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.

, correspAuthors=Bei-Bei DING, 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=Bei-Bei DING, Yan-Fang HUO, Hong-Rui PAN), CN=ArticleExt(id=1153986582345405256, articleId=1153986581166805829, tenantId=1146029695717560320, journalId=1149652044408987649, language=CN, title=食用农产品中农药残留检测技术研究进展, 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=

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。

, correspAuthors=丁贝贝, authorNote=null, correspAuthorsNote=
* 丁贝贝(1985—), 女, 硕士, 工程师, 主要研究方向为食品安全质量管理及检测。E-mail:
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Food Science, 2022, 43(18): 324-331., articleTitle=Fabrication of a white kidney bean esterase-based electrochemical biosensor using 1T-phase tungsten disulfide/Gold nanoparticles (1T-WS2@AuNPs) and its application in the detection of fenitrothion, refAbstract=null), Reference(id=1177985465027277299, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986581166805829, doi=null, pmid=null, pmcid=null, year=2023, volume=29, issue=13, pageStart=110, pageEnd=113, url=null, language=null, rfNumber=[49], rfOrder=95, authorNames=李佳洁, 沈俊毅, 贾敏, journalName=现代食品, refType=null, unstructuredReference=李佳洁, 沈俊毅, 贾敏. 光学生物传感器在有机磷农药检测中的应用[J]. 现代食品, 2023, 29(13): 110-113., articleTitle=光学生物传感器在有机磷农药检测中的应用, refAbstract=null), Reference(id=1177985465127940598, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986581166805829, doi=null, pmid=null, pmcid=null, year=2023, volume=29, issue=13, pageStart=110, pageEnd=113, url=null, language=null, rfNumber=[49], rfOrder=96, authorNames=LI JJ, SHEN JY, JIA M, journalName=Modern Food, refType=null, unstructuredReference=LI JJ, SHEN JY, JIA M. Application of optical biosensors in organophosphorus pesticide detection[J]. Modern Food, 2023, 29(13): 110-113., articleTitle=Application of optical biosensors in organophosphorus pesticide detection, refAbstract=null), Reference(id=1177985465228603894, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986581166805829, doi=null, pmid=null, pmcid=null, year=2024, volume=15, issue=11, pageStart=98, pageEnd=105, url=null, language=null, rfNumber=[50], rfOrder=97, authorNames=向俊, 唐万里, 陈同强, journalName=食品安全质量检测学报, refType=null, unstructuredReference=向俊, 唐万里, 陈同强, 等. 检测食品中有机磷农药的酶生物传感器制备与研究[J]. 食品安全质量检测学报, 2024, 15(11): 98-105., articleTitle=检测食品中有机磷农药的酶生物传感器制备与研究, refAbstract=null), Reference(id=1177985465291518457, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986581166805829, doi=null, pmid=null, pmcid=null, year=2024, volume=15, issue=11, pageStart=98, pageEnd=105, url=null, language=null, rfNumber=[50], rfOrder=98, authorNames=XIANG J, TANG WL, CHEN TQ, journalName=Journal of Food Safety & Quality, refType=null, unstructuredReference=XIANG J, TANG WL, CHEN TQ, et al. Preparation and study of enzyme biosensors for the detection of organophosphorus pesticides in food[J]. Journal of Food Safety & Quality, 2024, 15(11): 98-105., articleTitle=Preparation and study of enzyme biosensors for the detection of organophosphorus pesticides in food, refAbstract=null), Reference(id=1177985465358627323, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986581166805829, doi=null, pmid=null, pmcid=null, year=2021, volume=42, issue=3, pageStart=330, pageEnd=338, url=null, language=null, rfNumber=[51], rfOrder=99, authorNames=付瑞杰, 周静, 田风玉, journalName=食品科学, refType=null, unstructuredReference=付瑞杰, 周静, 田风玉, 等. 基于纳米材料的光学适配体传感器在农药残留检测中的应用研究进展[J]. 食品科学, 2021, 42(3): 330-338., articleTitle=基于纳米材料的光学适配体传感器在农药残留检测中的应用研究进展, refAbstract=null), Reference(id=1177985465425736189, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986581166805829, doi=null, pmid=null, pmcid=null, year=2021, volume=42, issue=3, pageStart=330, pageEnd=338, url=null, language=null, rfNumber=[51], rfOrder=100, authorNames=FU RJ, ZHOU J, TIAN FY, journalName=Food Science, refType=null, unstructuredReference=FU RJ, ZHOU J, TIAN FY, et al. Recent progress in nanomaterial-based optical aptasensors for the detection of pesticide residues[J]. Food Science, 2021, 42(3): 330-338., articleTitle=Recent progress in nanomaterial-based optical aptasensors for the detection of pesticide residues, refAbstract=null), Reference(id=1177985465497039360, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986581166805829, doi=null, pmid=null, pmcid=null, year=2024, volume=45, issue=15, pageStart=316, pageEnd=328, url=null, language=null, rfNumber=[52], rfOrder=101, authorNames=李瑶, 涂济源, 罗可馨, journalName=食品科学, refType=null, unstructuredReference=李瑶, 涂济源, 罗可馨, 等. 生物传感器在新烟碱类农药残留检测中的应用研究进展[J]. 食品科学, 2024, 45(15): 316-328., articleTitle=生物传感器在新烟碱类农药残留检测中的应用研究进展, refAbstract=null), Reference(id=1177985465606091266, tenantId=1146029695717560320, journalId=1149652044408987649, articleId=1153986581166805829, doi=null, pmid=null, pmcid=null, year=2024, volume=45, issue=15, pageStart=316, pageEnd=328, url=null, language=null, rfNumber=[52], rfOrder=102, authorNames=LI Y, TU JY, LUO KX, journalName=Food Science, refType=null, unstructuredReference=LI Y, TU JY, LUO KX, et al. Advances in the application of biosensors for the detection of neonicotinoid pesticide residues[J]. Food Science, 2024, 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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食用农产品中农药残留检测技术研究进展
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丁贝贝 1, * , 霍燕芳 2 , 潘红蕊 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): 53-61
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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): 53-61
食用农产品中农药残留检测技术研究进展
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丁贝贝1, * , 霍燕芳2, 潘红蕊3
作者信息
  • 1.淄博市周村区检验检测中心, 淄博 255300
  • 2.潍坊学院化学化工与环境工程学院, 潍坊 261061
  • 3.天津海关工业产品安全技术中心, 天津 300457

通讯作者:

* 丁贝贝(1985—), 女, 硕士, 工程师, 主要研究方向为食品安全质量管理及检测。E-mail:
Research progress on detection technology of pesticide residues in edible agricultural products
Bei-Bei DING1, * , Yan-Fang HUO2, Hong-Rui PAN3
Affiliations
  • 1. Zibo Zhoucun Inspection and Testing Center, Zibo 255300, China
  • 2. Chemistry & Chemical and Environmental Engineering College, Weifang University, Weifang 261061, China
  • 3. Technical Center of Safety of Industrial Products of Tianjin Customs, Tianjin 300457, China
出版时间: 2025-02-25 doi: 10.19812/j.cnki.jfsq11-5956/ts.20241218001
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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。

农药残留  /  食品安全  /  检测技术应用  /  快速检测

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.

pesticide residue  /  food safety  /  application of detection technology  /  rapid detection
丁贝贝, 霍燕芳, 潘红蕊. 食用农产品中农药残留检测技术研究进展. 食品安全质量检测学报, 2025 , 16 (4) : 53 -61 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20241218001
Bei-Bei DING, Yan-Fang HUO, Hong-Rui PAN. Research progress on detection technology of pesticide residues in edible agricultural products[J]. Journal of Food Safety & Quality, 2025 , 16 (4) : 53 -61 . DOI: 10.19812/j.cnki.jfsq11-5956/ts.20241218001
伴随现代农业生产技术的演进, 众多农作物生产者借助杀虫剂、除草剂、杀菌剂、植物生长调节剂等农药来防控病虫害、提升农作物的产量和质量, 但也逐渐暴露出一些负面效应, 其中过度依赖农药、违规使用禁用、限用农药以及不科学用药等情况, 致使食用农产品中农药残留超标, 这不仅对自然环境和农业可持续发展构成威胁, 同时由于不同国家之间农药最大残留限量标准的差异性也会导致我国农产品出口面临诸多挑战[1], 最重要的还会影响食用者的身体健康, 严重时造成不可逆的损伤[2-4]。2024年以来, 我国农业农村部组织开展了两次国家农产品质量安全例行监测工作, 监测结果显示, 水果、蔬菜、茶叶等主要农产品总体合格率分别为98%和97.7%, 尽管监测结果呈现稳中向好的趋势, 仍存在部分农药残留超标的现象[5-6]。故而, 开展农产品中农药残留检测迫在眉睫, 通过科学合理的开展农药残留检测, 可以及时跟踪和发现农产品中农药残留水平, 有效监控农业活动对土壤、水体和空气质量产生的影响, 为现代化农业生产提供数据支持, 指导农户更加精准的掌握农药使用后的残留情况, 提高我国农产品出口竞争力, 从源头上保障农产品质量安全, 让群众能够享用放心水果和疏菜[7-9]
当前, 我国食用农产品中农药残留检测技术不断完善, 并向简便、快捷、灵敏、高效、低成本等方向发展。目前传统的农药残留检测技术包括气相色谱法、液相色谱法、色谱-质谱技术等, 这类方法具有准确度高、灵敏性高、多残留痕量检测等特点; 但是也具有样品前处理过程复杂耗时, 仪器设备价格昂贵且维护成本高, 操作人员需具备较强专业技能, 现场携带检测不方便等不足。
近年来持续的技术革新推动以快速检测为目标的检测技术不断涌现, 如酶联免疫吸附法、电化学传感技术以及各类光谱技术等, 这些技术不仅克服了操作繁杂、耗时耗力等不足, 更能满足鲜活且易变质食用农产品大量快速筛查的目的[10-11], 但是新技术在实际应用中又存在诸多问题, 仍需进一步研究。本文主要针对近年来采用传统检测方法和快速检测技术开展食用农产品农药残留检测进行全面综述与分析, 以期为下一步开发和应用新技术、新方法提供理论参考, 为实施农产品质量安全监督与抽查提供重要支撑。
气相色谱法主要借助气相色谱仪结合各类检测器, 其中最常用的包括电子捕获检测器(electron capture detector, ECD)[12]、火焰离子化检测器(flame ionization detector, FID)[13]、火焰光度检测器(flame photometric detector, FPD)[14]等, 适合分析各类易挥发、高耐热性和沸点低的小分子量化合物, 如有机磷、有机氯和氨基甲酸酯类农药。检测器作为气相色谱检测核心组成部分, 需要根据残留农药所含元素种类、极性、沸点等性质选择不同的检测器, 如ECD对含卤素、硫、磷、氮、氧等电负性强的元素有响应, FID适合微量有机农药残留分析, 而FPD则对含硫、磷元素的农药残留具有较高的选择性和灵敏度, 李青云[12]采用气相色谱- ECD测定小麦粉中3种拟除虫菊酯农药残留, 其线性关系良好, 相关系数均大于0.999, 检出限、定量限、加标回收率和精密度等均能够满足相关标准要求, 但是ECD对操作条件和环境因素较为敏感, 可能导致结果重现性较差, 基线波动影响准确性; 张凌雪等[13]采用气相色谱-氢火焰离子化检测器检测蔬菜中有机氯和有机磷类农药残留, 该方法响应快、检出限低、稳定性好、回收率高, 为同时开展多组分农药残留检测提供了新选择, 且使用氮气作为载气, 安全性高; 李芳芳等[14]采用气相色谱结合FPD对植物油中11种有机磷类农药进行定量分析, 并验证17批次植物油样品, 表明建立的方法能够精准快速地分析实际样品中有机磷农药残留, 但是在具体检测过程中, 需要注意火焰条件和复杂样品的前处理。
气相色谱技术对样品要求严格, 需要在一定温度下先将样品气化后在进行色谱分离, 这可能会导致部分农药因挥发而影响检测准确性, 另外对有些在检测器上不响应的基团, 需要进行衍生化后再检测, 且该技术受基质干扰可能会出现杂质峰或出现假阳性可能, 对复杂混合物分离效能受限, 而液相色谱技术因其良好的检测性能, 成为农药残留检测领域的又一重要技术。
液相色谱法是一种以液体为流动相的色谱分离分析方法。该方法不受样品挥发性和热稳定性的限制, 对分子量大、难汽化、热稳定性差的生化样品及高分子和离子型样品均可检测, 适合大部分农药残留检测。近年来在经典色谱理论的基础上, 采用高压泵、化学键合固定相的高效分离柱以及高灵敏专用检测器, 如荧光检测器(fluorescence detector, FLD)、二极管阵列检测器(diode array detector, DAD)、紫外检测器(ultraviolet detector, UVD)等新实验技术建立的高效液相色谱分析技术和超高效液相色谱分析技术, 在农药残留检测中得到了更广泛的应用。张鑫鑫等[15]利用高效液相色谱技术检测了粮谷中10种氨基甲酸酯类农药残留, 该方法定量准确、成本合理, 可用于一般粮食谷物中氨基甲酸酯类农药的检测, 其采用的是紫外检测器, 可以实现准确的定量分析, 但其定性不足, 功能单一, 只能检测具有紫外吸收的物质, 且对流动相的选择也有一定限制, 此外农产品农药残留的成分较为复杂, 有可能出现不同组分在同一时间点出峰造成假阳性结果, 从而导致误判。现在较多应用的是可以进行全波长检测的DAD, 能够实现光谱与色谱结合的三维图谱分析, 纪晓娜等[16]采用以十八烷基键合硅胶为固定色谱柱, 运用高效液相色谱法结合二极管阵列检测器对蔬菜中甲萘威残留检测, 分离效果好、检测速度快、灵敏度高, 完全满足检测需求, 该方法主要以保留时间定性, 对多残留农药筛查能力有限, 容易被复杂基质干扰。除此之外, 由于UVD不破坏样品, FLD检出限低, DAD分析信息全面等优势, 将各类检测器串联使用并相互验证, 可以降低单一检测器造成误判的可能性, 提高检测的准确度[17-18]。液相色谱法和高效液相色谱法可以满足多数实验室检测需求, 但是溶剂使用量较大, 处理不当或对环境造成一定污染。超高效液相色谱法分离效能更高、分析速度更快, 适用于痕量组分农药残留分析, 但是设备及维护成本更高, 对操作人员技术要求也高。单独使用液相色谱技术进行农药残留分析时, 需要注意基质干扰问题, 在实际检测过程中可以通过优化实验条件和进行基质匹配、基质效应分析的方式, 确保达到预期目标。
目前色谱-质谱技术已成为农药残留检测中的主流手段, 将色谱的高分离效能与质谱的高分辨能力优势互补, 可以实现精确的定性定量分析, 有效克服色谱法检测能力不足和质谱法无法进行多组分含量分析的缺陷[19]
气相色谱-质谱联用与单一气相色谱法相比, 具有更高的选择性和灵敏度, 基质干扰小, 定性能力强, 能同时检测多种农药残留, 其中串联质谱技术应用更为广泛, 杨志敏等[20]建立了一种分散固相萃取净化结合气相色谱-串联质谱技术对蔬菜中157种农药残留同时测定, 通过多级质谱分析, 这157种农药化合物在各自质量浓度范围内线性关系良好, 对多残留农药的高通量筛查友好, 能为执法部门提供有力的技术支撑。此外根据质量分析器不同, 常用四极杆质量分析器、三重四极杆质量分析器以及飞行时间质量分析器等, 四极杆质量分析器分辨能力有限, 而飞行时间质量分析器以高分辨力著称, 两者单独使用因自身不具备串级功能, 限制了定性能力, 但通过技术结合, 可以实现复杂基质的定性和定量分析, 梁艳等[21]利用气相色谱-四极杆-飞行时间质谱法对橄榄油中180种农药残留检测, 并对市售10份橄榄油样品进行检测, 共检出5种农药, 说明采用该方法具有现实应用价值, 且精密度和准确度好, 能满足橄榄油中多种农药残留检测分析。三重四极杆质量分析器定性定量能力均好, 通常以选择反应监测和多反应监测模式扫描, 可以有效降低基质干扰, 王玮等[22]建立的QuEChERS-气相色谱-三重四极杆串联质谱技术并采用多反应监测模式对西洋参中227种农药残留进行检测, 并对50批次西洋参样品进行农药残留筛查, 共检出29种农药, 其检出率为80.0%, 是一种高效的分析技术。
相比于气相色谱-质谱技术, 液相色谱-质谱技术因灵敏度高、检出限低、分离效能高、检测范围更广, 适合绝大多数农药残留检测, 而采用液相色谱-串联质谱技术在实际检测中具有更好的应用效果, 潘永波等[23]运用氨基化-多壁碳纳米管改进的QuEChERS净化技术结合液相色谱-串联质谱法对热带水果中110种农药及其代谢物残留进行检测, 实现了短时间内可靠的样品分析, 其灵敏度、准确度等均达到实验预期; 唐淑军等[24]建立了一种分组同位素内标法结合超高效液相色谱-串联质谱混检快筛法对水果蔬菜食用菌中377种农药残留进行检测, 该方法灵敏度高, 准确性和稳定性好, 混检效率高, 应用价值大。此外随着技术的发展, 将多种质量分析器与液相色谱结合, 既能得到高质量的谱图, 又可以实现数百种残留农药检测分析, 如朱峰等[25]运用超高效液相色谱-四极杆飞行时间质谱法对青菜中214种农药残留进行检测, 在高分辨质谱信息依赖扫描模式下, 建立214种目标物质数据库, 为样品筛查确证提供比对信息, 该方法简单、快速、准确度高, 为评估青菜中农药残留提供了技术支撑; 张梅超等[26]运用QuEChERS结合高效液相色谱-串联四极杆/静电场轨道阱高分辨质谱法同时测定水产品中62种农药残留, 为实验室快速准确检测鱼肉产品中农药残留量提供了可靠方法。液相色谱-质谱联用技术在农药残留检测方面具有显著优势的同时, 也不可避免的存在一些挑战和局限性, 如样品前处理、仪器成本、基质干扰等因素, 要解决这些问题, 则需要进一步的技术优化和资金支持。
荧光光谱技术主要依赖待检农药分子经过光源激发后, 吸收能量产生荧光信号, 通过分析荧光信号的特征和强度从而获取农药残留量。该项技术可直接用于检测某些具有荧光特性的农药[27], 如有机磷类农药, 陈盛等[28]运用荧光光谱技术对韭菜汁中吡虫啉农药残留量开展检测, 经软件数据处理得出吡虫啉农药含量与荧光强度呈线性关系, 从而验证使用此项技术检测韭菜汁中农药残留是更加直接和有效的方法。在农药残留检测方面, 荧光光谱技术通常结合其他技术方法, 发挥其特有优势, 郝婕等[29]采用荧光高光谱成像技术对脐橙表面不同浓度毒死蜱和多菌灵进行判别, 通过主成分分析与线性判别结合算法和偏最小二乘算法建立了判别模型, 为各浓度农药残留快速无损检测提供了借鉴; 王晓燕等[30]应用三维荧光光谱结合平行因子算法, 对混合农药中的多杀菌素-高效氟氯氰菊酯以及多杀菌素-宁南霉素进行荧光检测及定性定量建模分析, 测试结果表明该方法精确度高, 可以推广应用于混合样品的检测分析; 陈珏等[31]建立了荧光光谱技术结合机器学习算法对白菜中吡虫啉残留量进行检测, 为蔬菜中农药残留快速检测提供了新的思路。荧光光谱分析技术在农药残留检测方面虽然处于起步阶段, 但具有灵敏度高, 分析速度快等优势, 未来在农药残留检测领域将具有广泛应用潜力。
拉曼光谱技术是一种用于表征分子结构的散射光谱方法, 传统拉曼光谱技术不适合小分子痕量农药的检测, 存在散射效应较弱, 灵敏度低等情况, 并且易受噪声和荧光背景影响[32]。近年来, 许多研究者在传统拉曼光谱检测技术基础上, 将待测物质分子吸附于增强基底(贵金属、过渡金属、碱金属及非金属无机材料、半导体材料、纳米材料等)表面, 可使拉曼散射信号强度增强, 这种基于表面增强效应的拉曼光谱技术因其更高的检测灵敏度、更便捷省时以及操作简单, 水干扰小、痕量检测等优点, 逐渐在农药残留检测领域占据一席之地[33-34]。彭彦昆等[35]以改进的银溶胶为增强基底, 采用增强拉曼光谱技术结合化学计量学方法对苹果中啶虫脒农药残留检测, 最低检出限为0.035 mg/kg, 远低于国家规定标准; 杜文瑜等[36]建立的以银溶胶作为柔性衬底、增强芯片作为固体基底的表面增强拉曼光谱双重基底方法, 对香蕉中苯醚甲环唑和毒死蜱残留进行检测, 得出单个样品的检测时间在20 s内, 两种农药的检出限分别为0.16 mg/kg、0.032 mg/kg, 均低于国家标准中的限量值, 该方法采用双重基底可以有效增强特征峰强度, 易于分辨。此外, 随着科技的发展, 基于表面增强拉曼光谱检测技术的便携式拉曼光谱仪的研究使用, 也被广泛应用于农药残留检测中, 为基层食品安全监管部门外出执法和现场抽检提供技术支持[37]
酶抑制技术是目前应用最为广泛的一种农药残留快检技术, 适用于有机磷类和氨基甲酸酯类农药残留量的快速筛查检定, 具有检测速度快、效率高、成本低等优势, 马宏等[38]采用经过改良的昆虫酯酶与荧光底物结合的酶抑制技术, 对粮食中有机磷类和氨基甲酸酯类农药残留进行快速定性检测, 该方法同时处理和检测7个样品总耗时35 min, 检测速度快, 为基层粮食监管机构进行质量监管提供了新思路。近年来随着智能手机强大的拍照功能, 一些研究人员通过手机应用程序将采集的图象转化为红绿蓝色彩模式下的信号强度, 实现待测组分的定量检测, 郑晓娇等[39]建立了一种针式滤器固相微萃取富集毒死蜱, 将酶抑制技术与数字图像比色法相结合对矿泉水、茶水、果汁中毒死蜱的含量进行分析研究, 该方法的加标萃取率在86.6%~92.5%之间, 相对标准偏差为3.2%~6.9%, 研究结果表明该方法适合复杂样品中毒死蜱的残留检测。目前酶抑制技术广泛应用于种植基地、农贸市场等大宗交易场所, 方便对现场大批量样品进行快速筛查[40]。然而, 该方法也存在一些不足: 可检出的农药类型有限, 如对拟除虫菊酯类、有机氯类农药检测效果不佳; 对实验条件要求严格, 酶活性降低或农药提取不完全等, 则可能出现假阳性或假阴性[41]
免疫分析技术是当前重要的一项农药残留快检方法, 非常适合大量样本的快速筛查, 酶联免疫吸附法和胶体金免疫层析法最为常用。酶联免疫吸附法利用抗原-抗体特异反应, 能够精准识别农药残留, 灵敏度高, 可以实现待测组分定性定量分析。YIN等[42]建立了一种单克隆抗体间接竞争酶联免疫吸附法检测蔬菜、谷物和肉类中吡蚜酮, 通过液相色谱-串联质谱法对比试验, 发现该方法比液相色谱-串联质谱技术具有更低的检出限, 同时操作简便且耗时更短, 更加经济实惠, 有效填补了农产品中吡蚜酮残留检测的空白。但该方法仍然存在一些不足, 例如抗体制备难度较大, 对试剂选择性较高, 无法同时分析多残留组分, 特异性识别时对结构类似的组分易出现交叉反应[43]
胶体金免疫层析法作为一种新型的农药残留快检方法, 通过农药残留与胶体金标记的特异性抗体结合, 抑制抗体与试纸条中检测线上抗原结合, 最终观察检测线与质控线颜色深浅变化, 实现农产品中农药残留检测, 且试纸条携带方便操作简便, 对操作者技能要求较低, 反应时间短, 特异性强等优点, 非常适合现场快速检测大批量样品[44-45]。潘喜芳等[46]采用3家公司生产的胶体金免疫层析快速检测卡对豇豆中9种农药残留进行定性判断, 并用三重四极杆液相色谱-质谱联用仪进行定量确证, 运用快速检测卡检测农药的检出限与用液相色谱-质谱技术定量检测结果一致或相似, 从而验证运用此方法可以在基层农产品质量监管中广泛应用。
生物传感器技术作为新兴的农药残留检测技术, 正在农药残留检测领域得到广泛研究与应用。该技术主要依赖生物传感器识别元件与待测组分发生特异性识别, 通过生物化学反应产生一系列信号变化, 再由换能器将其转化成可定量处理的信号, 经信号放大装置进一步处理, 从而获得相关信息[47]。根据识别元件和换能器不同, 一些研究者设计并采用多种农药残留生物传感器, 如电化学生物传感器[48]、光学生物传感器[49]、酶生物传感器[50]、适配体生物传感器[51]等, 实现了简单、快速、高选择性、低成本检测[52]。近年来生物传感技术与纳米技术结合使用, 不仅提高了检测灵敏度, 同时也适用于多残留农药检测。田运霞等[48]建立了一种基于新型1T相二硫化钨/纳米金纳米复合材料的白芸豆酯酶电化学生物传感器, 用于有机磷农药中杀螟硫磷检测, 在最优条件下, 检出限为0.04 μg/L, 将其用于实际农产品中杀螟硫磷检测, 其回收率为96.16%~109.60%, 表明用此种方法开展检测具有潜在应用价值。无论是传统检测技术还是快速检测技术, 都有其优势和不足, 适用于不同的检测场合, 对后续开展农药残留检测均具有借鉴意义, 为了更直观地反映各种检测技术特点及其在实际检测中的应用, 本文对其进行了汇总, 如表1所示。
面对市场上复杂多样的农产品, 保障农产品质量安全仍然是一项任重而道远的工作。目前虽然已有众多科研机构、高校以及专业实验室持续探索取得了一些成熟可靠的方法, 但伴随着日渐增多的农药种类以及愈加严格的农药残留限量标准, 未来农药残留的检测会更加注重多种检测技术的融合应用, 开发更多便携式的现场检测设备提高检测工作效率, 以及结合大数据和人工智能技术提高数据分析能力。所以对检验检测机构来说做好食用农产品中农药残留检测仍有很大发展空间, 在选用何种检测方法时, 需要综合考虑实际情况, 包括是否符合实际场景需求、新型农药类别、精确程度以及费用预算等等, 同时, 还需紧跟科技前沿, 不断突破和革新现有检测技术, 为实现精准、安全、高效、便捷的农药残留检测提供新的解决方案。
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2025年第16卷第4期
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doi: 10.19812/j.cnki.jfsq11-5956/ts.20241218001
  • 接收时间:2024-12-18
  • 首发时间:2025-07-21
  • 出版时间:2025-02-25
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  • 收稿日期:2024-12-18
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    1.淄博市周村区检验检测中心, 淄博 255300
    2.潍坊学院化学化工与环境工程学院, 潍坊 261061
    3.天津海关工业产品安全技术中心, 天津 300457

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* 丁贝贝(1985—), 女, 硕士, 工程师, 主要研究方向为食品安全质量管理及检测。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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