Pang Guo-Fang, Fan Chun-Lin, Chang Qiao-Ying, Li Jian-Xun, Kang Jian, Lu Mei-Ling
Chinese Academy of Inspection and Quarantine, No. 11 Ronghua Nanlu, Beijing Economic-Technological Development Area, Beijing 100176, People's Republic of China.
Agilent Technologies Inc., No. 3 Wangjing BeiLu, Chao Yang District, Beijing 100102, People's Republic of China.
J AOAC Int. 2018 Jul 1;101(4):1156-1182. doi: 10.5740/jaoacint.17-0125. Epub 2018 Mar 22.
This paper uses the LC-quadrupole-time-of-flight MS technique to evaluate the behavioral characteristics of MS of 485 pesticides under different conditions and has developed an accurate mass database and spectra library. A high-throughput screening and confirmation method has been developed for the 485 pesticides in fruits and vegetables. Through the optimization of parameters such as accurate mass number, time of retention window, ionization forms, etc., the method has improved the accuracy of pesticide screening, thus avoiding the occurrence of false-positive and false-negative results. The method features a full scan of fragments, with 80% of pesticide qualitative points over 10, which helps increase pesticide qualitative accuracy. The abundant differences of fragment categories help realize the effective separation and qualitative identification of isomer pesticides. Four different fruits and vegetables-apples, grapes, celery, and tomatoes-were chosen to evaluate the efficiency of the method at three fortification levels of 5, 10, and 20 μg/kg, and satisfactory results were obtained. With this method, a national survey of pesticide residues was conducted between 2012 and 2015 for 12 551 samples of 146 different fruits and vegetables collected from 638 sampling points in 284 counties across 31 provincial capitals/cities directly under the central government, which provided scientific data backup for ensuring pesticide residue safety of the fruits and vegetables consumed daily by the public. Meanwhile, the big data statistical analysis of the new technique also further proves it to be of high speed, high throughput, high accuracy, high reliability, and high informatization.
本文采用液相色谱 - 四极杆 - 飞行时间质谱技术,对485种农药在不同条件下的质谱行为特征进行了评价,并建立了准确质量数据库和光谱库。针对水果和蔬菜中的485种农药,开发了一种高通量筛选和确证方法。通过对精确质量数、保留窗口时间、电离形式等参数的优化,该方法提高了农药筛选的准确性,从而避免了假阳性和假阴性结果的出现。该方法具有碎片全扫描功能,80%的农药定性点数超过10个,有助于提高农药定性准确性。碎片类别的丰富差异有助于实现异构体农药的有效分离和定性鉴定。选择苹果、葡萄、芹菜和西红柿4种不同的水果和蔬菜,在5、10和20μg/kg三个加标水平下评价该方法的效率,获得了满意的结果。利用该方法,在2012年至2015年期间,对从31个省/直辖市的284个县的638个采样点采集的146种不同水果和蔬菜的12 551个样品进行了全国性农药残留调查,为确保公众日常消费的水果和蔬菜的农药残留安全提供了科学数据支持。同时,新技术的大数据统计分析也进一步证明了其具有高速度、高通量、高精度、高可靠性和高信息化的特点。