基于能力验证活动对玉米真菌毒素快检技术的评价
Evaluation of Rapid Detection Technology of Mycotoxin in Corn Based on Proficiency Testing Activity
钱承敬 1张巍巍 1史晓梅 1翟晨 1邹雷 2马同超2
作者信息
- 1. 中粮营养健康研究院;营养健康与食品安全北京市重点实验室,北京 102209
- 2. 中粮贸易有限公司,北京 100005
- 折叠
摘要
玉米原粮贸易过程中,真菌毒素快检是主要的卫生指标检测技术之一,但由于真菌毒素在原粮中的污染通常分布极不均匀,造成了检测数据差异大,准确性难于评估等问题.本研究通过广泛筛查玉米原粮天然真菌毒素污染物,研制满足均匀性、稳定性要求的玉米中脱氧雪腐镰刀菌烯醇(DON)、玉米赤霉烯酮(ZEN)质量控制样品.通过组织行业内企业参加原粮检测化验室能力验证活动,评价粮食中真菌毒素快检技术的应用情况.本研究报名参加毒素快检能力验证共153家企业,其中仓储粮库占36％、饲料养殖企业占64％.实验室遍布17个省、市、自治区,均采用食品快速检验技术检测并报送数据.DON及ZEN能力验证稳健CV分别为26％及29％,一定程度上反应了原粮毒素快检的实际使用情况,为提升快检技术及实验室原粮检测能力提供行业数据支持.
Abstract
During corn trading process,mycotoxin analysis by rapid detection is one of the main analytical tech-nologies for hygiene indicators.However,due to the misdistribution of mycotoxin pollution in raw grain,the analysis results are greatly different,and the accuracy is difficult to evaluate.In this study,the quality control samples of de-oxynivalenol(DON)and zearalenone(ZEN)in corn that met uniformity and stability requirements were developed through extensive screening of natural mycotoxin pollutants in corn products.The application states of rapid detection technology of mycotoxins in grain were evaluated by organizing proficiency test in grain processing company test labo-ratory.In this study,153 enterprises were enrolled for participation,of which 36％were warehouse grain depots and 64％were feed breeding enterprises.The laboratories were distributed in 17 provinces in China.And all of them useed rapid food detection technologies to report results.The coefficient of variation in DON and ZEN proficiency tes-ting were 26％and 29％,respectively,which reflected the actual use of rapid detection of raw grain toxins to a cer-tain extent and provided industry data support for improving rapid detection technologies and laboratory raw grain de-tection capabilities.
关键词
玉米/快速检验/真菌毒素/能力验证/脱氧雪腐镰刀菌烯醇/玉米赤霉烯酮Key words
corn/rapid detection technology/mycotoxin/proficiency testing/deoxynivalenol/zearalenone引用本文复制引用
基金项目
国家重点研发计划项目(2019YFC1604801)
出版年
2024
NETL
NSTL
万方数据