Application of Automated Mass Spectrometry Deconvolution and Identification Software for Pesticide Analysis in Surface Waters

A new approach to surface water analysis has been investigated in order to enhance the detection of different organic contaminants in Nathan Creek, British Columbia. Water samples from Nathan Creek were prepared by liquid/liquid extraction using dichloromethane (DCM) as an extraction solvent and analyzed by gas chromatography mass spectrometry method in scan mode (GC-MS scan). To increase sensitivity for pesticides detection, acquired scan data were further analyzed by Automated Mass Spectrometry Deconvolution and Identification Software (AMDIS) incorporated into the Agilent Deconvolution Reporting Software (DRS), which also includes mass spectral libraries for 567 pesticides. Extracts were reanalyzed by gas chromatography mass spectrometry single ion monitoring (GC-MS-SIM) to confirm and quantitate detected pesticides. Pesticides: atrazine, dimethoate, diazinone, metalaxyl, myclobutanil, napropamide, oxadiazon, propazine and simazine were detected at three sampling sites on the mainstream of the Nathan Creek. Results of the study are further discussed in terms of detectivity and identification level for each pesticide found. The proposed approach of monitoring pesticides in surface waters enables their detection and identification at trace levels.     

AMDIS在土壤有机污染物鉴别中表征性统计量的探究

土壤样品中有机污染物的GCMS数据经AMDIS处理,其结果报告中就组分对应的色谱峰和质谱棒图来讲给出了大量的定性定量统计量。基于官方标准分析方法中的特定要求和AMDIS对GCMS数据处理结果,首先讨论了有机化合物鉴别中选择和使用保留特性统计量时所应考量的要素,并阐述了保留时间或相对保留时间窗口确立的过程,继而依据是否有标准品来阐明了基于AMDIS进行土壤中有机污染物鉴别所应关注的统计量及应遵从的步骤。从而,为准确高效地使用AMDIS甄别土壤中有机污染物组分奠定了坚实的基础。