面增强拉曼 光谱技术 | 啶虫脒 | 最低检出限0.035 mg/kg 627 cm-1特征峰时相对标准 偏差6.14% 835 cm-1特征峰时相对标准 偏差6.83% 1107 cm-1特征峰时相对标准 偏差6.99% | [35] |
酶抑制 技术 | 优点: 操作简单、检测时间短、对操作人员技术要求低; 缺点: 检测种类受限、灵敏度、重复性、回收率不理想、酶试剂稳定性较差 | 粮食 | 生物荧光酶 抑制法 | 有机磷类和氨基 甲酸酯类 | 最低检出限为0.1 μg/kg | [38] |
免疫分析 技术 | 优点: 操作简便、灵敏度高、特异性强, 检测快速, 检测效率高 缺点: 检测精度有待提升, 无法同时开展多残留检测, 需针对不同农药开发应用不同的试剂盒 | 蔬菜、谷物、肉类 | 酶联免疫吸附法 | 吡蚜酮 | 检出限1.56~2.72 μg /kg 平均回收率81.25%~103.19% | [42] |
| 黄瓜、香蕉 | 胶体金免疫 层析法 | 三唑酮及其代 谢物 | 检出限分别为0.05 mg/kg、 0.85 mg/kg | [44] |
| 生物传感器技术 | 优点: 灵敏度高, 专一性强, 检出限低, 分析速度快, 缺点: 易受环境因素影响 | 科研机构和部分要求 较高的快速检测场所 | 蔬菜 | 电化学生物 传感器 | 有机磷农药 (杀螟硫磷) | 检出限0.04 μg/L 回收率96.16%~109.60% | [48] |
), ArticleFig(id=1177985454948364524, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986581166805829, language=CN, label=表1, caption=
食用农产品中常用农药残留检测技术特点及应用
, figureFileSmall=null, figureFileBig=null, tableContent=
| 分析检测技术 | 优缺点 | 主要应用 场合 | 实际应用 |
检测 样品 | 所用方法 | 检测项目 | 检测结果 | 参考 文献 |
| 传统检测技术 | 色谱法 | 优点: 定量分析准确, 精确度高, 灵敏度好, 分离效能高; 缺点: 前处理复杂、对人员专 业技术要求高、设备成本高、检测周期长, 检测农药种类有限, 在定性方面易受干扰 | 实验室 检测 | 植物油 | 气相色谱法 | 有机磷类农药 (11种) | 检出限0.002~0.003 mg/kg 定量限0.007~0.010 mg/kg 相对标准偏差1.6%~8.7% 回收率75.6%~116.2% | [14] |
| 叶类蔬菜 | 高效液相色 谱法 | 氨基甲酸酯类 农药(甲萘威) | 相对标准偏差0.447% 回收率82.1%~92.1% | [16] |
| 气相色谱-质谱技术 | 优点: 灵敏度高, 检出限低、检测结果准确可靠, 精确度高、重复性好, 可同时进行多残留农药检测; 缺点: 样品要求严格, 分析样品范围受限, 设备造价昂贵, 无法应用于现场检测 | 蔬菜 | 气相色谱-串联 质谱法 | 157种农药 | 检出限0.032~6.198 μg/kg 定量限0.107~19.496 μg/kg 相对标准偏差0.4%~10.5% 平均回收率74.5%~117.3% | [20] |
| 橄榄油 | 气相色谱-四级 杆-飞行时间 质谱法 | 180种农药 | 检出限0.002~0.020 mg/kg 回收率70%~120%的农药占 全部被测农药的76.3% | [21] |
| 西洋参 | 气相色谱-三重 四级杆串联 质谱法 | 227种农药 | 定量限0.01~0.05 μg/kg 相对标准偏差0.16%~11.62% 回收率61.4%~125.3% | [22] |
| 液相色谱-质谱技术 | 优点: 检测范围广, 灵敏度高, 检出限低, 分离能力好, 定性定量分析准确, 分析时间短, 可同时进行多残留农药检测; 缺点: 设备造价昂贵, 现场检测 受限 | 热带水果 | 液相色谱- 串联质谱法 | 110种农药及其 代谢物 | 定量限0.005~0.050 mg/kg 相对标准偏差0.30%~15.00% 回收率61%~120% | [23] |
水果蔬菜 食用菌 | 超高效液相色谱- 串联质谱法 | 377种农药 | 定量限0.5~5.0 µg/kg 低质量分数(0.5~5.0 µg/kg)加 标水平下 回收率为73.9%~112.7% 中质量分数(10 µg/kg)加标 水平下 回收率为70.7%~114.5% 高质量分数(50 µg/kg)加标 水平下 回收率为86.4%~107.2% | [24] |
| 青菜 | 超高效液相色谱- 四级杆飞行时间 质谱法 | 214种农药 | 检出限0.004~5.443 µg/kg 定量限0.206~18.125 µg/kg 相对标准偏差0.4%~14.3% 回收率41.9%~128.5% | [25] |
| 水产品 | 高效液相色谱- 串联 四级杆/静电场 轨道阱高分辨质 谱法 | 62种农药 | 定量限2~10 µg/kg 1倍定量限时相对标准偏差2.1%~16.5% 回收率61.4%~112.4%; 2倍定量限时相对标准偏差2.6%~13.4% 回收率63.7%~119.6%; 10倍定量限时相对标准偏差2.7%~15.2% 回收率66.2%~113.5% | [26] |
| 快速检测技术 | 荧光光谱 技术 | 优点: 特异性强, 灵敏度高, 无损检测; 缺点: 易出现光谱重叠及样品干扰, 应用范围不够广泛、高端设备成本高, 易受环境影响 | 现场快速检测或实验室检测 | - | 三维荧光光 谱法 | 混合有机农药(多杀菌素-高效氟氯氰菊酯、多杀菌素-宁南霉素) | 平均回收率接近100% | [30] |
拉曼光谱 技术 | 优点: 样品前处理简单, 操作方便, 响应迅速, 无损检测, 不受水分子干扰, 检测范围广; 缺点: 灵敏度和特异性不高, 重现性较差, 需建立光谱数据库 | 苹果 | 表面增强拉曼 光谱技术 | 啶虫脒 | 最低检出限0.035 mg/kg 627 cm-1特征峰时相对标准 偏差6.14% 835 cm-1特征峰时相对标准 偏差6.83% 1107 cm-1特征峰时相对标准 偏差6.99% | [35] |
酶抑制 技术 | 优点: 操作简单、检测时间短、对操作人员技术要求低; 缺点: 检测种类受限、灵敏度、重复性、回收率不理想、酶试剂稳定性较差 | 粮食 | 生物荧光酶 抑制法 | 有机磷类和氨基 甲酸酯类 | 最低检出限为0.1 μg/kg | [38] |
免疫分析 技术 | 优点: 操作简便、灵敏度高、特异性强, 检测快速, 检测效率高 缺点: 检测精度有待提升, 无法同时开展多残留检测, 需针对不同农药开发应用不同的试剂盒 | 蔬菜、谷物、肉类 | 酶联免疫吸附法 | 吡蚜酮 | 检出限1.56~2.72 μg /kg 平均回收率81.25%~103.19% | [42] |
| 黄瓜、香蕉 | 胶体金免疫 层析法 | 三唑酮及其代 谢物 | 检出限分别为0.05 mg/kg、 0.85 mg/kg | [44] |
| 生物传感器技术 | 优点: 灵敏度高, 专一性强, 检出限低, 分析速度快, 缺点: 易受环境因素影响 | 科研机构和部分要求 较高的快速检测场所 | 蔬菜 | 电化学生物 传感器 | 有机磷农药 (杀螟硫磷) | 检出限0.04 μg/L 回收率96.16%~109.60% | [48] |
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