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气相色谱-三重四极杆质谱联用在动态多反应监测模式下测定枸杞干中118种农药残留量

[Determination of 118 pesticide residues in dried wolfberry by gas chromatography-triple quadrupole mass spectrometry in dynamic multiple reaction monitoring mode].

作者信息

Yang Zhimin, Zhang Wen, Wu Fuxiang, Wang Xingzhi, Xu Xiaohui

机构信息

Lanzhou Institutes for Food and Drug Control, Lanzhou 730050, China.

出版信息

Se Pu. 2021 Jun;39(6):659-669. doi: 10.3724/SP.J.1123.2020.07028.

DOI:10.3724/SP.J.1123.2020.07028
PMID:34227327
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9404218/
Abstract

Wolfberry fruit is very popular among consumers because it is rich in nutrients. However, it is vulnerable to diseases caused by insect pest feeding and microbial pathogen infection. Pesticide application is the main approach for controlling wolfberry disease; however, various concerns have been raised regarding chemical residues in foodstuffs and consequent environmental contamination. Matrix interference is a significant challenge in trace analysis. Chromatography, coupled with MS techniques with high sensitivity and selectivity, proved to be a powerful tool for the detection of multi-pesticide residues in complex matrices. The traditional MRM mode has been gradually replaced by the dynamic MRM (dMRM) mode, which could dynamically allocate the retention time window of each target pesticide, significantly adjust the loading cycle time of multiple compounds, and improve the analysis efficiency. The QuEChERS pretreatment method, based on dispersive solid-phase extraction, has been widely used in the detection of pesticide residues in food because it is simple and rapid. In this study, a robust and high-throughput method was established for the simultaneous determination of 118 pesticide residues in wolfberry using the modified QuEChERS method, combined with gas chromatography-triple quadrupole mass spectrometry in dMRM mode. The optimal pretreatment method was determined by comparing the recovery rates obtained with different volumes of added water (5, 10, 15, and 20 mL), different extraction solvents (acetone, -hexane, acetonitrile, and acetonitrile containing 0.1% formic acid), different extraction temperatures (normal temperature, -18 ℃ for 10 min and 20 min), water absorbent (anhydrous magnesium sulfate), and purification with primary secondary amine (PSA) and octadecylsilane (C). The results showed that 5 g samples were rehydrated with 10 mL ultrapure water, extracted with 10 mL acetonitrile, frozen at -18 ℃ for 10 min, partitioned with buffer system salt package containing 4.0 g anhydrous magnesium sulfate, 1.0 g sodium chloride, 1.0 g sodium citrate, and 0.5 g disodium citrate, purified up with 800 mg MgSO, 150 mg PSA, and 150 mg C. Pesticides were separated on a capillary column HP-5MS UI (30 m×0.25 mm×0.25 μm), and quantified by a matrix-matched external standard method. The results showed that the 118 pesticides exhibited good linearity in the range from 20 to 640 μg/L, with correlation coefficients ≥0.9923. The limits of detection and quantification were 0.006-28.344 μg/kg and 0.021-94.480 μg/kg, respectively. The average recoveries at four spiked levels of 0.01, 0.04, 0.10, and 0.20 mg/kg were in the range of 64.97%-126.21%, with relative standard deviations (RSDs) of 0.69%-18.86% (=6). The results of the matrix effect showed that 82% of the pesticides exhibited matrix enhancement effects, while others showed matrix inhibition effects. In addition, 9% of the pesticides showed a strong matrix effect, while others showed moderate or weak matrix effects. The matrix effects could be reduced by the matrix-matched standard curve method. The proposed method was employed for the analysis of 10 real samples purchased from local markets. The results demonstrated that pesticides were detected in all the samples, 22 pesticides were detected in total, and 3-12 pesticides were found in a single sample. Chlorpyrifos, fipronil, cypermethrin, pyridaben, and difenoconazole were detected at high detection rates. The captan content in a batch of samples was 1.4066 mg/kg. Thus, the optimized method is simple, fast, accurate, and reliable, and it is suitable for the routine detection and rapid screening of the multi-pesticide residues in wolfberry.

摘要

枸杞果实因其富含营养而深受消费者喜爱。然而,它易受害虫取食和微生物病原体感染所引发疾病的影响。施用农药是防治枸杞病害的主要方法;然而,人们对食品中的化学残留以及随之而来的环境污染提出了各种担忧。基质干扰是痕量分析中的一项重大挑战。色谱法与具有高灵敏度和选择性的质谱技术相结合,被证明是检测复杂基质中多种农药残留的有力工具。传统的多反应监测(MRM)模式已逐渐被动态MRM(dMRM)模式所取代,后者可以动态分配每种目标农药的保留时间窗口,显著调整多种化合物的进样循环时间,并提高分析效率。基于分散固相萃取的QuEChERS预处理方法,因其简单快速,已广泛应用于食品中农药残留的检测。在本研究中,建立了一种稳健且高通量的方法,采用改进的QuEChERS方法,结合dMRM模式的气相色谱 - 三重四极杆质谱法,同时测定枸杞中118种农药残留。通过比较不同加水量(5、10、15和20 mL)、不同萃取溶剂(丙酮、正己烷、乙腈和含0.1%甲酸的乙腈)、不同萃取温度(常温、-18℃ 10分钟和20分钟)、吸水剂(无水硫酸镁)以及用伯仲胺(PSA)和十八烷基硅烷(C18)净化后获得的回收率,确定了最佳预处理方法。结果表明,5 g样品用10 mL超纯水复水,用10 mL乙腈萃取,在-18℃冷冻10分钟,用含有4.0 g无水硫酸镁、1.0 g氯化钠、1.0 g柠檬酸钠和0.5 g柠檬酸二钠的缓冲体系盐包进行分配,用800 mg MgSO4、150 mg PSA和150 mg C18净化。农药在HP - 5MS UI毛细管柱(30 m×0.25 mm×0.25μm)上分离,并用基质匹配外标法进行定量。结果表明,118种农药在20至640μg/L范围内呈现良好的线性关系,相关系数≥0.9923。检测限和定量限分别为0.006 - 28.344μg/kg和0.021 - 94.480μg/kg。在0.01、0.04、0.10和0.20 mg/kg四个加标水平下的平均回收率在64.97% - 126.21%之间,相对标准偏差(RSD)为0.69% - 18.86%(n = 6)。基质效应结果表明,82%的农药呈现基质增强效应,而其他农药呈现基质抑制效应。此外,9%的农药表现出强烈的基质效应,而其他农药表现出中等或较弱的基质效应。通过基质匹配标准曲线法可以降低基质效应。所提出的方法用于分析从当地市场购买的10个实际样品。结果表明,所有样品中均检测到农药,共检测到22种农药,单个样品中发现3 - 12种农药。毒死蜱、氟虫腈、氯氰菊酯、哒螨灵和苯醚甲环唑的检出率较高。一批样品中福美双含量为1.4066 mg/kg。因此,该优化方法简单、快速、准确、可靠,适用于枸杞中多种农药残留的常规检测和快速筛查。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5519/9404218/c8a6830db19c/cjc-39-06-659-img_5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5519/9404218/12260c7e95a1/cjc-39-06-659-img_1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5519/9404218/aaa4977c132e/cjc-39-06-659-img_2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5519/9404218/f8db31ca0b14/cjc-39-06-659-img_3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5519/9404218/e14aacf781dc/cjc-39-06-659-img_4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5519/9404218/c8a6830db19c/cjc-39-06-659-img_5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5519/9404218/12260c7e95a1/cjc-39-06-659-img_1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5519/9404218/aaa4977c132e/cjc-39-06-659-img_2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5519/9404218/f8db31ca0b14/cjc-39-06-659-img_3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5519/9404218/e14aacf781dc/cjc-39-06-659-img_4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5519/9404218/c8a6830db19c/cjc-39-06-659-img_5.jpg

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