程序升温大体积进样和解卷积气相色谱-质谱法测定蔬菜水果中32种农药残留量

建立了蔬菜、水果中有机磷、有机氯、拟除虫菊酯和氨基甲酸酯等32种农药的气相色谱-质谱(GC-MS)检测方法。样品经乙腈提取,石墨碳黑串联丙氨基固相萃取柱净化,采用程序升温大体积进样,GC-MS全扫描模式采集,结合解卷积技术定性分析,内标法定量。分别对程序升温和大体积进样等条件进行了研究,并考察了方法选择性和耐用性。在最优条件下,32种农药的响应值与浓度呈良好的线性关系(r>0.995),各农药的方法检出限为2.0~5.0 μg/kg,以菠菜、四季豆和黄瓜为代表基质,进行3个水平(0.010~0.50 mg/kg)的加标回收试验(n=6),回收率为65.2%~120.3%,相对标准偏差(RSD)为4.1%~22.3%。该方法快速、灵敏、可靠、耐用,能满足蔬菜、水果中多类多残留痕量分析的要求。

Combination of programmable temperature vaporizer-large volume injection gas chromatography-mass spectrometry and automated mass spectral deconvolution and identification system for the determination of 32 pesticides in fruits and vegetables

An analytical method was developed for the simultaneous determination of 32 pesticides including organophosphorus, organochlorine, pyrethroid and carbamate pesticides in fruits and vegetables using gas chromatography-mass spectrometry (GC-MS). The sample was extracted with acetonitrile, and the organic layer was cleaned up with ENVI-Carb and LC-NH₂ cartridges. A large volume of 20 μL purified solution was injected into the GC system using programmable temperature vaporizer (PTV). The mass spectrometric detection was operated with full scan mode. The automated mass spectral deconvolution and identfication system (AMDIS) and an isotopic internal standard were used for the qualitative and quantitative determination of the 32 pesticides, respectively. The conditions for PTV-large volume injection were studied. Furthermore, the selectivity and durability of the method were also assessed. Under the optimized conditions, the experimental results showed that all the linearities were good within their test ranges, with correlation coefficients more than 0.995, and the method detection limits of the pesticides were 2.0-5.0 μg/kg. The spiked recoveries (n=6) at three levels in the 0.010-0.50 mg/kg using spinach, snap bean and cucumber samples as blank matrices were in the range of 65.2%-120.3% with the relative standard deviations (RSDs) varying from 4.1% to 22.3%, showing good accuracy of the method. The advantages of the proposed method include high throughput, high sensitivity, reliability, robustness, and the ability to meet the demand of multi-residue pesticide analysis in fruits and vegetables.